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1
Khan, Arijit. "Knowledge Graphs Querying." ACM SIGMOD Record 52, no. 2 (August 10, 2023): 18–29. http://dx.doi.org/10.1145/3615952.3615956.
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Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples - that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two entities. Querying KGs is critical in web search, question answering (QA), semantic search, personal assistants, fact checking, and recommendation. While significant progress has been made on KG construction and curation, thanks to deep learning recently we have seen a surge of research on KG querying and QA. The objectives of our survey are two-fold. First, research on KG querying has been conducted by several communities, such as databases, data mining, semantic web, machine learning, information retrieval, and natural language processing (NLP), with different focus and terminologies; and also in diverse topics ranging from graph databases, query languages, join algorithms, graph patterns matching, to more sophisticated KG embedding and natural language questions (NLQs). We aim at uniting different interdisciplinary topics and concepts that have been developed for KG querying. Second, many recent advances on KG and query embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and computer vision domains. We identify important challenges of KG querying that received less attention by graph databases, and by the DB community in general, e.g., incomplete KG, semantic matching, multimodal data, and NLQs. We conclude by discussing interesting opportunities for the data management community, for instance, KG as a unified data model and vector-based query processing.
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Kejriwal, Mayank. "Knowledge Graphs: A Practical Review of the Research Landscape." Information 13, no. 4 (March 23, 2022): 161. http://dx.doi.org/10.3390/info13040161.
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Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years. Building on a storied tradition of graphs in the AI community, a KG may be simply defined as a directed, labeled, multi-relational graph with some form of semantics. In part, this has been fueled by increased publication of structured datasets on the Web, and well-publicized successes of large-scale projects such as the Google Knowledge Graph and the Amazon Product Graph. However, another factor that is less discussed, but which has been equally instrumental in the success of KGs, is the cross-disciplinary nature of academic KG research. Arguably, because of the diversity of this research, a synthesis of how different KG research strands all tie together could serve a useful role in enabling more ‘moonshot’ research and large-scale collaborations. This review of the KG research landscape attempts to provide such a synthesis by first showing what the major strands of research are, and how those strands map to different communities, such as Natural Language Processing, Databases and Semantic Web. A unified framework is suggested in which to view the distinct, but overlapping, foci of KG research within these communities.
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Yang, Xu, Ziyi Huan, Yisong Zhai, and Ting Lin. "Research of Personalized Recommendation Technology Based on Knowledge Graphs." Applied Sciences 11, no. 15 (July 31, 2021): 7104. http://dx.doi.org/10.3390/app11157104.
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Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs (KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.
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Cao, Keyan, and Chuang Zheng. "TBRm: A Time Representation Method for Industrial Knowledge Graph." Applied Sciences 12, no. 22 (November 8, 2022): 11316. http://dx.doi.org/10.3390/app122211316.
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With the development of the artificial intelligence industry, Knowledge Graph (KG), as a concise and intuitive data presentation form, has received extensive attention and research from both academia and industry in recent years. At the same time, developments in the Internet of Things (IoT) have empowered modern industries to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). Using knowledge graphs (KG) to process data from the Industrial Internet of Things (IIoT) is a research field worthy of attention, but most of the researched knowledge graph technologies are mainly concentrated in the field of static knowledge graphs, which are composed of triples. In fact, many graphs also contain some dynamic information, such as time changes at points and time changes at edges; such knowledge graphs are called Temporal Knowledge Graphs (TKGs). We consider the temporal knowledge graph based on the projection and change of space. In order to combine the temporal information, we propose a new representation of the temporal knowledge graph, namely TBRm, which increases the temporal dimension of the translational distance model and utilizes relational predicates in time add representation in time dimension. We evaluate the proposed method on knowledge graph completion tasks using four benchmark datasets. Experiments demonstrate the effectiveness of TBRm representation in the temporal dimension. At the same time, it is also practiced on a network security data set of the Industrial Internet of Things. The practical results prove that the TBRm method can achieve good performance in terms of the degree of harm to IIoT network security.
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Krinkin, Kirill, Alexander Ivanovich Vodyaho, Igor Kulikov, and Nataly Zhukova. "Deductive Synthesis of Networks Hierarchical Knowledge Graphs." International Journal of Embedded and Real-Time Communication Systems 12, no. 3 (July 2021): 32–48. http://dx.doi.org/10.4018/ijertcs.2021070103.
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The article focuses on developing of a deductive synthesis method for building telecommunications networks (TN) hierarchical knowledge graphs (KG). Synthesized KGs can be used to solve search, analytical, and recommendation (forecast) problems. TNs are complex heterogeneous objects. The synthesis of knowledge graphs of such objects requires much computational resources. The proposed method provides a low complexity of the synthesis of KG of TN by taking into account their hierarchical structure. The authors propose to do synthesis by direct downward multilevel inference and reverse multilevel inference. The article analyses existing graph models of TNs and methods for their building. Detailed description of the proposed method of networks hierarchical KGs synthesis is given. In order to evaluate the deductive synthesis method, a prototype of the system is developed. The provided real-world example shows how telecommunications networks hierarchical knowledge graphs are synthesized and used in practice. Finally, conclusions are formulated, and the areas of further research are identified.
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Tong, Peihao, Qifan Zhang, and Junjie Yao. "Leveraging Domain Context for Question Answering Over Knowledge Graph." Data Science and Engineering 4, no. 4 (November 4, 2019): 323–35. http://dx.doi.org/10.1007/s41019-019-00109-w.
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Abstract With the growing availability of different knowledge graphs in a variety of domains, question answering over knowledge graph (KG-QA) becomes a prevalent information retrieval approach. Current KG-QA methods usually resort to semantic parsing, search or neural matching models. However, they cannot well tackle increasingly long input questions and complex information needs. In this work, we propose a new KG-QA approach, leveraging the rich domain context in the knowledge graph. We incorporate the new approach with question and answer domain context descriptions. Specifically, for questions, we enrich them with users’ subsequent input questions within a session and expand the input question representation. For the candidate answers, we equip them with surrounding context structures, i.e., meta-paths within the targeting knowledge graph. On top of these, we design a cross-attention mechanism to improve the question and answer matching performance. An experimental study on real datasets verifies these improvements. The new approach is especially beneficial for specific knowledge graphs with complex questions.
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Hao, Wu, Jiao Menglin, Tian Guohui, Ma Qing, and Liu Guoliang. "R-KG: A Novel Method for Implementing a Robot Intelligent Service." AI 1, no. 1 (March 2, 2020): 117–40. http://dx.doi.org/10.3390/ai1010006.
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Aiming to solve the problem of environmental information being difficult to characterize when an intelligent service is used, knowledge graphs are used to express environmental information when performing intelligent services. Here, we specially design a kind of knowledge graph for environment expression referred to as a robot knowledge graph (R-KG). The main work of a R-KG is to integrate the diverse semantic information in the environment and pay attention to the relationship at the instance level. Also, through the efficient knowledge organization of a R-KG, robots can fully understand the environment. The R-KG firstly integrates knowledge from different sources to form a unified and standardized representation of a knowledge graph. Then, the deep logical relationship hidden in the knowledge graph is explored. To this end, a knowledge reasoning model based on a Markov logic network is proposed to realize the self-developmental ability of the knowledge graph and to further enrich it. Finally, as the strength of environment expression directly affects the efficiency of robots performing services, in order to verify the efficiency of the R-KG, it is used here as the semantic map that can be directly used by a robot for performing intelligent services. The final results prove that the R-KG can effectively express environmental information.
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Chen, Xuelu, Muhao Chen, Weijia Shi, Yizhou Sun, and Carlo Zaniolo. "Embedding Uncertain Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3363–70. http://dx.doi.org/10.1609/aaai.v33i01.33013363.
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Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge. The capturing of uncertain knowledge will benefit many knowledge-driven applications such as question answering and semantic search by providing more natural characterization of the knowledge. In this paper, we propose a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space. Unlike previous models that characterize relation facts with binary classification techniques, UKGE learns embeddings according to the confidence scores of uncertain relation facts. To further enhance the precision of UKGE, we also introduce probabilistic soft logic to infer confidence scores for unseen relation facts during training. We propose and evaluate two variants of UKGE based on different confidence score modeling strategies. Experiments are conducted on three real-world uncertain KGs via three tasks, i.e. confidence prediction, relation fact ranking, and relation fact classification. UKGE shows effectiveness in capturing uncertain knowledge by achieving promising results, and it consistently outperforms baselines on these tasks.
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Monka, Sebastian, Lavdim Halilaj, and Achim Rettinger. "A survey on visual transfer learning using knowledge graphs." Semantic Web 13, no. 3 (April 6, 2022): 477–510. http://dx.doi.org/10.3233/sw-212959.
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The information perceived via visual observations of real-world phenomena is unstructured and complex. Computer vision (CV) is the field of research that attempts to make use of that information. Recent approaches of CV utilize deep learning (DL) methods as they perform quite well if training and testing domains follow the same underlying data distribution. However, it has been shown that minor variations in the images that occur when these methods are used in the real world can lead to unpredictable and catastrophic errors. Transfer learning is the area of machine learning that tries to prevent these errors. Especially, approaches that augment image data using auxiliary knowledge encoded in language embeddings or knowledge graphs (KGs) have achieved promising results in recent years. This survey focuses on visual transfer learning approaches using KGs, as we believe that KGs are well suited to store and represent any kind of auxiliary knowledge. KGs can represent auxiliary knowledge either in an underlying graph-structured schema or in a vector-based knowledge graph embedding. Intending to enable the reader to solve visual transfer learning problems with the help of specific KG-DL configurations we start with a description of relevant modeling structures of a KG of various expressions, such as directed labeled graphs, hypergraphs, and hyper-relational graphs. We explain the notion of feature extractor, while specifically referring to visual and semantic features. We provide a broad overview of knowledge graph embedding methods and describe several joint training objectives suitable to combine them with high dimensional visual embeddings. The main section introduces four different categories on how a KG can be combined with a DL pipeline: 1) Knowledge Graph as a Reviewer; 2) Knowledge Graph as a Trainee; 3) Knowledge Graph as a Trainer; and 4) Knowledge Graph as a Peer. To help researchers find meaningful evaluation benchmarks, we provide an overview of generic KGs and a set of image processing datasets and benchmarks that include various types of auxiliary knowledge. Last, we summarize related surveys and give an outlook about challenges and open issues for future research.
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Li, Tongxin, Weiping Wang, Xiaobo Li, Tao Wang, Xin Zhou, and Meigen Huang. "Embedding Uncertain Temporal Knowledge Graphs." Mathematics 11, no. 3 (February 3, 2023): 775. http://dx.doi.org/10.3390/math11030775.
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Knowledge graph (KG) embedding for predicting missing relation facts in incomplete knowledge graphs (KGs) has been widely explored. In addition to the benchmark triple structural information such as head entities, tail entities, and the relations between them, there is a large amount of uncertain and temporal information, which is difficult to be exploited in KG embeddings, and there are some embedding models specifically for uncertain KGs and temporal KGs. However, these models either only utilize uncertain information or only temporal information, without integrating both kinds of information into the underlying model that utilizes triple structural information. In this paper, we propose an embedding model for uncertain temporal KGs called the confidence score, time, and ranking information embedded jointly model (CTRIEJ), which aims to preserve the uncertainty, temporal and structural information of relation facts in the embedding space. To further enhance the precision of the CTRIEJ model, we also introduce a self-adversarial negative sampling technique to generate negative samples. We use the embedding vectors obtained from our model to complete the missing relation facts and predict their corresponding confidence scores. Experiments are conducted on an uncertain temporal KG extracted from Wikidata via three tasks, i.e., confidence prediction, link prediction, and relation fact classification. The CTRIEJ model shows effectiveness in capturing uncertain and temporal knowledge by achieving promising results, and it consistently outperforms baselines on the three downstream experimental tasks.
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Yang, Zhenyu, Lei Wu, Peian Wen, and Peng Chen. "Visual Question Answering reasoning with external knowledge based on bimodal graph neural network." Electronic Research Archive 31, no. 4 (2023): 1948–65. http://dx.doi.org/10.3934/era.2023100.
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<abstract><p>Visual Question Answering (VQA) with external knowledge requires external knowledge and visual content to answer questions about images. The defect of existing VQA solutions is that they need to identify task-related information in the obtained pictures, questions, and knowledge graphs. It is necessary to properly fuse and embed the information between different modes identified, to reduce the noise and difficulty in cross-modality reasoning of VQA models. However, this process of rationally integrating information between different modes and joint reasoning to find relevant evidence to correctly predict the answer to the question still deserves further study. This paper proposes a bimodal Graph Neural Network model combining pre-trained Language Models and Knowledge Graphs (BIGNN-LM-KG). Researchers built the concepts graph by the images and questions concepts separately. In constructing the concept graph, we used the combined reasoning advantages of LM+KG. Specifically, use KG to jointly infer the images and question entity concepts to build a concept graph. Use LM to calculate the correlation score to screen the nodes and paths of the concept graph. Then, we form a visual graph from the visual and spatial features of the filtered image entities. We use the improved GNN to learn the representation of the two graphs and to predict the most likely answer by fusing the information of two different modality graphs using a modality fusion GNN. On the common dataset of VQA, the model we proposed obtains good experiment results. It also verifies the validity of each component in the model and the interpretability of the model.</p></abstract>
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Bizon, Chris, Steven Cox, James Balhoff, Yaphet Kebede, Patrick Wang, Kenneth Morton, Karamarie Fecho, and Alexander Tropsha. "ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources." Journal of Chemical Information and Modeling 59, no. 12 (November 26, 2019): 4968–73. http://dx.doi.org/10.1021/acs.jcim.9b00683.
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Wang, Meihong, Linling Qiu, and Xiaoli Wang. "A Survey on Knowledge Graph Embeddings for Link Prediction." Symmetry 13, no. 3 (March 16, 2021): 485. http://dx.doi.org/10.3390/sym13030485.
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Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform the link-prediction task based on various representation techniques. Among them, KG-embedding models have significantly advanced the state of the art in the past few years. In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs. We first provide a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding. Then, we investigate several representative models that are classified into five categories. Finally, we conducted experiments on two benchmark datasets to report comprehensive findings and provide some new insights into the strengths and weaknesses of existing models.
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Fang, Yin, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan, and Huajun Chen. "Molecular Contrastive Learning with Chemical Element Knowledge Graph." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 3968–76. http://dx.doi.org/10.1609/aaai.v36i4.20313.
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Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive learning is a promising paradigm as it utilizes self-supervision signals and has no requirements for human annotations. However, prior works fail to incorporate fundamental domain knowledge into graph semantics and thus ignore the correlations between atoms that have common attributes but are not directly connected by bonds. To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning. KCL framework consists of three modules. The first module, knowledge-guided graph augmentation, augments the original molecular graph based on the Chemical Element KG. The second module, knowledge-aware graph representation, extracts molecular representations with a common graph encoder for the original molecular graph and a Knowledge-aware Message Passing Neural Network (KMPNN) to encode complex information in the augmented molecular graph. The final module is a contrastive objective, where we maximize agreement between these two views of molecular graphs. Extensive experiments demonstrated that KCL obtained superior performances against state-of-the-art baselines on eight molecular datasets. Visualization experiments properly interpret what KCL has learned from atoms and attributes in the augmented molecular graphs.
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Debruyne, Christophe, Gary Munnelly, Lynn Kilgallon, Declan O’Sullivan, and Peter Crooks. "Creating a Knowledge Graph for Ireland’s Lost History: Knowledge Engineering and Curation in the Beyond 2022 Project." Journal on Computing and Cultural Heritage 15, no. 2 (June 30, 2022): 1–25. http://dx.doi.org/10.1145/3474829.
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The Beyond 2022 project aims to create a virtual archive by digitally reconstructing and digitizing historical records lost in a catastrophic fire which consumed items in the Public Record Office of Ireland in 1922. The project is developing a knowledge graph (KG) to facilitate information retrieval and discovery over the reconstructed items. The project decided to adopt Semantic Web technologies to support its distributed KG and reasoning. In this article, we present our approach to KG generation and management. We elaborate on how we help historians contribute to the KG (via a suite of spreadsheets) and its ontology. We furthermore demonstrate how we use named graphs to store different versions of factoids and their provenance information and how these are serviced in two different endpoints. Modeling data in this manner allows us to acknowledge that history is, to some extent, subjective and different perspectives can exist in parallel. The construction of the KG is driven by competency questions elicited from subject matter experts within the consortium. We avail of CIDOC-CRM as our KG’s foundation, though we needed to extend this ontology with various qualifiers (types) and relations to support the competency questions. We illustrate how one can explore the KG to gain insights and answer questions. We conclude that CIDOC-CRM provides an adequate, albeit complex, foundation for the KG and that named graphs and Linked Data principles are a suitable mechanism to manage sets of factoids and their provenance.
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Lan, Ning, Shuqun Yang, Ling Yin, and Yongbin Gao. "Research on Knowledge Graphs with Concept Lattice Constraints." Symmetry 13, no. 12 (December 8, 2021): 2363. http://dx.doi.org/10.3390/sym13122363.
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The application of knowledge graphs has been restricted in some domains, especially the industrial and academic domains. One of the reasons is that they require a high reliability of knowledge, which cannot be satisfied by the existing knowledge graph research. By comparison, traditional knowledge engineering has a high correctness, but low efficiency is an inevitable drawback. Therefore, it is meaningful to organically connect traditional knowledge engineering and knowledge graphs. Therefore, we propose a theory from Attribute Implications to Knowledge Graphs, named AIs-KG, which can construct knowledge graphs based on implications. The theory connects formal concept analysis and knowledge graphs. We firstly analyze the mutual transformation based on the ideas of symmetry with a strict proof among the attribute implication, the formal context and the concept lattice, which forms the closed cycle between the three. Particularly, we propose an Augment algorithm (IFC-A) to generate the Implication Formal Context through the attribute implication, which can make knowledge more complete. Furthermore, we regard ontology as a bridge to realize the transformation from the concept lattice to the knowledge graph through some mapping methods. We conduct our experiments on the attribute implication from the rule base of an animal recognition expert system to prove the feasibility of our algorithms.
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Liu, Rui, Rong Fu, Kang Xu, Xuanzhe Shi, and Xiaoning Ren. "A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems." Applied Sciences 13, no. 7 (March 29, 2023): 4357. http://dx.doi.org/10.3390/app13074357.
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Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role in guiding power equipment operation and decision-making. Due to the complex and weak relations from multi-source heterogeneous data, the use of KGs has become popular in research to represent potential information in power knowledge reasoning. In this review, we first summarize the key technologies of knowledge graph representation and learning. Then, based on the complexity and real-time changes of power system operation and maintenance, we present multiple data processing, knowledge representation learning, and the graph construction process. In three typical power operation and fault decision application scenarios, we investigate current algorithms in power KG acquisition, representation embedding, and knowledge completion to illustrate accurate and exhaustive recommendations. Thus, using KGs to provide reference solutions and decision guidance has a significant role in improving the efficiency of power system operations. Finally, we summarize the achievements and difficulties of current research and give an outlook for future, promising roles of KG in power systems.
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Garifo, Giovanni, Giuseppe Futia, Antonio Vetrò, and Juan Carlos De Martin. "The Geranium Platform: A KG-Based System for Academic Publications." Information 12, no. 9 (September 8, 2021): 366. http://dx.doi.org/10.3390/info12090366.
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Knowledge Graphs (KGs) have emerged as a core technology for incorporating human knowledge because of their capability to capture the relational dimension of information and of its semantic properties. The nature of KGs meets one of the vocational pursuits of academic institutions, which is sharing their intellectual output, especially publications. In this paper, we describe and make available the Polito Knowledge Graph (PKG) –which semantically connects information on more than 23,000 publications and 34,000 authors– and Geranium, a semantic platform that leverages the properties of the PKG to offer advanced services for search and exploration. In particular, we describe the Geranium recommendation system, which exploits Graph Neural Networks (GNNs) to suggest collaboration opportunities between researchers of different disciplines. This work integrates the state of the art because we use data from a real application in the scholarly domain, while the current literature still explores the combination of KGs and GNNs in a prototypal context using synthetic data. The results shows that the fusion of these technologies represents a promising approach for recommendation and metadata inference in the scholarly domain.
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Chen, Kai, Guohua Shen, Zhiqiu Huang, and Haijuan Wang. "Improved Entity Linking for Simple Question Answering Over Knowledge Graph." International Journal of Software Engineering and Knowledge Engineering 31, no. 01 (January 2021): 55–80. http://dx.doi.org/10.1142/s0218194021400039.
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Question Answering systems over Knowledge Graphs (KG) answer natural language questions using facts contained in a knowledge graph, and Simple Question Answering over Knowledge Graphs (KG-SimpleQA) means that the question can be answered by a single fact. Entity linking, which is a core component of KG-SimpleQA, detects the entities mentioned in questions, and links them to the actual entity in KG. However, traditional methods ignore some information of entities, especially entity types, which leads to the emergence of entity ambiguity problem. Besides, entity linking suffers from out-of-vocabulary (OOV) problem due to the limitation of pre-trained word embeddings. To address these problems, we encode questions in a novel way and encode the features contained in the entities in a multilevel way. To evaluate the enhancement of the whole KG-SimpleQA brought by our improved entity linking, we utilize a relatively simple approach for relation prediction. Besides, to reduce the impact of losing the feature during the encoding procedure, we utilize a ranking algorithm to re-rank (entity, relation) pairs. According to the experimental results, our method for entity linking achieves an accuracy of 81.8% that beats the state-of-the-art methods, and our improved entity linking brings a boost of 5.6% for the whole KG-SimpleQA.
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Niu, Guanglin, Yongfei Zhang, Bo Li, Peng Cui, Si Liu, Jingyang Li, and Xiaowei Zhang. "Rule-Guided Compositional Representation Learning on Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2950–58. http://dx.doi.org/10.1609/aaai.v34i03.5687.
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Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Moreover, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.
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Govindapillai, Sini, Lay-Ki Soon, and Su-Cheng Haw. "Resource Description Framework reification for trustworthiness in knowledge graphs." F1000Research 10 (September 2, 2021): 881. http://dx.doi.org/10.12688/f1000research.72843.1.
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Knowledge graph (KG) publishes machine-readable representation of knowledge on the Web. Structured data in the knowledge graph is published using Resource Description Framework (RDF) where knowledge is represented as a triple (subject, predicate, object). Due to the presence of erroneous, outdated or conflicting data in the knowledge graph, the quality of facts cannot be guaranteed. Therefore, the provenance of knowledge can assist in building up the trust of these knowledge graphs. In this paper, we have provided an analysis of popular, general knowledge graphs Wikidata and YAGO4 with regard to the representation of provenance and context data. Since RDF does not support metadata for providing provenance and contextualization, an alternate method, RDF reification is employed by most of the knowledge graphs. Trustworthiness of facts in knowledge graph can be enhanced by the addition of metadata like the source of information, location and time of the fact occurrence. Wikidata employs qualifiers to include metadata to facts, while YAGO4 collects metadata from Wikidata qualifiers. RDF reification increases the magnitude of data as several statements are required to represent a single fact. However, facts in Wikidata and YAGO4 can be fetched without using reification. Another limitation for applications that uses provenance data is that not all facts in these knowledge graphs are annotated with provenance data. Structured data in the knowledge graph is noisy. Therefore, the reliability of data in knowledge graphs can be increased by provenance data. To the best of our knowledge, this is the first paper that investigates the method and the extent of the addition of metadata of two prominent KGs, Wikidata and YAGO4.
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Cui, Yuanning, Yuxin Wang, Zequn Sun, Wenqiang Liu, Yiqiao Jiang, Kexin Han, and Wei Hu. "Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4217–24. http://dx.doi.org/10.1609/aaai.v37i4.25539.
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Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.
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Mohoney, Jason, Anil Pacaci, Shihabur Rahman Chowdhury, Ali Mousavi, Ihab F. Ilyas, Umar Farooq Minhas, Jeffrey Pound, and Theodoros Rekatsinas. "High-Throughput Vector Similarity Search in Knowledge Graphs." Proceedings of the ACM on Management of Data 1, no. 2 (June 13, 2023): 1–25. http://dx.doi.org/10.1145/3589777.
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There is an increasing adoption of machine learning for encoding data into vectors to serve online recommendation and search use cases. As a result, recent data management systems propose augmenting query processing with online vector similarity search. In this work, we explore vector similarity search in the context of Knowledge Graphs (KGs). Motivated by the tasks of finding related KG queries and entities for past KG query workloads, we focus on hybrid vector similarity search (hybrid queries for short) where part of the query corresponds to vector similarity search and part of the query corresponds to predicates over relational attributes associated with the underlying data vectors. For example, given past KG queries for a song entity, we want to construct new queries for new song entities whose vector representations are close to the vector representation of the entity in the past KG query. But entities in a KG also have non-vector attributes such as a song associated with an artist, a genre, and a release date. Therefore, suggested entities must also satisfy query predicates over non-vector attributes beyond a vector-based similarity predicate. While these tasks are central to KGs, our contributions are generally applicable to hybrid queries. In contrast to prior works that optimize online queries, we focus on enabling efficient batch processing of past hybrid query workloads. We present our system, HQI, for high-throughput batch processing of hybrid queries. We introduce a workload-aware vector data partitioning scheme to tailor the vector index layout to the given workload and describe a multi-query optimization technique to reduce the overhead of vector similarity computations. We evaluate our methods on industrial workloads and demonstrate that HQI yields a 31× improvement in throughput for finding related KG queries compared to existing hybrid query processing approaches.
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Orogat, Abdelghny, and Ahmed El-Roby. "Maestro: Automatic Generation of Comprehensive Benchmarks for Question Answering Over Knowledge Graphs." Proceedings of the ACM on Management of Data 1, no. 2 (June 13, 2023): 1–24. http://dx.doi.org/10.1145/3589322.
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Recently, there has been an upsurge in the number of knowledge graphs (KG) that can only be accessed by experts. Non-expert users lack an adequate understanding of the queried knowledge graph's vocabulary and structure, as well as the syntax of the structured query language used to express the user's information needs. To increase the user base of these KGs, a set of Question Answering (QA) systems that use natural language to query these knowledge graphs have been introduced. However, finding a benchmark that accurately evaluates the quality of a QA system is a difficult task due to (1) the high degree of variation in the fine-grained properties among the existing benchmarks, (2) the static nature of the existing benchmarks versus the evolving nature of KGs, and (3) the limited number of KGs targeted by existing benchmarks, which hinders the usability of QA systems in real-world deployment over KGs that are different from those that were used in the evaluation of the QA systems. In this paper, we introduce Maestro, a benchmark generation system for question answering over knowledge graphs. Maestro can generate a new benchmark for any KG given the KG and, optionally, a text corpus that covers this KG. The benchmark generated by Maestro is guaranteed to cover all the properties of the natural language questions and queries that were encountered in the literature as long as the targeted KG includes these properties. Maestro also generates high-quality natural language questions with various utterances that are on par with manually-generated ones to better evaluate QA systems.
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Mavromatis, Costas, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, Adesoji Adeshina, Phillip R. Howard, Tetiana Grinberg, Nagib Hakim, and George Karypis. "TempoQR: Temporal Question Reasoning over Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5825–33. http://dx.doi.org/10.1609/aaai.v36i5.20526.
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Knowledge Graph Question Answering (KGQA) involves retrieving facts from a Knowledge Graph (KG) using natural language queries. A KG is a curated set of facts consisting of entities linked by relations. Certain facts include also temporal information forming a Temporal KG (TKG). Although many natural questions involve explicit or implicit time constraints, question answering (QA) over TKGs has been a relatively unexplored area. Existing solutions are mainly designed for simple temporal questions that can be answered directly by a single TKG fact. This paper puts forth a comprehensive embedding-based framework for answering complex questions over TKGs. Our method termed temporal question reasoning (TempoQR) exploits TKG embeddings to ground the question to the specific entities and time scope it refers to. It does so by augmenting the question embeddings with context, entity and time-aware information by employing three specialized modules. The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings. Finally, a transformer-based encoder learns to fuse the generated temporal information with the question representation, which is used for answer predictions. Extensive experiments show that TempoQR improves accuracy by 25--45 percentage points on complex temporal questions over state-of-the-art approaches and it generalizes better to unseen question types.
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Yu, Donghan, Chenguang Zhu, Yiming Yang, and Michael Zeng. "JAKET: Joint Pre-training of Knowledge Graph and Language Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11630–38. http://dx.doi.org/10.1609/aaai.v36i10.21417.
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Knowledge graphs (KGs) contain rich information about world knowledge, entities, and relations. Thus, they can be great supplements to existing pre-trained language models. However, it remains a challenge to efficiently integrate information from KG into language modeling. And the understanding of a knowledge graph requires related context. We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language. The knowledge module and language module provide essential information to mutually assist each other: the knowledge module produces embeddings for entities in text while the language module generates context-aware initial embeddings for entities and relations in the graph. Our design enables the pre-trained model to easily adapt to unseen knowledge graphs in new domains. Experiment results on several knowledge-aware NLP tasks show that our proposed framework achieves superior performance by effectively leveraging knowledge in language understanding.
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Guo, Lingbing, Qingheng Zhang, Wei Hu, Zequn Sun, and Yuzhong Qu. "Learning to Complete Knowledge Graphs with Deep Sequential Models." Data Intelligence 1, no. 3 (June 2019): 289–308. http://dx.doi.org/10.1162/dint_a_00016.
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Knowledge graph (KG) completion aims at filling the missing facts in a KG, where a fact is typically represented as a triple in the form of ( head, relation, tail). Traditional KG completion methods compel two-thirds of a triple provided (e.g., head and relation) to predict the remaining one. In this paper, we propose a new method that extends multi-layer recurrent neural networks (RNNs) to model triples in a KG as sequences. It obtains state-of-the-art performance on the common entity prediction task, i.e., giving head (or tail) and relation to predict the tail (or the head), using two benchmark data sets. Furthermore, the deep sequential characteristic of our method enables it to predict the relations given head (or tail) only, and even predict the whole triples. Our experiments on these two new KG completion tasks demonstrate that our method achieves superior performance compared with several alternative methods.
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Yang, Tong, Yifei Wang, Long Sha, Jan Engelbrecht, and Pengyu Hong. "Knowledgebra: An Algebraic Learning Framework for Knowledge Graph." Machine Learning and Knowledge Extraction 4, no. 2 (May 5, 2022): 432–45. http://dx.doi.org/10.3390/make4020019.
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Knowledge graph (KG) representation learning aims to encode entities and relations into dense continuous vector spaces such that knowledge contained in a dataset could be consistently represented. Dense embeddings trained from KG datasets benefit a variety of downstream tasks such as KG completion and link prediction. However, existing KG embedding methods fell short to provide a systematic solution for the global consistency of knowledge representation. We developed a mathematical language for KG based on an observation of their inherent algebraic structure, which we termed as Knowledgebra. By analyzing five distinct algebraic properties, we proved that the semigroup is the most reasonable algebraic structure for the relation embedding of a general knowledge graph. We implemented an instantiation model, SemE, using simple matrix semigroups, which exhibits state-of-the-art performance on standard datasets. Moreover, we proposed a regularization-based method to integrate chain-like logic rules derived from human knowledge into embedding training, which further demonstrates the power of the developed language. As far as we know, by applying abstract algebra in statistical learning, this work develops the first formal language for general knowledge graphs, and also sheds light on the problem of neural-symbolic integration from an algebraic perspective.
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Morton, Kenneth, Patrick Wang, Chris Bizon, Steven Cox, James Balhoff, Yaphet Kebede, Karamarie Fecho, and Alexander Tropsha. "ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering." Bioinformatics 35, no. 24 (August 13, 2019): 5382–84. http://dx.doi.org/10.1093/bioinformatics/btz604.
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Abstract Summary Knowledge graphs (KGs) are quickly becoming a common-place tool for storing relationships between entities from which higher-level reasoning can be conducted. KGs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest that have been posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the KG and the storage and analysis of query results are straightforward; however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a KG with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank and explore sub-graphs of a complex KG represents a major challenge to any effort designed to exploit the use of KGs for applications in biomedical research and other domains. We present Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways as an abstraction layer and user interface to more easily query KGs and store, rank and explore query results. Availability and implementation An instance of the ROBOKOP UI for exploration of the ROBOKOP Knowledge Graph can be found at http://robokop.renci.org. The ROBOKOP Knowledge Graph can be accessed at http://robokopkg.renci.org. Code and instructions for building and deploying ROBOKOP are available under the MIT open software license from https://github.com/NCATS-Gamma/robokop. Supplementary information Supplementary data are available at Bioinformatics online.
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Hsu, Chao-Chun, Zi-Yuan Chen, Chi-Yang Hsu, Chih-Chia Li, Tzu-Yuan Lin, Ting-Hao Huang, and Lun-Wei Ku. "Knowledge-Enriched Visual Storytelling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7952–60. http://dx.doi.org/10.1609/aaai.v34i05.6303.
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Stories are diverse and highly personalized, resulting in a large possible output space for story generation. Existing end-to-end approaches produce monotonous stories because they are limited to the vocabulary and knowledge in a single training dataset. This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories. KG-Story distills a set of representative words from the input prompts, enriches the word set by using external knowledge graphs, and finally generates stories based on the enriched word set. This distill-enrich-generate framework allows the use of external resources not only for the enrichment phase, but also for the distillation and generation phases. In this paper, we show the superiority of KG-Story for visual storytelling, where the input prompt is a sequence of five photos and the output is a short story. Per the human ranking evaluation, stories generated by KG-Story are on average ranked better than that of the state-of-the-art systems. Our code and output stories are available at https://github.com/zychen423/KE-VIST.
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Bu, *Chenyang, Xingchen Yu, Yan Hong, and Tingting Jiang. "Low-Quality Error Detection for Noisy Knowledge Graphs." Journal of Database Management 32, no. 4 (October 2021): 48–64. http://dx.doi.org/10.4018/jdm.2021100104.
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The automatic construction of knowledge graphs (KGs) from multiple data sources has received increasing attention. The automatic construction process inevitably brings considerable noise, especially in the construction of KGs from unstructured text. The noise in a KG can be divided into two categories: factual noise and low-quality noise. Factual noise refers to plausible triples that meet the requirements of ontology constraints. For example, the plausible triple <New_York, IsCapitalOf, America> satisfies the constraints that the head entity “New_York” is a city and the tail entity “America” belongs to a country. Low-quality noise denotes the obvious errors commonly created in information extraction processes. This study focuses on entity type errors. Most existing approaches concentrate on refining an existing KG, assuming that the type information of most entities or the ontology information in the KG is known in advance. However, such methods may not be suitable at the start of a KG's construction. Therefore, the authors propose an effective framework to eliminate entity type errors. The experimental results demonstrate the effectiveness of the proposed method.
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Zhang, Chuxu, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, and Nitesh V. Chawla. "Few-Shot Knowledge Graph Completion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 3041–48. http://dx.doi.org/10.1609/aaai.v34i03.5698.
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Knowledge graphs (KGs) serve as useful resources for various natural language processing applications. Previous KG completion approaches require a large number of training instances (i.e., head-tail entity pairs) for every relation. The real case is that for most of the relations, very few entity pairs are available. Existing work of one-shot learning limits method generalizability for few-shot scenarios and does not fully use the supervisory information; however, few-shot KG completion has not been well studied yet. In this work, we propose a novel few-shot relation learning model (FSRL) that aims at discovering facts of new relations with few-shot references. FSRL can effectively capture knowledge from heterogeneous graph structure, aggregate representations of few-shot references, and match similar entity pairs of reference set for every relation. Extensive experiments on two public datasets demonstrate that FSRL outperforms the state-of-the-art.
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Dash, Sanjeeb, and Joao Goncalves. "Rule Induction in Knowledge Graphs Using Linear Programming." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4233–41. http://dx.doi.org/10.1609/aaai.v37i4.25541.
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We present a simple linear programming (LP) based method to learn compact and interpretable sets of rules encoding the facts in a knowledge graph (KG) and use these rules to solve the KG completion problem. Our LP model chooses a set of rules of bounded complexity from a list of candidate first-order logic rules and assigns weights to them. The complexity bound is enforced via explicit constraints. We combine simple rule generation heuristics with our rule selection LP to obtain predictions with accuracy comparable to state-of-the-art codes, even while generating much more compact rule sets. Furthermore, when we take as input rules generated by other codes, we often improve interpretability by reducing the number of chosen rules, while maintaining accuracy.
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Destandau, Marie, and Jean-Daniel Fekete. "The missing path: Analysing incompleteness in knowledge graphs." Information Visualization 20, no. 1 (January 2021): 66–82. http://dx.doi.org/10.1177/1473871621991539.
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Knowledge Graphs (KG) allow to merge and connect heterogeneous data despite their differences; they are incomplete by design. Yet, KG data producers need to ensure the best level of completeness, as far as possible. The difficulty is that they have no means to distinguish cases where incomplete entities could and should be fixed. We present a new visualization tool: The Missing Path, to support them in identifying coherent subsets of entities that can be repaired. It relies on a map, grouping entities according to their incomplete profile. The map is coordinated with histograms and stacked charts to support interactive exploration and analysis; the summary of a subset can be compared with the one of the full collection to reveal its distinctive features. We conduct an iterative design process and evaluation with nine Wikidata contributors. Participants gain insights and find various strategies to identify coherent subsets to be fixed.
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Zhang, Weihang, Ovidiu Șerban, Jiahao Sun, and Yike Guo. "IPPT4KRL: Iterative Post-Processing Transfer for Knowledge Representation Learning." Machine Learning and Knowledge Extraction 5, no. 1 (January 6, 2023): 43–58. http://dx.doi.org/10.3390/make5010004.
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Knowledge Graphs (KGs), a structural way to model human knowledge, have been a critical component of many artificial intelligence applications. Many KG-based tasks are built using knowledge representation learning, which embeds KG entities and relations into a low-dimensional semantic space. However, the quality of representation learning is often limited by the heterogeneity and sparsity of real-world KGs. Multi-KG representation learning, which utilizes KGs from different sources collaboratively, presents one promising solution. In this paper, we propose a simple, but effective iterative method that post-processes pre-trained knowledge graph embedding (IPPT4KRL) on individual KGs to maximize the knowledge transfer from another KG when a small portion of alignment information is introduced. Specifically, additional triples are iteratively included in the post-processing based on their adjacencies to the cross-KG alignments to refine the pre-trained embedding space of individual KGs. We also provide the benchmarking results of existing multi-KG representation learning methods on several generated and well-known datasets. The empirical results of the link prediction task on these datasets show that the proposed IPPT4KRL method achieved comparable and even superior results when compared against more complex methods in multi-KG representation learning.
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Xie, Luodi, Huimin Huang, and Qing Du. "A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs." Applied Sciences 12, no. 2 (January 12, 2022): 715. http://dx.doi.org/10.3390/app12020715.
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Knowledge graph (KG) embedding has been widely studied to obtain low-dimensional representations for entities and relations. It serves as the basis for downstream tasks, such as KG completion and relation extraction. Traditional KG embedding techniques usually represent entities/relations as vectors or tensors, mapping them in different semantic spaces and ignoring the uncertainties. The affinities between entities and relations are ambiguous when they are not embedded in the same latent spaces. In this paper, we incorporate a co-embedding model for KG embedding, which learns low-dimensional representations of both entities and relations in the same semantic space. To address the issue of neglecting uncertainty for KG components, we propose a variational auto-encoder that represents KG components as Gaussian distributions. In addition, compared with previous methods, our method has the advantages of high quality and interpretability. Our experimental results on several benchmark datasets demonstrate our model’s superiority over the state-of-the-art baselines.
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Qin, Pengda, Xin Wang, Wenhu Chen, Chunyun Zhang, Weiran Xu, and William Yang Wang. "Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8673–80. http://dx.doi.org/10.1609/aaai.v34i05.6392.
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Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion need to collect adequate training instances for newly-added relations. In this paper, we consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions and hence recognize the facts of unseen relations with no examples being seen. For this purpose, we leverage Generative Adversarial Networks (GANs) to establish the connection between text and knowledge graph domain: The generator learns to generate the reasonable relation embeddings merely with noisy text descriptions. Under this setting, zero-shot learning is naturally converted to a traditional supervised classification task. Empirically, our method is model-agnostic that could be potentially applied to any version of KG embeddings, and consistently yields performance improvements on NELL and Wiki dataset.
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Wu, Jiajing, Zhiqiang Wei, Dongning Jia, Xin Dou, Huo Tang, and Nannan Li. "Constructing marine expert management knowledge graph based on Trellisnet-CRF." PeerJ Computer Science 8 (September 5, 2022): e1083. http://dx.doi.org/10.7717/peerj-cs.1083.
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Creating and maintaining a domain-specific database of research institutions, academic experts and scholarly literature is essential to expanding national marine science and technology. Knowledge graphs (KGs) have now been widely used in both industry and academia to address real-world problems. Despite the abundance of generic KGs, there is a vital need to build domain-specific knowledge graphs in the marine sciences domain. In addition, there is still not an effective method for named entity recognition when constructing a knowledge graph, especially when including data from both scientific and social media sources. This article presents a novel marine science domain-based knowledge graph framework. This framework involves capturing marine domain data into KG representations. The proposed approach utilizes various entity information based on marine domain experts to enrich the semantic content of the knowledge graph. To enhance named entity recognition accuracy, we propose a novel TrellisNet-CRF model. Our experiment results demonstrate that the TrellisNet-CRF model reached a 96.99% accuracy rate for marine domain named entity recognition, which outperforms the current state-of-the-art baseline. The effectiveness of the TrellisNet-CRF module was then further demonstrated and confirmed on entity recognition and visualization tasks.
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Wang, Xiaxia, Tengteng Lin, Weiqing Luo, Gong Cheng, and Yuzhong Qu. "CKGSE: A Prototype Search Engine for Chinese Knowledge Graphs." Data Intelligence 4, no. 1 (2022): 41–65. http://dx.doi.org/10.1162/dint_a_00118.
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Abstract Nowadays, with increasing open knowledge graphs (KGs) being published on the Web, users depend on open data portals and search engines to find KGs. However, existing systems provide search services and present results with only metadata while ignoring the contents of KGs, i.e., triples. It brings difficulty for users' comprehension and relevance judgement. To overcome the limitation of metadata, in this paper we propose a content-based search engine for open KGs named CKGSE. Our system provides keyword search, KG snippet generation, KG profiling and browsing, all based on KGs' detailed, informative contents rather than their brief, limited metadata. To evaluate its usability, we implement a prototype with Chinese KGs crawled from OpenKG.CN and report some preliminary results and findings.
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Goel, Rishab, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. "Diachronic Embedding for Temporal Knowledge Graph Completion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3988–95. http://dx.doi.org/10.1609/aaai.v34i04.5815.
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Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones–a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been developed mostly for static KGs. Developing temporal KG embedding models is an increasingly important problem. In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time. This is in contrast to the existing temporal KG embedding approaches where only static entity features are provided. The proposed embedding function is model-agnostic and can be potentially combined with any static model. We prove that combining it with SimplE, a recent model for static KG embedding, results in a fully expressive model for temporal KG completion. Our experiments indicate the superiority of our proposal compared to existing baselines.
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Kurniawan, Kabul, Andreas Ekelhart, Elmar Kiesling, Dietmar Winkler, Gerald Quirchmayr, and A. Min Tjoa. "VloGraph: A Virtual Knowledge Graph Framework for Distributed Security Log Analysis." Machine Learning and Knowledge Extraction 4, no. 2 (April 11, 2022): 371–96. http://dx.doi.org/10.3390/make4020016.
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The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages, such as SPARQL, can support rich log analyses by leveraging semantic relationships between objects in heterogeneous log streams. Constructing, materializing, and maintaining centralized log knowledge graphs, however, poses significant challenges. To tackle this issue, we propose VloGraph—a distributed and virtualized alternative to centralized log knowledge graph construction. The proposed approach does not involve any a priori parsing, aggregation, and processing of log data, but dynamically constructs a virtual log KG from heterogeneous raw log sources across multiple hosts. To explore the feasibility of this approach, we developed a prototype and demonstrate its applicability to three scenarios. Furthermore, we evaluate the approach in various experimental settings with multiple heterogeneous log sources and machines; the encouraging results from this evaluation suggest that the approach can enable efficient graph-based ad-hoc log analyses in federated settings.
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Govindapillai, Sini, Lay-Ki Soon, and Su-Cheng Haw. "An empirical study on Resource Description Framework reification for trustworthiness in knowledge graphs." F1000Research 10 (November 29, 2021): 881. http://dx.doi.org/10.12688/f1000research.72843.2.
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Knowledge graph (KG) publishes machine-readable representation of knowledge on the Web. Structured data in the knowledge graph is published using Resource Description Framework (RDF) where knowledge is represented as a triple (subject, predicate, object). Due to the presence of erroneous, outdated or conflicting data in the knowledge graph, the quality of facts cannot be guaranteed. Trustworthiness of facts in knowledge graph can be enhanced by the addition of metadata like the source of information, location and time of the fact occurrence. Since RDF does not support metadata for providing provenance and contextualization, an alternate method, RDF reification is employed by most of the knowledge graphs. RDF reification increases the magnitude of data as several statements are required to represent a single fact. Another limitation for applications that uses provenance data like in the medical domain and in cyber security is that not all facts in these knowledge graphs are annotated with provenance data. In this paper, we have provided an overview of prominent reification approaches together with the analysis of popular, general knowledge graphs Wikidata and YAGO4 with regard to the representation of provenance and context data. Wikidata employs qualifiers to include metadata to facts, while YAGO4 collects metadata from Wikidata qualifiers. However, facts in Wikidata and YAGO4 can be fetched without using reification to cater for applications that do not require metadata. To the best of our knowledge, this is the first paper that investigates the method and the extent of metadata covered by two prominent KGs, Wikidata and YAGO4.
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Fernández-Álvarez, Daniel, Johannes Frey, Jose Emilio Labra Gayo, Daniel Gayo-Avello, and Sebastian Hellmann. "Approaches to measure class importance in Knowledge Graphs." PLOS ONE 16, no. 6 (June 10, 2021): e0252862. http://dx.doi.org/10.1371/journal.pone.0252862.
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The amount, size, complexity, and importance of Knowledge Graphs (KGs) have increased during the last decade. Many different communities have chosen to publish their datasets using Linked Data principles, which favors the integration of this information with many other sources published using the same principles and technologies. Such a scenario requires to develop techniques of Linked Data Summarization. The concept of a class is one of the core elements used to define the ontologies which sustain most of the existing KGs. Moreover, classes are an excellent tool to refer to an abstract idea which groups many individuals (or instances) in the context of a given KG, which is handy to use when producing summaries of its content. Rankings of class importance are a powerful summarization tool that can be used both to obtain a superficial view of the content of a given KG and to prioritize many different actions over the data (data quality checking, visualization, relevance for search engines…). In this paper, we analyze existing techniques to measure class importance and propose a novel approach called ClassRank. We compare the class usage in SPARQL logs of different KGs with the importance ranking produced by the approaches evaluated. Then, we discuss the strengths and weaknesses of the evaluated techniques. Our experimentation suggests that ClassRank outperforms state-of-the-art approaches measuring class importance.
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Gu, Tianlong, Haohong Liang, Chenzhong Bin, and Liang Chang. "Combining user-end and item-end knowledge graph learning for personalized recommendation." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 9213–25. http://dx.doi.org/10.3233/jifs-201635.
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How to accurately model user preferences based on historical user behaviour and auxiliary information is of great importance in personalized recommendation tasks. Among all types of auxiliary information, knowledge graphs (KGs) are an emerging type of auxiliary information with nodes and edges that contain rich structural information and semantic information. Many studies prove that incorporating KG into personalized recommendation tasks can effectively improve the performance, rationality and interpretability of recommendations. However, existing methods either explore the independent meta-paths for user-item pairs in KGs or use a graph convolution network on all KGs to obtain embeddings for users and items separately. Although both types of methods have respective effects, the former cannot fully capture the structural information of user-item pairs in KGs, while the latter ignores the mutual effect between the target user and item during the embedding learning process. To alleviate the shortcomings of these methods, we design a graph convolution-based recommendation model called Combining User-end and Item-end Knowledge Graph Learning (CUIKG), which aims to capture the relevance between users’ personalized preferences and items by jointly mining the associated attribute information in their respective KG. Specifically, we describe user embedding from a user KG and then introduce user embedding, which contains the user profile into the item KG, to describe item embedding with the method of Graph Convolution Network. Finally, we predict user preference probability for a given item via multilayer perception. CUIKG describes the connection between user-end KG and item-end KG, and mines the structural and semantic information present in KG. Experimental results with two real-world datasets demonstrate the superiority of the proposed method over existing methods.
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Di Paolo, Giuseppina, Diego Rincon-Yanez, and Sabrina Senatore. "A Quick Prototype for Assessing OpenIE Knowledge Graph-Based Question-Answering Systems." Information 14, no. 3 (March 16, 2023): 186. http://dx.doi.org/10.3390/info14030186.
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Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent years, question-answering approaches have received increasing attention from academia and industry. Question-answering systems use knowledge graphs to organize, navigate, search and connect knowledge entities. Managing such systems requires a thorough understanding of the underlying graph-oriented structures and, at the same time, an appropriate query language, such as SPARQL, to access relevant data. Natural language interfaces are needed to enable non-technical users to query ever more complex data. The paper proposes a question-answering approach to support end users in querying graph-oriented knowledge bases. The system pipeline is composed of two main modules: one is dedicated to translating a natural language query submitted by the user into a triple of the form <subject, predicate, object>, while the second module implements knowledge graph embedding (KGE) models, exploiting the previous module triple and retrieving the answer to the question. Our framework delivers a fast OpenIE-based knowledge extraction system and a graph-based answer prediction model for question-answering tasks. The system was designed by leveraging existing tools to accomplish a simple prototype for fast experimentation, especially across different knowledge domains, with the added benefit of reducing development time and costs. The experimental results confirm the effectiveness of the proposed system, which provides promising performance, as assessed at the module level. In particular, in some cases, the system outperforms the literature. Finally, a use case example shows the KG generated by user questions in a graphical interface provided by an ad-hoc designed web application.
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Wang, Chenming, and Bo Huang. "The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations." Applied Sciences 13, no. 8 (April 7, 2023): 4667. http://dx.doi.org/10.3390/app13084667.
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Collaborative filtering (CF) usually suffers from data sparsity and cold starts. Knowledge graphs (KGs) are widely used to improve recommendation performance. To verify that knowledge graphs can further alleviate the above problems, this paper proposes an end-to-end framework that uses attentive knowledge graph perceptual propagation for recommendations (AKGP). This framework uses a knowledge graph as a source of auxiliary information to extract user–item interaction information and build a sub-knowledge base. The fusion of structural and contextual information is used to construct fine-grained knowledge graphs via knowledge graph embedding methods and to generate initial embedding representations. Through multi-layer propagation, the structured information and historical preference information are embedded into a unified vector space, and the potential user–item vector representation is expanded. This article used a knowledge perception attention module to achieve feature representation, and finally, the model was optimized using the stratified sampling joint learning method. Compared with the baseline model using MovieLens-1M, Last-FM, Book-Crossing and other data sets, the experimental results demonstrate that the model outperforms state-of-the-art KG-based recommendation methods, and the shortcomings of the existing model are improved. The model was applied to product design data and historical maintenance records provided by an automotive parts manufacturing company. The predictions of the recommended system are matched to the product requirements and possible failure records. This helped reduce costs and increase productivity, helping the company to quickly determine the cause of failures and reduce unplanned downtime.
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Kelley, Aidan, and Daniel Garijo. "A framework for creating knowledge graphs of scientific software metadata." Quantitative Science Studies 2, no. 4 (2021): 1423–46. http://dx.doi.org/10.1162/qss_a_00167.
Full textAbstract:
Abstract An increasing number of researchers rely on computational methods to generate or manipulate the results described in their scientific publications. Software created to this end—scientific software—is key to understanding, reproducing, and reusing existing work in many disciplines, ranging from Geosciences to Astronomy or Artificial Intelligence. However, scientific software is usually challenging to find, set up, and compare to similar software due to its disconnected documentation (dispersed in manuals, readme files, websites, and code comments) and the lack of structured metadata to describe it. As a result, researchers have to manually inspect existing tools to understand their differences and incorporate them into their work. This approach scales poorly with the number of publications and tools made available every year. In this paper we address these issues by introducing a framework for automatically extracting scientific software metadata from its documentation (in particular, their readme files); a methodology for structuring the extracted metadata in a Knowledge Graph (KG) of scientific software; and an exploitation framework for browsing and comparing the contents of the generated KG. We demonstrate our approach by creating a KG with metadata from over 10,000 scientific software entries from public code repositories.
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Chen, Xuelu, Ziniu Hu, and Yizhou Sun. "Fuzzy Logic Based Logical Query Answering on Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 3939–48. http://dx.doi.org/10.1609/aaai.v36i4.20310.
Full textAbstract:
Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task. Recent advances embed logical queries and KG entities in the same space and conduct query answering via dense similarity search. However, most logical operators designed in previous studies do not satisfy the axiomatic system of classical logic, limiting their performance. Moreover, these logical operators are parameterized and thus require many complex FOL queries as training data, which are often arduous to collect or even inaccessible in most real-world KGs. We thus present FuzzQE, a fuzzy logic based logical query embedding framework for answering FOL queries over KGs. FuzzQE follows fuzzy logic to define logical operators in a principled and learning-free manner, where only entity and relation embeddings require learning. FuzzQE can further benefit from labeled complex logical queries for training. Extensive experiments on two benchmark datasets demonstrate that FuzzQE provides significantly better performance in answering FOL queries compared to state-of-the-art methods. In addition, FuzzQE trained with only KG link prediction can achieve comparable performance to those trained with extra complex query data.
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Jiang, Yukun, Xin Gao, Wenxin Su, and Jinrong Li. "Systematic Knowledge Management of Construction Safety Standards Based on Knowledge Graphs: A Case Study in China." International Journal of Environmental Research and Public Health 18, no. 20 (October 12, 2021): 10692. http://dx.doi.org/10.3390/ijerph182010692.
Full textAbstract:
Construction safety standards (CSS) have knowledge characteristics, but few studies have introduced knowledge graphs (KG) as a tool into CSS management. In order to improve CSS knowledge management, this paper first analyzed the knowledge structure of 218 standards and obtained three knowledge levels of CSS. Second, a concept layer was designed which consisted of five levels of concepts and eight types of relationships. Third, an entity layer containing 147 entities was constructed via entity identification, attribute extraction and entity extraction. Finally, 177 nodes and 11 types of attributes were collected and the construction of a knowledge graph of construction safety standard (KGCSS) was completed using knowledge storage. Furthermore, we implemented knowledge inference and obtained CSS planning, i.e., the list of standard work plans used to guide the development and revision of CSS. In addition, we conducted CSS knowledge retrieval; a process which supports interrogative input. The construction of KGCSS thus facilitates the analysis, querying, and sharing of safety standards knowledge.
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Geng, Yuxia, Jiaoyan Chen, Zhiquan Ye, Zonggang Yuan, Wei Zhang, and Huajun Chen. "Explainable zero-shot learning via attentive graph convolutional network and knowledge graphs." Semantic Web 12, no. 5 (August 27, 2021): 741–65. http://dx.doi.org/10.3233/sw-210435.
Full textAbstract:
Zero-shot learning (ZSL) which aims to deal with new classes that have never appeared in the training data (i.e., unseen classes) has attracted massive research interests recently. Transferring of deep features learned from training classes (i.e., seen classes) are often used, but most current methods are black-box models without any explanations, especially textual explanations that are more acceptable to not only machine learning specialists but also common people without artificial intelligence expertise. In this paper, we focus on explainable ZSL, and present a knowledge graph (KG) based framework that can explain the transferability of features in ZSL in a human understandable manner. The framework has two modules: an attentive ZSL learner and an explanation generator. The former utilizes an Attentive Graph Convolutional Network (AGCN) to match class knowledge from WordNet with deep features learned from CNNs (i.e., encode inter-class relationship to predict classifiers), in which the features of unseen classes are transferred from seen classes to predict the samples of unseen classes, with impressive (important) seen classes detected, while the latter generates human understandable explanations for the transferability of features with class knowledge that are enriched by external KGs, including a domain-specific Attribute Graph and DBpedia. We evaluate our method on two benchmarks of animal recognition. Augmented by class knowledge from KGs, our framework generates promising explanations for the transferability of features, and at the same time improves the recognition accuracy.