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Academic literature on the topic 'Domain specific knowledge graph'

Author: Grafiati

Published: 9 March 2023

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Journal articles on the topic "Domain specific knowledge graph"

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Barai, Mohit Kumar, and Subhasis Sanyal. "DOMAIN SPECIFIC KEY FEATURE EXTRACTION USING KNOWLEDGE GRAPH MINING." Multiple Criteria Decision Making 15 (2020): 1–22. http://dx.doi.org/10.22367/mcdm.2020.15.01.

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In the field of text mining, many novel feature extraction approaches have been propounded. The following research paper is based on a novel feature extraction algorithm. In this paper, to formulate this approach, a weighted graph mining has been used to ensure the effectiveness of the feature extraction and computational efficiency; only the most effective graphs representing the maximum number of triangles based on a predefined relational criterion have been considered. The proposed novel technique is an amalgamation of the relation between words surrounding an aspect of the product and the lexicon-based connection among those words, which creates a relational triangle. A maximum number of a triangle covering an element has been accounted as a prime feature. The proposed algorithm performs more than three times better than TF-IDF within a limited set of data in analysis based on domain-specific data. Keywords: feature extraction, natural language processing, product review, text processing, knowledge graph.

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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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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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Choi, Junho. "Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature Summary." Sustainability 14, no. 19 (September 27, 2022): 12299. http://dx.doi.org/10.3390/su141912299.

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Knowledge bases built in the knowledge processing field have a problem in that experts have to add rules or update them through modifications. To solve this problem, research has been conducted on knowledge graph expansion methods using deep learning technology, and in recent years, many studies have been conducted on methods of generating knowledge bases by embedding the knowledge graph’s triple information in a continuous vector space. In this paper, using a research literature summary, we propose a domain-specific knowledge graph expansion method based on graph embedding. To this end, we perform pre-processing and process and text summarization with the collected research literature data. Furthermore, we propose a method of generating a knowledge graph by extracting the entity and relation information and a method of expanding the knowledge graph using web data. To this end, we summarize research literature using the Bidirectional Encoder Representations from Transformers for Summarization (BERTSUM) model based on domain-specific research literature data and design a Research-BERT (RE-BERT) model that extracts entities and relation information, which are components of the knowledge graph, from the summarized research literature. Moreover, we proposed a method of expanding related entities based on Google news after extracting related entities through the web for the entities in the generated knowledge graph. In the experiment, we measured the performance of summarizing research literature using the BERTSUM model and the accuracy of the knowledge graph relation extraction model. In the experiment of removing unnecessary sentences from the research literature text and summarizing them in key sentences, the result shows that the BERTSUM Classifier model’s ROUGE-1 precision is 57.86%. The knowledge graph extraction performance was measured using the mean reciprocal rank (MRR), mean rank (MR), and HIT@N rank-based evaluation metric. The knowledge graph extraction method using summarized text showed superior performance in terms of speed and knowledge graph quality.

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Yuan, Jianbo, Zhiwei Jin, Han Guo, Hongxia Jin, Xianchao Zhang, Tristram Smith, and Jiebo Luo. "Constructing biomedical domain-specific knowledge graph with minimum supervision." Knowledge and Information Systems 62, no. 1 (March 23, 2019): 317–36. http://dx.doi.org/10.1007/s10115-019-01351-4.

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Hassani, Kaveh. "Cross-Domain Few-Shot Graph Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6856–64. http://dx.doi.org/10.1609/aaai.v36i6.20642.

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We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly available datasets. We also propose an attention-based graph encoder that uses three congruent views of graphs, one contextual and two topological views, to learn representations of task-specific information for fast adaptation, and task-agnostic information for knowledge transfer. We run exhaustive experiments to evaluate the performance of contrastive and meta-learning strategies. We show that when coupled with metric-based meta-learning frameworks, the proposed encoder achieves the best average meta-test classification accuracy across all benchmarks.

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Kim, Taejin, Yeoil Yun, and Namgyu Kim. "Deep Learning-Based Knowledge Graph Generation for COVID-19." Sustainability 13, no. 4 (February 19, 2021): 2276. http://dx.doi.org/10.3390/su13042276.

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Many attempts have been made to construct new domain-specific knowledge graphs using the existing knowledge base of various domains. However, traditional “dictionary-based” or “supervised” knowledge graph building methods rely on predefined human-annotated resources of entities and their relationships. The cost of creating human-annotated resources is high in terms of both time and effort. This means that relying on human-annotated resources will not allow rapid adaptability in describing new knowledge when domain-specific information is added or updated very frequently, such as with the recent coronavirus disease-19 (COVID-19) pandemic situation. Therefore, in this study, we propose an Open Information Extraction (OpenIE) system based on unsupervised learning without a pre-built dataset. The proposed method obtains knowledge from a vast amount of text documents about COVID-19 rather than a general knowledge base and add this to the existing knowledge graph. First, we constructed a COVID-19 entity dictionary, and then we scraped a large text dataset related to COVID-19. Next, we constructed a COVID-19 perspective language model by fine-tuning the bidirectional encoder representations from transformer (BERT) pre-trained language model. Finally, we defined a new COVID-19-specific knowledge base by extracting connecting words between COVID-19 entities using the BERT self-attention weight from COVID-19 sentences. Experimental results demonstrated that the proposed Co-BERT model outperforms the original BERT in terms of mask prediction accuracy and metric for evaluation of translation with explicit ordering (METEOR) score.

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Sharma, Bhuvan, Van C. Willis, Claudia S. Huettner, Kirk Beaty, Jane L. Snowdon, Shang Xue, Brett R. South, Gretchen P. Jackson, Dilhan Weeraratne, and Vanessa Michelini. "Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs." JAMIA Open 3, no. 3 (September 29, 2020): 332–37. http://dx.doi.org/10.1093/jamiaopen/ooaa028.

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Abstract Objectives Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. Methods New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus. Results Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph. Conclusion Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.

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Liu, Weijie, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. "K-BERT: Enabling Language Representation with Knowledge Graph." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2901–8. http://dx.doi.org/10.1609/aaai.v34i03.5681.

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Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-specific knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge. However, too much knowledge incorporation may divert the sentence from its correct meaning, which is called knowledge noise (KN) issue. To overcome KN, K-BERT introduces soft-position and visible matrix to limit the impact of knowledge. K-BERT can easily inject domain knowledge into the models by being equipped with a KG without pre-training by itself because it is capable of loading model parameters from the pre-trained BERT. Our investigation reveals promising results in twelve NLP tasks. Especially in domain-specific tasks (including finance, law, and medicine), K-BERT significantly outperforms BERT, which demonstrates that K-BERT is an excellent choice for solving the knowledge-driven problems that require experts.

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Huang, Lan, Yuanwei Zhao, Bo Wang, Dongxu Zhang, Rui Zhang, Subhashis Das, Simone Bocca, and Fausto Giunchiglia. "Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas." Algorithms 14, no. 8 (August 17, 2021): 241. http://dx.doi.org/10.3390/a14080241.

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Knowledge graph-based data integration is a practical methodology for heterogeneous legacy database-integrated service construction. However, it is neither efficient nor economical to build a new cross-domain knowledge graph on top of the schemas of each legacy database for the specific integration application rather than reusing the existing high-quality knowledge graphs. Consequently, a question arises as to whether the existing knowledge graph is compatible with cross-domain queries and with heterogenous schemas of the legacy systems. An effective criterion is urgently needed in order to evaluate such compatibility as it limits the quality upbound of the integration. This research studies the semantic similarity of the schemas from the aspect of properties. It provides a set of in-depth criteria, namely coverage and flexibility, to evaluate the pairwise compatibility between the schemas. It takes advantage of the properties of knowledge graphs to evaluate the overlaps between schemas and defines the weights of entity types in order to perform precise compatibility computation. The effectiveness of the criteria obtained to evaluate the compatibility between knowledge graphs and cross-domain queries is demonstrated using a case study.

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Dissertations / Theses on the topic "Domain specific knowledge graph"

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Lalithsena, Sarasi. "Domain-specific Knowledge Extraction from the Web of Data." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527202092744638.

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PORRINI, RICCARDO. "Construction and Maintenance of Domain Specific Knowledge Graphs for Web Data Integration." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/126789.

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A Knowledge Graph (KG) is a semantically organized, machine readable collection of types, entities, and relations holding between them. A KG helps in mitigating semantic heterogeneity in scenarios that require the integration of data from independent sources into a so called dataspace, realized through the establishment of mappings between the sources and the KG. Applications built on top of a dataspace provide advanced data access features to end-users based on the representation provided by the KG, obtained through the enrichment of the KG with domain specific facets. A facet is a specialized type of relation that models a salient characteristic of entities of particular domains (e.g., the vintage of wines) from an end-user perspective. In order to enrich a KG with a salient and meaningful representation of data, domain experts in charge of maintaining the dataspace must be in possess of extensive knowledge about disparate domains (e.g., from wines to football players). From an end-user perspective, the difficulties in the definition of domain specific facets for dataspaces significantly reduce the user-experience of data access features and thus the ability to fulfill the information needs of end-users. Remarkably, this problem has not been adequately studied in the literature, which mostly focuses on the enrichment of the KG with a generalist, coverage oriented, and not domain specific representation of data occurring in the dataspace. Motivated by this challenge, this dissertation introduces automatic techniques to support domain experts in the enrichment of a KG with facets that provide a domain specific representation of data. Since facets are a specialized type of relations, the techniques proposed in this dissertation aim at extracting salient domain specific relations. The fundamental components of a dataspace, namely the KG and the mappings between sources and KG elements, are leveraged to elicitate such domain specific representation from specialized data sources of the dataspace, and to support domain experts with valuable information for the supervision of the process. Facets are extracted by leveraging already established mappings between specialized sources and the KG. After extraction, a domain specific interpretation of facets is provided by re-using relations already defined in the KG, to ensure tight integration of data. This dissertation introduces also a framework to profile the status of the KG, to support the supervision of domain experts in the above tasks. Altogether, the contributions presented in this dissertation provide a set of automatic techniques to support domain experts in the evolution of the KG of a dataspace towards a domain specific, end-user oriented representation. Such techniques analyze and exploit the fundamental components of a dataspace (KG, mappings, and source data) with an effectiveness not achievable with state-of-the-art approaches, as shown by extensive evaluations conducted in both synthetic and real world scenarios.

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Jen, Chun-Heng. "Exploring Construction of a Company Domain-Specific Knowledge Graph from Financial Texts Using Hybrid Information Extraction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291107.

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Companies do not exist in isolation. They are embedded in structural relationships with each other. Mapping a given company’s relationships with other companies in terms of competitors, subsidiaries, suppliers, and customers are key to understanding a company’s major risk factors and opportunities. Conventionally, obtaining and staying up to date with this key knowledge was achieved by reading financial news and reports by highly skilled manual labor like a financial analyst. However, with the development of Natural Language Processing (NLP) and graph databases, it is now possible to systematically extract and store structured information from unstructured data sources. The current go-to method to effectively extract information uses supervised machine learning models, which require a large amount of labeled training data. The data labeling process is usually time-consuming and hard to get in a domain-specific area. This project explores an approach to construct a company domain-specific Knowledge Graph (KG) that contains company-related entities and relationships from the U.S. Securities and Exchange Commission (SEC) 10-K filings by combining a pre-trained general NLP with rule-based patterns in Named Entity Recognition (NER) and Relation Extraction (RE). This approach eliminates the time-consuming data-labeling task in the statistical approach, and by evaluating ten 10-k filings, the model has the overall Recall of 53.6%, Precision of 75.7%, and the F1-score of 62.8%. The result shows it is possible to extract company information using the hybrid methods, which does not require a large amount of labeled training data. However, the project requires the time-consuming process of finding lexical patterns from sentences to extract company-related entities and relationships.
Företag existerar inte som isolerade organisationer. De är inbäddade i strukturella relationer med varandra. Att kartlägga ett visst företags relationer med andra företag när det gäller konkurrenter, dotterbolag, leverantörer och kunder är nyckeln till att förstå företagets huvudsakliga riskfaktorer och möjligheter. Det konventionella sättet att hålla sig uppdaterad med denna viktiga kunskap var genom att läsa ekonomiska nyheter och rapporter från högkvalificerad manuell arbetskraft som till exempel en finansanalytiker. Men med utvecklingen av ”Natural Language Processing” (NLP) och grafdatabaser är det nu möjligt att systematiskt extrahera och lagra strukturerad information från ostrukturerade datakällor. Den nuvarande metoden för att effektivt extrahera information använder övervakade maskininlärningsmodeller som kräver en stor mängd märkta träningsdata. Datamärkningsprocessen är vanligtvis tidskrävande och svår att få i ett domänspecifikt område. Detta projekt utforskar ett tillvägagångssätt för att konstruera en företagsdomänspecifikt ”Knowledge Graph” (KG) som innehåller företagsrelaterade enheter och relationer från SEC 10-K-arkivering genom att kombinera en i förväg tränad allmän NLP med regelbaserade mönster i ”Named Entity Recognition” (NER) och ”Relation Extraction” (RE). Detta tillvägagångssätt eliminerar den tidskrävande datamärkningsuppgiften i det statistiska tillvägagångssättet och genom att utvärdera tio SEC 10-K arkiv har modellen den totala återkallelsen på 53,6 %, precision på 75,7 % och F1-poängen på 62,8 %. Resultatet visar att det är möjligt att extrahera företagsinformation med hybridmetoderna, vilket inte kräver en stor mängd märkta träningsdata. Projektet kräver dock en tidskrävande process för att hitta lexikala mönster från meningar för att extrahera företagsrelaterade enheter och relationer.

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Kerzhner, Aleksandr A. "Using domain specific languages to capture design knowledge for model-based systems engineering." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28249.

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Dubé, Denis 1981. "Graph layout for domain-specific modeling." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97943.

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The aim of this thesis is to investigate automatic graph layout in the context of domain-specific modeling. Inherent in the nature of domain-specific modeling is the creation of new formalisms to solve the current problem as well as the combined use of multiple formalisms. Unfortunately, graph layout algorithms tend to be formalism-specific, thus limiting their applicability.
As a starting point, all major graph drawing techniques and many of their variants are summarized from the literature. Thereafter, several of these graph drawing techniques are chosen and implemented in AToM3, A Tool for Multi-formalism and Meta-Modeling.
A new means of specifying formalism-specific user-interface behaviour is then described. By fully modeling the reactive behaviour of a formalism-specific modeling environment, including layout, existing graph drawing algorithms can be re-used without modification. The DCharts formalism is modeled to demonstrate the effectiveness of this approach.

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Yoon, Changwoo. "Domain-specific knowledge-based informational retrieval model using knowledge reduction." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011560.

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Eryarsoy, Enes. "Using domain-specific knowledge in support vector machines." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011358.

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Kelemen, Deborah Ann 1967. "The effects of domain-specific knowledge on similarity judgements." Thesis, The University of Arizona, 1992. http://hdl.handle.net/10150/278269.

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The study contrasts natural kinds versus artifacts in order to assess the impact of domain-specific knowledge on adult subjects strategies in a perceptual classification task. Subjects classifications show differential weighting of perceptual dimensions as a consequence of background context. In addition, subjects display a tendency to reject identity within a specific dimension when such a non-identity based strategy permitted the creation of a theoretically cohesive category. This provides evidence against the view that identity possesses an inherent value in classification and supports the alternative, that background knowledge determines the degree to which identity is valued and the manner in which categories are constructed.

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Yang, Mengjiao. "Cache and NUMA optimizations in a domain-specific language for graph processing." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119915.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 63-67).
High-performance graph processing is challenging because the sizes and structures of real-world graphs can vary widely. Graph algorithms also have distinct performance characteristics that lead to different performance bottlenecks. Even though memory technologies such as CPU cache and non-uniform memory access (NUMA) have been designed to improve software performance, the existing graph processing frameworks either do not take advantage of these hardware features or over-complicate the original graph algorithms. In addition, these frameworks do not provide an interface for easily composing and fine-tuning performance optimizations from various levels of the software stack. As a result, they achieve suboptimal performance. The work described in this thesis builds on recent research in developing a domain-specific language (DSL) for graph processing. GraphIt is a DSL designed to provide a comprehensive set of performance optimizations and an interface to combine the best optimization schedules. This work extends the GraphIt DSL to support locality optimizations on modern multisocket multicore machines, while preserving the simplicity of graph algorithms. To our knowledge, this is the first work to support cache and NUMA optimizations in a graph DSL. We show that cache and NUMA optimizations together are able to improve the performance of GraphIt by up to a factor of 3. Combined with all of the optimizations in GraphIt, our performance is up to 4.8x faster than the next fastest existing framework. In addition, algorithms implemented in GraphIt use fewer lines of code than existing frameworks. The work in this thesis supports the design choice of a compiler approach to constructing graph processing systems. The high performance and simplicity of GraphIt justify the separation of concerns (modularity) design principle in computer science, and contribute to the larger effort of agile software systems development.
by Mengjiao Yang.
M. Eng.

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Lewenhaupt, Adam, and Emil Brismar. "The impact of corpus choice in domain specific knowledge representation." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220679.

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Recent advents in the machine learning community, driven by larger datasets and novel algorithmic approaches to deep reinforcement learning, reward the use of large datasets. In this thesis, we examine whether dataset size has a signicant impact on the recall quality in a very specic knowledge domain. We compare a large corpus extracted from Wikipedia to smaller ones from Stackoverow and evaluate their representational quality of niche computer science knowledge. We show that a smaller dataset with high-quality data points greatly outperform a larger one, even though the smaller is a subset of the latter. This implicates that corpus choice is highly relevant for NLP-applications aimed toward complex and specic knowledge representations.

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Books on the topic "Domain specific knowledge graph"

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Kejriwal, Mayank. Domain-Specific Knowledge Graph Construction. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12375-8.

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Gdanskiy, Nikolay. Fundamentals of the theory and algorithms on graphs. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/978686.

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The textbook describes the main theoretical principles of graph theory, the main tasks to be solved using graph structures, and General methods of their solution and specific algorithms, with estimates of their complexity. I covered a lot of the examples given questions to test knowledge and tasks for independent decisions. Along with the control tasks to verify the theoretical training provided practical assignments to develop programs to study topics of graph theory. Meets the requirements of Federal state educational standards of higher education of the last generation. Designed for undergraduate and graduate programs, studying information technology, for in-depth training in analysis and design of systems of complex structure. Also the guide can be useful to specialists of the IT sphere in the study of algorithmic aspects of graph theory.

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Kejriwal, Mayank. Domain-Specific Knowledge Graph Construction. Springer, 2019.

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Fischer, Frank, Jonathan Osborne, Clark A. Chinn, and Katharina Engelmann. Scientific Reasoning and Argumentation: The Roles of Domain-Specific and Domain-General Knowledge. Taylor & Francis Group, 2018.

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Scientific Reasoning and Argumentation: The Roles of Domain-Specific and Domain-General Knowledge. Taylor & Francis Group, 2018.

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Scientific Reasoning and Argumentation: The Roles of Domain-Specific and Domain-General Knowledge. Taylor & Francis Group, 2018.

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Lennox, James G. Aristotle on Inquiry: Erotetic Frameworks and Domain-Specific Norms. University of Cambridge ESOL Examinations, 2021.

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Lennox, James G. Aristotle on Inquiry: Erotetic Frameworks and Domain-Specific Norms. Cambridge University Press, 2021.

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Wahtera, Sandra Lee. DIFFERENTIATING NURSING PROCESS PERFORMANCE BY EDUCATION, EXPERIENCE, DOMAIN-SPECIFIC KNOWLEDGE, STRATEGIC KNOWLEDGE AND SELF-EFFICACY. 1991.

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The effect of domain knowledge on searching for specific information in a hypertext environment. Ann Arbor, Mich: University Microfilms International, 1991.

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Book chapters on the topic "Domain specific knowledge graph"

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Kejriwal, Mayank. "What Is a Knowledge Graph?" In Domain-Specific Knowledge Graph Construction, 1–7. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12375-8_1.

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Kejriwal, Mayank. "Advanced Topic: Knowledge Graph Completion." In Domain-Specific Knowledge Graph Construction, 59–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12375-8_4.

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Kejriwal, Mayank. "Information Extraction." In Domain-Specific Knowledge Graph Construction, 9–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12375-8_2.

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Kejriwal, Mayank. "Entity Resolution." In Domain-Specific Knowledge Graph Construction, 33–57. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12375-8_3.

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Kejriwal, Mayank. "Ecosystems." In Domain-Specific Knowledge Graph Construction, 75–87. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12375-8_5.

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Mo, Wenkai, Peng Wang, Haiyue Song, Jianyu Zhao, and Xiang Zhang. "Learning Domain-Specific Ontologies from the Web." In Linked Data and Knowledge Graph, 132–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-54025-7_12.

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Yang, Hang, Yubo Chen, Kang Liu, and Jun Zhao. "Meta Learning for Event Argument Extraction via Domain-Specific Information Enhanced." In Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence, 160–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1964-9_13.

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Jain, Nitisha. "Domain-Specific Knowledge Graph Construction for Semantic Analysis." In The Semantic Web: ESWC 2020 Satellite Events, 250–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62327-2_40.

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Huang, Shanshan, and Xiaojun Wan. "AKMiner: Domain-Specific Knowledge Graph Mining from Academic Literatures." In Lecture Notes in Computer Science, 241–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41154-0_18.

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Gohourou, Didier, and Kazuhiro Kuwabara. "Building a Domain-Specific Knowledge Graph for Business Networking Analysis." In Intelligent Information and Database Systems, 362–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73280-6_29.

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Conference papers on the topic "Domain specific knowledge graph"

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Alotaibi, Rana, Chuan Lei, Abdul Quamar, Vasilis Efthymiou, and Fatma Ozcan. "Property Graph Schema Optimization for Domain-Specific Knowledge Graphs." In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. http://dx.doi.org/10.1109/icde51399.2021.00085.

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Goyal, Nidhi, Niharika Sachdeva, Vijay Choudhary, Rijula Kar, Ponnurangam Kumaraguru, and Nitendra Rajput. "Con2KG-A Large-scale Domain-Specific Knowledge Graph." In HT '19: 30th ACM Conference on Hypertext and Social Media. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3342220.3344931.

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Yang, Hao, Gengui Xie, Ying Qin, and Song Peng. "Domain Specific NMT based on Knowledge Graph Embedding and Attention." In 2019 21st International Conference on Advanced Communication Technology (ICACT). IEEE, 2019. http://dx.doi.org/10.23919/icact.2019.8701980.

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Zhao, Xuejiao, Zhenchang Xing, Muhammad Ashad Kabir, Naoya Sawada, Jing Li, and Shang-Wei Lin. "HDSKG: Harvesting domain specific knowledge graph from content of webpages." In 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2017. http://dx.doi.org/10.1109/saner.2017.7884609.

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Wang, Yan, Yacine Allouache, and Christian Joubert. "A Staffing Recommender System based on Domain-Specific Knowledge Graph." In 2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS). IEEE, 2021. http://dx.doi.org/10.1109/snams53716.2021.9732087.

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Hou, Pei-Yu, Daniel R. Korn, Cleber C. Melo-Filho, David R. Wright, Alexander Tropsha, and Rada Chirkova. "Compact Walks: Taming Knowledge-Graph Embeddings with Domain- and Task-Specific Pathways." In SIGMOD/PODS '22: International Conference on Management of Data. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514221.3517903.

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Zhu, Xixi, Bin Liu, Zhaoyun Ding, Cheng Zhu, and Li Yao. "Approximate Ontology Reasoning for Domain-Specific Knowledge Graph based on Deep Learning." In 2021 7th International Conference on Big Data and Information Analytics (BigDIA). IEEE, 2021. http://dx.doi.org/10.1109/bigdia53151.2021.9619694.

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Yang, Yuanyuan, Kailei Li, Yun Yan, and Jiali Zhu. "Research on the Development Process and Construction of Domain-specific Knowledge Graph." In 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2022. http://dx.doi.org/10.1109/ipec54454.2022.9777576.

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Stewart, Michael, and Wei Liu. "Seq2KG: An End-to-End Neural Model for Domain Agnostic Knowledge Graph (not Text Graph) Construction from Text." In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/77.

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Knowledge Graph Construction (KGC) from text unlocks information held within unstructured text and is critical to a wide range of downstream applications. General approaches to KGC from text are heavily reliant on the existence of knowledge bases, yet most domains do not even have an external knowledge base readily available. In many situations this results in information loss as a wealth of key information is held within "non-entities". Domain-specific approaches to KGC typically adopt unsupervised pipelines, using carefully crafted linguistic and statistical patterns to extract co-occurred noun phrases as triples, essentially constructing text graphs rather than true knowledge graphs. In this research, for the first time, in the same flavour as Collobert et al.'s seminal work of "Natural language processing (almost) from scratch" in 2011, we propose a Seq2KG model attempting to achieve "Knowledge graph construction (almost) from scratch". An end-to-end Sequence to Knowledge Graph (Seq2KG) neural model jointly learns to generate triples and resolves entity types as a multi-label classification task through deep learning neural networks. In addition, a novel evaluation metric that takes both semantic and structural closeness into account is developed for measuring the performance of triple extraction. We show that our end-to-end Seq2KG model performs on par with a state of the art rule-based system which outperformed other neural models and won the first prize of the first Knowledge Graph Contest in 2019. A new annotation scheme and three high-quality manually annotated datasets are available to help promote this direction of research.

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Wang, Shuai, Yu Zhang, and Zhiyong Liao. "Building Domain-Specific Knowledge Graph for Unmanned Combat Vehicle Decision Making under Uncertainty." In 2019 Chinese Automation Congress (CAC). IEEE, 2019. http://dx.doi.org/10.1109/cac48633.2019.8996418.

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Reports on the topic "Domain specific knowledge graph"

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Pereira, Fernando. Candide: An Interactive System for the Acquisition of Domain Specific Knowledge. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada196646.

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Shalatska, Hanna M., Olena Yu Zotova-Sadylo, and Ivan O. Muzyka. Moodle course in teaching English language for specific purposes for masters in mechanical engineering. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3881.

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The central thesis of this paper is that e-learning courses can have a significant impact on English language for specific purposes (ESP) proficiency of mining mechanical engineering students. The purpose of this study is to assess the effectiveness of ESP Moodle-based course “English for Mining Mechanical Engineers” and to reveal the results of its experimental approbation. In order to identify the lectures’ and learners’ needs we have applied the survey research. The survey confirmed the greatest demand for Moodle courses that include all the elements of a coherent training manual to provide self-development of engineering students. The interview results contributed to design of author’s ESP course syllabus. The importance and originality of this study are that to approbate the course materials’ effectiveness two approaches have been adopted simultaneously. The first is blended learning method based on e-learning platform applied in the experimental group and the second one is classic in-class instructor-led studying used in a control group. Students’ progress in ESP proficiency has been assessed using the cross assessment method. The experiment has validated the initial hypothesis that the special online courses focused on honing foreign language skills and integrated in the domain of specific professional knowledge have a beneficial effect on students’ communicative competencies in general. There were identified the advantages of self-tuition based on Moodle platform. The Moodle course lets the teachers save considerable in-class time to focus more on communicative assignments. The findings of this study have a number of practical implications in ESP online courses development.

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Shapovalov, Viktor B., Yevhenii B. Shapovalov, Zhanna I. Bilyk, Artem I. Atamas, Roman A. Tarasenko, and Vitaliy V. Tron. Centralized information web-oriented educational environment of Ukraine. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3251.

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The modern development of science and technology has provided high quantity of information. This information must be systemized and classified. For taxonomization of educational materials, it was proposed to use existing graph-generators and graph-visualizers of the TODOS IT platform. A separate aspect of the TODOS IT platform is the possibility of using a centralized web-oriented learning environment. Creation of the system and transdisciplinary knowledge is a problem of modern education, which can be solved by creating a centralized web-oriented educational environment. Using this approach is an important part of the learning process. Such a centralized web-oriented environment based on the ontological approach involves filling, adaptive educational services with information resources that reflect the conceptual system of a particular discipline. One of the systems providing not only collection of information but include its systemizing is centralized web-oriented educational environment based on Ontology4 system. Ontology 4 use elements of the TODOS. The paper presents specific developments of one centralized web-oriented educational environment can be used to teach different subjects such as biology, chemistry, Ukrainian language and literature, using the STEM approach.

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Mehmood, Hamid. Bibliometrics of Water Research: A Global Snapshot. United Nations University Institute for Water, Environment and Health, May 2019. http://dx.doi.org/10.53328/eybt8774.

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This report examines the various dimensions of global water-related research over the 2012-2017 period, using extensive bibliographic data. The review covers trends in water-related publications and citations, the relative importance of water-related research in the overall body of scientific research, flows of water-related knowledge between countries and the dynamics of water research publishing opportunities. In summary, it shows that: less than 50% of all countries are publishing water-related research, that China and USA are the two top publishers, and that China’s publishing rate has been growing steadily over the study period. More than 70% of water related publications originating in USA are being cited globally, while China’s water research output appears to be primarily internally cited at present. Analysis of the global water knowledge flows suggests that research is hardly addressing a range of regional water challenges. Countries with protracted water problems – for example in infrastructure, environment, agriculture, energy solutions – do not seem to be at the forefront of water research production or knowledge transfer. Instead, global water research is reliant on Western, particularly US-produced, scientific outputs. A disconnect is also observed between the percentage increase in the publication and the number of citations, suggesting low quality or a narrow focus of many publications. Among other factors, this may reflect the pressure on researchers to contribute a certain number of publications per year, or of the progressively increasing role of grey literature in scientific discourse that ‘diverts’ some citation flow. Analysis of the number of research publications per million people suggests that water research does not necessarily emerge as a reaction to water scarcity in a specific country, but may be driven by the traditional economic value of water supply, geopolitical location, a focus on regional development - including cross-border water management - or development aid spending, or globally applicable research in water management. The proportion of water research in the overall research output of a country is small, including for some of the top-publishing countries. The number of water-related journals that create opportunities for publishing water research, has grown dramatically in absolute terms since 2000, and is now close 2100 journals. The metrics used in this report are based on readily available bibliographic data. They can be further focused to better understand a specific thematic domain, geographical region or country, or to analyze a different period. To help accelerate solutions to global and national water challenges that many of these research papers are highlighting, the water research community needs to look beyond the research ‘box’ and identify ways to measure development impact of water research programmes, rather ‘impact’ based solely on academic impact measured in citations. The research findings, learning and knowledge in these research publications needs to be conveyed in a practical way to the real users of this knowledge – stakeholders who are beyond research circles.

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Marold, Juliane, Ruth Wagner, Markus Schöbel, and Dietrich Manzey. Decision-making in groups under uncertainty. Fondation pour une culture de sécurité industrielle, February 2012. http://dx.doi.org/10.57071/361udm.

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The authors have studied daily decision-making processes in groups under uncertainty, with an exploratory field study in the medical domain. The work follows the tradition of naturalistic decision-making (NDM) research. It aims to understand how groups in this high reliability context conceptualize and internalize uncertainties, and how they handle them in order to achieve effective decision-making in their everyday activities. Analysis of the survey data shows that uncertainty is thought of in terms of issues and sources (as identified by previous research), but also (possibly a domain-specific observation) as a lack of personal knowledge or skill. Uncertainty is accompanied by emotions of fear and shame. It arises during the diagnostic process, the treatment process and the outcome of medical decision making. The most frequently cited sources of uncertainty are partly lacking information and inadequate understanding owing to instability of information. Descriptions of typical group decisions reveal that the individual himself is a source of uncertainty when a lack of knowledge, skills and expertise is perceived. The group can serve as a source of uncertainty if divergent opinions in the decision making group exist. Three different situations of group decisions are identified: Interdisciplinary regular meetings (e.g. tumor conferences), formal ward meetings and ad hoc consultations. In all healthcare units concerned by the study, only little use of structured decision making procedures and processes is reported. Strategies used to handle uncertainty include attempts to reduce uncertainty by collecting additional information, delaying action until more information is available or by soliciting advice from other physicians. The factors which ultimately determine group decisions are hierarchy (the opinion of more senior medical staff carries more weight than that of junior staff), patients’ interest and professional competence. Important attributes of poor group decisions are the absence of consensus and the use of hierarchy as the predominant decision criterion. On the other hand, decisions judged to be effective are marked by a sufficient information base, a positive discussion culture and consensus. The authors identify four possible obstacles to effective decision making: a steep hierarchy gradient, a poor discussion culture, a strong need for consensus, and insufficient structure and guidance of group decision making processes. A number of intervention techniques which have been shown in other industries to be effective in improving some of these obstacles are presented.

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Sessa, Guido, and Gregory Martin. role of FLS3 and BSK830 in pattern-triggered immunity in tomato. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7604270.bard.

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Pattern-recognition receptors (PRRs) located on the plant cell surface initiate immune responses by perceiving conserved pathogen molecules known as pathogen-associated molecular patterns (PAMPs). PRRs typically function in multiprotein complexes that include transmembrane and cytoplasmickinases and contribute to the initiation and signaling of pattern-triggered immunity (PTI). An important challenge is to identify molecular components of PRR complexes and downstream signaling pathways, and to understand the molecular mechanisms that mediate their function. In research activities supported by BARD-4931, we studied the role of the FLAGELLIN SENSING 3 (FLS3) PRR in the response of tomato leaves to flagellin-derivedPAMPs and PTI. In addition, we investigated molecular properties of the tomato brassinosteroid signaling kinase 830 (BSK830) that physically interacts with FLS3 and is a candidate for acting in the FLS3 signaling pathway. Our investigation refers to the proposal original objectives that were to: 1) Investigate the role of FLS3 and its interacting proteins in PTI; 2) Investigate the role of BSK830 in PTI; 3) Examine molecular and phosphorylation dynamics of the FLS3-BSK830 interaction; 4) Examine the possible interaction of FLS3 and BSK830 with Pstand Xcveffectors. We used CRISPR/Cas9 techniques to develop plants carrying single or combined mutations in the FLS3 gene and in the paralogsFLS2.1 and FLS2.2 genes, which encode the receptor FLAGELLIN SENSING2 (FLS2), and analyzed their function in PTI. Domain swapping analysis of the FLS2 and FLS3 receptors revealed domains of the proteins responsible for PAMP detection and for the different ROS response initiated by flgII-28/FLS3 as compared to flg22/FLS2. In addition, in vitro kinase assays and point mutations analysis identified FLS2 and FLS3 domains required for kinase activity and ATP binding. In research activities on tomato BSK830, we found that it interacts with PRRs and with the co-receptor SERK3A and PAMP treatment affects part of these interactions. CRISPR/Cas9 bsk830 mutant plants displayed enhanced pathogen susceptibility and reduced ROS production upon PAMP treatment. In addition, BSK830 interacted with 8 Xanthomonastype III secreted effectors. Follow up analysis revealed that among these effectors XopAE is part of an operon, is translocated into plant cells, and displays E3 ubiquitinligase activity. Our investigation was also extended to other Arabidopsis and tomato BSK family members. Arabidopsis BSK5 localized to the plant cell periphery, interacted with receptor-like kinases, and it was phosphorylatedin vitro by the PEPR1 and EFRPRRs. bsk5 mutant plants displayed enhanced susceptibility to pathogens and were impaired in several, but not all, PAMP-induced responses. Conversely, BSK5 overexpression conferred enhanced disease resistance and caused stronger PTI responses. Genetic complementation suggested that proper localization, kinase activity, and phosphorylation by PRRs are critical for BSK5 function. BSK7 and BSK8 specifically interacted with the FLS2 PRR, their respective mutant plants were more susceptible to B. cinereaand displayed reduced flg22-induced responses. The tomato BSK Mai1 was found to interact with the M3KMAPKKK, which is involved in activation of cell death associated with effector-triggered immunity. Silencing of Mai1 in N. benthamianaplants compromised cell death induced by a specific class of immune receptors. In addition, co-expression of Mai1 and M3Kin leaves enhanced MAPKphosphorylation and cell death, suggesting that Mai1 acts as a molecular link between pathogen recognition and MAPK signaling. Finally, We identified the PP2C phosphatase Pic1 that acts as a negative regulator of PTI by interacting with and dephosphorylating the receptor-like cytoplasmickinase Pti1, which is a positive regulator of plant immunity. The results of this investigation shed new light on the molecular characteristics and interactions of components of the immune system of crop plants providing new knowledge and tools for development of novel strategies for disease control.

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