Relevant bibliographies by topics / Knowledge Graph Evaluation

Academic literature on the topic 'Knowledge Graph Evaluation'

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Published: 6 September 2023

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Journal articles on the topic "Knowledge Graph Evaluation"

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Gao, Junyang, Xian Li, Yifan Ethan Xu, Bunyamin Sisman, Xin Luna Dong, and Jun Yang. "Efficient knowledge graph accuracy evaluation." Proceedings of the VLDB Endowment 12, no. 11 (July 2019): 1679–91. http://dx.doi.org/10.14778/3342263.3342642.

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Wang, Wenguang, Yonglin Xu, Chunhui Du, Yunwen Chen, Yijie Wang, and Hui Wen. "Data Set and Evaluation of Automated Construction of Financial Knowledge Graph." Data Intelligence 3, no. 3 (2021): 418–43. http://dx.doi.org/10.1162/dint_a_00108.

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With the technological development of entity extraction, relationship extraction, knowledge reasoning, and entity linking, the research on knowledge graph has been carried out in full swing in recent years. To better promote the development of knowledge graph, especially in the Chinese language and in the financial industry, we built a high-quality data set, named financial research report knowledge graph (FR2KG), and organized the automated construction of financial knowledge graph evaluation at the 2020 China Knowledge Graph and Semantic Computing Conference (CCKS2020). FR2KG consists of 17,799 entities, 26,798 relationship triples, and 1,328 attribute triples covering 10 entity types, 19 relationship types, and 6 attributes. Participants are required to develop a constructor that will automatically construct a financial knowledge graph based on the FR2KG. In addition, we summarized the technologies for automatically constructing knowledge graphs, and introduced the methods used by the winners and the results of this evaluation.
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Alshahrani, Mona, Maha A. Thafar, and Magbubah Essack. "Application and evaluation of knowledge graph embeddings in biomedical data." PeerJ Computer Science 7 (February 18, 2021): e341. http://dx.doi.org/10.7717/peerj-cs.341.

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Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases. That is, linked data and bio-ontologies are employed in databases to maintain data integrity, data organization, and to empower search capabilities. However, linked data and bio-ontologies are more recently being used to represent information as multi-relational heterogeneous graphs, “knowledge graphs”. The reason being, entities and relations in the knowledge graph can be represented as embedding vectors in semantic space, and these embedding vectors have been used to predict relationships between entities. Such knowledge graph embedding methods provide a practical approach to data analytics and increase chances of building machine learning models with high prediction accuracy that can enhance decision support systems. Here, we present a comparative assessment and a standard benchmark for knowledge graph-based representation learning methods focused on the link prediction task for biological relations. We systematically investigated and compared state-of-the-art embedding methods based on the design settings used for training and evaluation. We further tested various strategies aimed at controlling the amount of information related to each relation in the knowledge graph and its effects on the final performance. We also assessed the quality of the knowledge graph features through clustering and visualization and employed several evaluation metrics to examine their uses and differences. Based on this systematic comparison and assessments, we identify and discuss the limitations of knowledge graph-based representation learning methods and suggest some guidelines for the development of more improved methods.
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Mao, Yanmei. "Summary and Evaluation of the Application of Knowledge Graphs in Education 2007–2020." Discrete Dynamics in Nature and Society 2021 (September 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/6304109.

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Since 2007, knowledge graphs, an important research tool, have been applied to education and many other disciplines. This paper firstly overviews the application of knowledge graphs in education and then samples the knowledge graph applications in CSSCI- (Chinese Social Sciences Citation Index-) indexed journals in the past two years. These samples were classified and analyzed in terms of research institute, data source, visualization software, and analysis perspective. Next, the situation of knowledge graph applications in education was summarized and evaluated in detail. Furthermore, the authors discussed and assessed the normalization of knowledge graph applications in education. The results show that in the past 15 years, knowledge graphs have been widely used in education. The academia has reached a consensus on the paradigm of the research tool: examining the hotspots, topics, and trends in the related fields from the angles of keyword cooccurrence network (KCN), time zone map, clustering network, and literature/author cocitation, with the aid of CiteSpace and other visualization software and text analysis. However, there is not yet a thorough understanding of the limitations of the visualization software. The relevant research should be improved in terms of scientific level, normalization level, and quality.
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Malaviya, Chaitanya, Chandra Bhagavatula, Antoine Bosselut, and Yejin Choi. "Commonsense Knowledge Base Completion with Structural and Semantic Context." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2925–33. http://dx.doi.org/10.1609/aaai.v34i03.5684.

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Automatic KB completion for commonsense knowledge graphs (e.g., ATOMIC and ConceptNet) poses unique challenges compared to the much studied conventional knowledge bases (e.g., Freebase). Commonsense knowledge graphs use free-form text to represent nodes, resulting in orders of magnitude more nodes compared to conventional KBs ( ∼18x more nodes in ATOMIC compared to Freebase (FB15K-237)). Importantly, this implies significantly sparser graph structures — a major challenge for existing KB completion methods that assume densely connected graphs over a relatively smaller set of nodes.In this paper, we present novel KB completion models that can address these challenges by exploiting the structural and semantic context of nodes. Specifically, we investigate two key ideas: (1) learning from local graph structure, using graph convolutional networks and automatic graph densification and (2) transfer learning from pre-trained language models to knowledge graphs for enhanced contextual representation of knowledge. We describe our method to incorporate information from both these sources in a joint model and provide the first empirical results for KB completion on ATOMIC and evaluation with ranking metrics on ConceptNet. Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1.5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency. Further analysis on model predictions shines light on the types of commonsense knowledge that language models capture well.
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Sekkal, Houda, Naïla Amrous, and Samir Bennani. "Knowledge graph-based method for solutions detection and evaluation in an online problem-solving community." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6350. http://dx.doi.org/10.11591/ijece.v12i6.pp6350-6362.

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<span lang="EN-US">Online communities are a real medium for human experiences sharing. They contain rich knowledge of lived situations and experiences that can be used to support decision-making process and problem-solving. This work presents an approach for extracting, representing, and evaluating components of problem-solving knowledge shared in online communities. Few studies have tackled the issue of knowledge extraction and its usefulness evaluation in online communities. In this study, we propose a new approach to detect and evaluate best solutions to problems discussed by members of online communities. Our approach is based on knowledge graph technology and graphs theory enabling the representation of knowledge shared by the community and facilitating its reuse. Our process of problem-solving knowledge extraction in online communities (PSKEOC) consists of three phases: problems and solutions detection and classification, knowledge graph constitution and finally best solutions evaluation. The experimental results are compared to the World Health Organization (WHO) model chapter about Infant and young child feeding and show that our approach succeed to extract and reveal important problem-solving knowledge contained in online community’s conversations. Our proposed approach leads to the construction of an experiential knowledge graph as a representation of the constructed knowledge base in the community studied in this paper.</span>
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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, Pu, Tianci Li, Xin Wang, Suzhi Zhang, Yuncheng Jiang, and Yong Tang. "Scholar Recommendation Based on High-Order Propagation of Knowledge Graphs." International Journal on Semantic Web and Information Systems 18, no. 1 (January 2022): 1–19. http://dx.doi.org/10.4018/ijswis.297146.

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In a big data environment, traditional recommendation methods have limitations such as data sparseness and cold start, etc. In view of the rich semantics, excellent quality, and good structure of knowledge graphs, many researchers have introduced knowledge graphs into the research about recommendation systems, and studied interpretable recommendations based on knowledge graphs. Along this line, this paper proposes a scholar recommendation method based on the high-order propagation of knowledge graph (HoPKG), which analyzes the high-order semantic information in the knowledge graph, and generates richer entity representations to obtain users’ potential interest by distinguishing the importance of different entities. On this basis, a dual aggregation method of high-order propagation is proposed to enable entity information to be propagated more effectively. Through experimental analysis, compared with some baselines, such as Ripplenet, RKGE and CKE, our method has certain advantages in the evaluation indicators AUC and F1.
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Grundspenkis, Janis, and Maija Strautmane. "Usage of Graph Patterns for Knowledge Assessment Based on Concept Maps." Scientific Journal of Riga Technical University. Computer Sciences 38, no. 38 (January 1, 2009): 60–71. http://dx.doi.org/10.2478/v10143-009-0005-y.

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Usage of Graph Patterns for Knowledge Assessment Based on Concept MapsThe paper discusses application of concepts maps (CMs) for knowledge assessment. CMs are graphs which nodes represent concepts and arcs represent relationships between them. CMs reveal learners' knowledge structure and allow assessing their knowledge level. Step-by-step construction and use of CMs is easy. However, mere comparison of expert constructed and learners' completed CMs forces students to construct their knowledge exactly in the same way as experts. At the same time it is known that individuals construct their knowledge structures in different ways. The developed adaptive knowledge assessment system which is implemented as multiagent system includes the knowledge evaluation agent which carries out the comparison of CMs. The paper presents a novel approach to comparison of CMs using graph patterns. Graph patterns are subgraphs, i.e., paths with limited length. Graph patterns are given for both fill-in-the-map tasks where CM structure is predefined and construct-the-map tasks. The corresponding production rules of graph patterns allow to expand the expert's constructed CM and in this way to promote more flexible and adaptive knowledge assessment.
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Zhang, Yixiao, Xiaosong Wang, Ziyue Xu, Qihang Yu, Alan Yuille, and Daguang Xu. "When Radiology Report Generation Meets Knowledge Graph." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12910–17. http://dx.doi.org/10.1609/aaai.v34i07.6989.

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Automatic radiology report generation has been an attracting research problem towards computer-aided diagnosis to alleviate the workload of doctors in recent years. Deep learning techniques for natural image captioning are successfully adapted to generating radiology reports. However, radiology image reporting is different from the natural image captioning task in two aspects: 1) the accuracy of positive disease keyword mentions is critical in radiology image reporting in comparison to the equivalent importance of every single word in a natural image caption; 2) the evaluation of reporting quality should focus more on matching the disease keywords and their associated attributes instead of counting the occurrence of N-gram. Based on these concerns, we propose to utilize a pre-constructed graph embedding module (modeled with a graph convolutional neural network) on multiple disease findings to assist the generation of reports in this work. The incorporation of knowledge graph allows for dedicated feature learning for each disease finding and the relationship modeling between them. In addition, we proposed a new evaluation metric for radiology image reporting with the assistance of the same composed graph. Experimental results demonstrate the superior performance of the methods integrated with the proposed graph embedding module on a publicly accessible dataset (IU-RR) of chest radiographs compared with previous approaches using both the conventional evaluation metrics commonly adopted for image captioning and our proposed ones.
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Dissertations / Theses on the topic "Knowledge Graph Evaluation"

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Stefanoni, Giorgio. "Evaluating conjunctive and graph queries over the EL profile of OWL 2." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:232978e9-90a2-41cc-afd5-319518296894.

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OWL 2 EL is a popular ontology language that is based on the EL family of description logics and supports regular role inclusions,axioms that can capture compositional properties of roles such as role transitivity and reflexivity. In this thesis, we present several novel complexity results and algorithms for answering expressive queries over OWL 2 EL knowledge bases (KBs) with regular role inclusions. We first focus on the complexity of conjunctive query (CQ) answering in OWL 2 EL and show that the problem is PSpace-complete in combined complexity, the complexity measured in the total size of the input. All the previously known approaches encode the regular role inclusions using finite automata that can be worst-case exponential in size, and thus are not optimal. In our PSpace procedure, we address this problem by using a novel, succinct encoding of regular role inclusions based on pushdown automata with a bounded stack. Moreover, we strengthen the known PSpace lower complexity bound and show that the problem is PSpace-hard even if we consider only the regular role inclusions as part of the input and the query is acyclic; thus, our algorithm is optimal in knowledge base complexity, the complexity measured in the size of the KB, as well as for acyclic queries. We then study graph queries for OWL 2 EL and show that answering positive, converse- free conjunctive graph queries is PSpace-complete. Thus, from a theoretical perspective, we can add navigational features to CQs over OWL 2 EL without an increase in complexity. Finally, we present a practicable algorithm for answering CQs over OWL 2 EL KBs with only transitive and reflexive composite roles. None of the previously known approaches target transitive and reflexive roles specifically, and so they all run in PSpace and do not provide a tight upper complexity bound. In contrast, our algorithm is optimal: it runs in NP in combined complexity and in PTime in KB complexity. We also show that answering CQs is NP-hard in combined complexity if the query is acyclic and the KB contains one transitive role, one reflexive role, or nominals—concepts containing precisely one individual.
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McNaughton, Ross. "Inference graphs : a structural model and measures for evaluating knowledge-based systems." Thesis, London South Bank University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260994.

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Issa, Subhi. "Linked data quality : completeness and conciseness." Electronic Thesis or Diss., Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1274.

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La large diffusion des technologies du Web Sémantique telles que le Resource Description Framework (RDF) permet aux individus de construire leurs bases de données sur le Web, d'écrire des vocabulaires et de définir des règles pour organiser et expliquer les relations entre les données selon les principes des données liées. En conséquence, une grande quantité de données structurées et interconnectées est générée quotidiennement. Un examen attentif de la qualité de ces données pourrait s'avérer très critique, surtout si d'importantes recherches et décisions professionnelles en dépendent. La qualité des données liées est un aspect important pour indiquer leur aptitude à être utilisées dans des applications. Plusieurs dimensions permettant d'évaluer la qualité des données liées sont identifiées, telles que la précision, la complétude, la provenance et la concision. Cette thèse se concentre sur l'évaluation de la complétude et l'amélioration de la concision des données liées. En particulier, nous avons d'abord proposé une approche de calcul de complétude fondée sur un schéma généré. En effet, comme un schéma de référence est nécessaire pour évaluer la complétude, nous avons proposé une approche fondée sur la fouille de données pour obtenir un schéma approprié (c.-à-d. un ensemble de propriétés) à partir des données. Cette approche permet de distinguer les propriétés essentielles des propriétés marginales pour générer, pour un ensemble de données, un schéma conceptuel qui répond aux attentes de l'utilisateur quant aux contraintes de complétude des données. Nous avons implémenté un prototype appelé "LOD-CM" pour illustrer le processus de dérivation d'un schéma conceptuel d'un ensemble de données fondé sur les besoins de l'utilisateur. Nous avons également proposé une approche pour découvrir des prédicats équivalents afin d'améliorer la concision des données liées. Cette approche s'appuie, en plus d'une analyse statistique, sur une analyse sémantique approfondie des données et sur des algorithmes d'apprentissage. Nous soutenons que l'étude de la signification des prédicats peut aider à améliorer l'exactitude des résultats. Enfin, un ensemble d'expériences a été mené sur des ensembles de données réelles afin d'évaluer les approches que nous proposons
The wide spread of Semantic Web technologies such as the Resource Description Framework (RDF) enables individuals to build their databases on the Web, to write vocabularies, and define rules to arrange and explain the relationships between data according to the Linked Data principles. As a consequence, a large amount of structured and interlinked data is being generated daily. A close examination of the quality of this data could be very critical, especially, if important research and professional decisions depend on it. The quality of Linked Data is an important aspect to indicate their fitness for use in applications. Several dimensions to assess the quality of Linked Data are identified such as accuracy, completeness, provenance, and conciseness. This thesis focuses on assessing completeness and enhancing conciseness of Linked Data. In particular, we first proposed a completeness calculation approach based on a generated schema. Indeed, as a reference schema is required to assess completeness, we proposed a mining-based approach to derive a suitable schema (i.e., a set of properties) from data. This approach distinguishes between essential properties and marginal ones to generate, for a given dataset, a conceptual schema that meets the user's expectations regarding data completeness constraints. We implemented a prototype called “LOD-CM” to illustrate the process of deriving a conceptual schema of a dataset based on the user's requirements. We further proposed an approach to discover equivalent predicates to improve the conciseness of Linked Data. This approach is based, in addition to a statistical analysis, on a deep semantic analysis of data and on learning algorithms. We argue that studying the meaning of predicates can help to improve the accuracy of results. Finally, a set of experiments was conducted on real-world datasets to evaluate our proposed approaches
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Haller, Armin, Javier D. Fernández, Maulik R. Kamdar, and Axel Polleres. "What are Links in Linked Open Data? A Characterization and Evaluation of Links between Knowledge Graphs on the Web." Department für Informationsverarbeitung und Prozessmanagement, WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/7193/1/20191002ePub_LOD_link_analysis.pdf.

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Linked Open Data promises to provide guiding principles to publish interlinked knowledge graphs on the Web in the form of findable, accessible, interoperable and reusable datasets. We argue that while as such, Linked Data may be viewed as a basis for instantiating the FAIR principles, there are still a number of open issues that cause significant data quality issues even when knowledge graphs are published as Linked Data. Firstly, in order to define boundaries of single coherent knowledge graphs within Linked Data, a principled notion of what a dataset is, or, respectively, what links within and between datasets are, has been missing. Secondly, we argue that in order to enable FAIR knowledge graphs, Linked Data misses standardised findability and accessability mechanism, via a single entry link. In order to address the first issue, we (i) propose a rigorous definition of a naming authority for a Linked Data dataset (ii) define different link types for data in Linked datasets, (iii) provide an empirical analysis of linkage among the datasets of the Linked Open Data cloud, and (iv) analyse the dereferenceability of those links. We base our analyses and link computations on a scalable mechanism implemented on top of the HDT format, which allows us to analyse quantity and quality of different link types at scale.
Series: Working Papers on Information Systems, Information Business and Operations
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Ojha, Prakhar. "Utilizing Worker Groups And Task Dependencies in Crowdsourcing." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4265.

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Crowdsourcing has emerged as a convenient mechanism to collect human judgments on a variety of tasks, ranging from document and image classification to scientific experimentation. However, in recent times crowdsourcing has evolved from solving simpler tasks, like recognizing objects in images, to more complex tasks such as collaborative journalism, language translation, product designing etc. Unlike simpler micro-tasks performed by a single worker, these complex tasks require a group of workers and greater resources. In such scenarios, where groups of participants are the atomic units, it is a non-trivial task to distinguish workers (who contribute positively) from idlers (who do not contribute to group task) among the participants using only group's performance. The first part of this thesis studies the problem of distinguishing workers from idlers, without assuming any prior knowledge of individual skills and considers \groups" as the smallest observable unit for evaluation. We draw upon literature from group-testing and give bounds over minimum number of groups required to identify quality of subsets of individuals with high confidence. We validate our theory experimentally and report insights for the number of workers and idlers that can be identified for a given number of group-tasks with significant probability. In most crowdsourcing applications, there exist dependencies among the pool of Human Intelligence Tasks (HITs) and often in practical scenarios there are far too many HITs available than what can realistically be covered by limited available budget. Estimating the accuracy of automatically constructed Knowledge Graphs (KG) is one such important application. Automatic construction of large knowledge graphs has gained wide popularity in recent times. These KGs, such as NELL, Google Knowledge Vault, etc., consist of thousands of predicate-relations (e.g., is Person, is Mayor Of) and millions of their instances (e.g., (Bill de Blasio, is Mayor Of, New York City)). Estimating accuracy of such KGs is a challenging problem due to their size and diversity. In the second part of this study, we show that standard single-task crowdsourc- ing is sub-optimal and very expensive as it ignores dependencies among various predicates and instances. We propose Relational Crowdsourcing (RelCrowd) to overcome this challenge, where the tasks are created while taking dependencies among predicates and instances into account. We apply this framework in the context of large-scale Knowledge Graph Evaluation (KGEval) and demonstrate its effectiveness through extensive experiments on real-world datasets.
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Books on the topic "Knowledge Graph Evaluation"

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Zhang, Ningyu, Shumin Deng, Wei Hu, Meng Wang, and Tianxing Wu. CCKS 2022 - Evaluation Track: 7th China Conference on Knowledge Graph and Semantic Computing Evaluations, CCKS 2022, Qinhuangdao, China, August 24-27, 2022, Revised Selected Papers. Springer, 2023.

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Zhang, Jiangtao, Ming Liu, Haofen Wang, and Bing Qin. CCKS 2021 - Evaluation Track: 6th China Conference on Knowledge Graph and Semantic Computing, CCKS 2021, Guangzhou, China, December 25-26, 2021, Revised Selected Papers. Springer Singapore Pte. Limited, 2022.

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Book chapters on the topic "Knowledge Graph Evaluation"

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Zhou, Zhangquan, and Guilin Qi. "Implementation and Evaluation of a Backtracking Algorithm for Finding All Justifications in OWL 2 EL." In Linked Data and Knowledge Graph, 235–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-54025-7_21.

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van Bakel, Ruud, Teodor Aleksiev, Daniel Daza, Dimitrios Alivanistos, and Michael Cochez. "Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification." In Lecture Notes in Computer Science, 107–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72308-8_8.

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AbstractLarge, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.Structured querying on such incomplete graphs will result in incomplete sets of answers, even if the correct entities exist in the graph, since one or more edges needed to match the pattern are missing. To overcome this problem, several algorithms for approximate structured query answering have been proposed. Inspired by modern Information Retrieval metrics, these algorithms produce a ranking of all entities in the graph, and their performance is further evaluated based on how high in this ranking the correct answers appear.In this work we take a critical look at this way of evaluation. We argue that performing a ranking-based evaluation is not sufficient to assess methods for complex query answering. To solve this, we introduce Message Passing Query Boxes (MPQB), which takes binary classification metrics back into use and shows the effect this has on the recently proposed query embedding method MPQE.
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Wang, Jingchu, Jianyi Liu, Feiyu Chen, Teng Lu, Hua Huang, and Jinmeng Zhao. "Cross-Knowledge Graph Entity Alignment via Neural Tensor Network." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 66–74. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_8.

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AbstractWith the expansion of the current knowledge graph scale and the increase of the number of entities, a large number of knowledge graphs express the same entity in different ways, so the importance of knowledge graph fusion is increasingly manifested. Traditional entity alignment algorithms have limited application scope and low efficiency. This paper proposes an entity alignment method based on neural tensor network (NtnEA), which can obtain the inherent semantic information of text without being restricted by linguistic features and structural information, and without relying on string information. In the three cross-lingual language data sets DBPFR−EN, DBPZH−EN and DBPJP−EN of the DBP15K data set, Mean Reciprocal Rank and Hits@k are used as the alignment effect evaluation indicators for entity alignment tasks. Compared with the existing entity alignment methods of MTransE, IPTransE, AlignE and AVR-GCN, the Hit@10 values of the NtnEA method are 85.67, 79.20, and 78.93, and the MRR is 0.558, 0.511, and 0.499, which are better than traditional methods and improved 10.7% on average.
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Atemezing, Ghislain Auguste. "Empirical Evaluation of a Cloud-Based Graph Database: the Case of Neptune." In Knowledge Graphs and Semantic Web, 31–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91305-2_3.

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Zhang, Yuxin, Bohan Li, Han Gao, Ye Ji, Han Yang, and Meng Wang. "Fine-Grained Evaluation of Knowledge Graph Embedding Models in Downstream Tasks." In Web and Big Data, 242–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_19.

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Kuric, Emil, Javier D. Fernández, and Olha Drozd. "Knowledge Graph Exploration: A Usability Evaluation of Query Builders for Laypeople." In Lecture Notes in Computer Science, 326–42. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33220-4_24.

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Guo, Pengfei, Zhiqing Cun, Tao Yang, Liang Yin, Wenqiang Chang, and Qiang Gao. "Research on Knowledge Graph-Based Business Travel Analysis and Evaluation Methodology." In Applications of Decision Science in Management, 145–53. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2768-3_14.

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Zheng, Xiuwen, Subhasis Dasgupta, and Amarnath Gupta. "P2KG: Declarative Construction and Quality Evaluation of Knowledge Graph from Polystores." In New Trends in Database and Information Systems, 427–39. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42941-5_37.

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De Donato, Renato, Martina Garofalo, Delfina Malandrino, Maria Angela Pellegrino, Andrea Petta, and Vittorio Scarano. "QueDI: From Knowledge Graph Querying to Data Visualization." In Semantic Systems. In the Era of Knowledge Graphs, 70–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59833-4_5.

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Abstract While Open Data (OD) publishers are spur in providing data as Linked Open Data (LOD) to boost innovation and knowledge creation, the complexity of RDF querying languages, such as SPARQL, threatens their exploitation. We aim to help lay users (by focusing on experts in table manipulation, such as OD experts) in querying and exploiting LOD by taking advantage of our target users’ expertise in table manipulation and chart creation. We propose QueDI (Query Data of Interest), a question-answering and visualization tool that implements a scaffold transitional approach to 1) query LOD without being aware of SPARQL and representing results by data tables; 2) once reached our target user comfort zone, users can manipulate and 3) visually represent data by exportable and dynamic visualizations. The main novelty of our approach is the split of the querying phase in SPARQL query building and data table manipulation. In this article, we present the QueDI operating mechanism, its interface supported by a guided use-case over DBpedia, and the evaluation of its accuracy and usability level.
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Huang, Yuan-sheng, Jian-xun Qi, and Jun-hua Zhou. "Method of Risk Discernment in Technological Innovation Based on Path Graph and Variable Weight Fuzzy Synthetic Evaluation." In Fuzzy Systems and Knowledge Discovery, 635–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_79.

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Conference papers on the topic "Knowledge Graph Evaluation"

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Cai, Borui, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, and Jianxin Li. "Temporal Knowledge Graph Completion: A Survey." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/734.

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Knowledge graph completion (KGC) predicts missing links and is crucial for real-life knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. Emerging methods have recently shown improved prediction results by further incorporating the temporal validity of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods explicitly learn the dynamic evolution of the knowledge graph that KGC methods fail to capture. In this paper, for the first time, we comprehensively summarize the recent advances in TKGC research. First, we detail the background of TKGC, including the preliminary knowledge, benchmark datasets, and evaluation metrics. Then, we summarize existing TKGC methods based on how the temporal validity of facts is used to capture the temporal dynamics. Finally, we conclude the paper and present future research directions of TKGC.
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Ji, Yimu, Kaihang Liu, Shangdong Liu, Shuning Tang, Wan Xiao, Zhengyang Xu, Lin Hu, Yanlan Liu, and Qiang Liu. "FEPF: A knowledge Fusion and Evaluation Method based on Pagerank and Feature Selection." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00095.

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Rashid, Sabbir M., Amar K. Viswanathan, Ian Gross, Elisa Kendall, and Deborah L. McGuinness. "Leveraging Semantics for Large-Scale Knowledge Graph Evaluation." In WebSci '17: ACM Web Science Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3091478.3162385.

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Sun, Zhiqing, Shikhar Vashishth, Soumya Sanyal, Partha Talukdar, and Yiming Yang. "A Re-evaluation of Knowledge Graph Completion Methods." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.489.

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Prado-Romero, Mario Alfonso, and Giovanni Stilo. "GRETEL: Graph Counterfactual Explanation Evaluation Framework." In CIKM '22: The 31st ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3511808.3557608.

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Khokhlov, Igor, and Leon Reznik. "Knowledge Graph in Data Quality Evaluation for IoT applications." In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE, 2020. http://dx.doi.org/10.1109/wf-iot48130.2020.9221091.

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Xu, ZhenHao, Yan Gao, and Fei Yu. "Quality Evaluation Model of AI-based Knowledge Graph System." In 2021 3rd International Conference on Natural Language Processing (ICNLP). IEEE, 2021. http://dx.doi.org/10.1109/icnlp52887.2021.00018.

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Faralli, Stefano, Irene Finocchi, Simone Paolo Ponzetto, and Paola Velardi. "Efficient Pruning of Large Knowledge Graphs." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/564.

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In this paper we present an efficient and highly accurate algorithm to prune noisy or over-ambiguous knowledge graphs given as input an extensional definition of a domain of interest, namely as a set of instances or concepts. Our method climbs the graph in a bottom-up fashion, iteratively layering the graph and pruning nodes and edges in each layer while not compromising the connectivity of the set of input nodes. Iterative layering and protection of pre-defined nodes allow to extract semantically coherent DAG structures from noisy or over-ambiguous cyclic graphs, without loss of information and without incurring in computational bottlenecks, which are the main problem of state-of-the-art methods for cleaning large, i.e., Web-scale, knowledge graphs. We apply our algorithm to the tasks of pruning automatically acquired taxonomies using benchmarking data from a SemEval evaluation exercise, as well as the extraction of a domain-adapted taxonomy from the Wikipedia category hierarchy. The results show the superiority of our approach over state-of-art algorithms in terms of both output quality and computational efficiency.
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Mirza, Paramita, Fariz Darari, and Rahmad Mahendra. "KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents." In Proceedings of The 12th International Workshop on Semantic Evaluation. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/s18-1010.

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Halliwell, Nicholas, Fabien Gandon, and Freddy Lecue. "User Scored Evaluation of Non-Unique Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs." In K-CAP '21: Knowledge Capture Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460210.3493557.

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