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Soshnikov, D.: ROMEO: an ontology-based multi-agent architecture for online information retrieval (2021)
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- Abstract
- This paper describes an approach to path-finding in the intelligent graphs, with vertices being intelligent agents. A possible implementation of this approach is described, based on logical inference in distributed frame hierarchy. Presented approach can be used for implementing distributed intelligent information systems that include automatic navigation and path generation in hypertext, which can be used, for example in distance education, as well as for organizing intelligent web catalogues with flexible ontology-based information retrieval.
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Gladun, A.; Rogushina, J.: Development of domain thesaurus as a set of ontology concepts with use of semantic similarity and elements of combinatorial optimization (2021)
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- Abstract
- We consider use of ontological background knowledge in intelligent information systems and analyze directions of their reduction in compliance with specifics of particular user task. Such reduction is aimed at simplification of knowledge processing without loss of significant information. We propose methods of generation of task thesauri based on domain ontology that contain such subset of ontological concepts and relations that can be used in task solving. Combinatorial optimization is used for minimization of task thesaurus. In this approach, semantic similarity estimates are used for determination of concept significance for user task. Some practical examples of optimized thesauri application for semantic retrieval and competence analysis demonstrate efficiency of proposed approach.
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Hauff-Hartig, S.: Wissensrepräsentation durch RDF: Drei angewandte Forschungsbeispiele : Bitte recht vielfältig: Wie Wissensgraphen, Disco und FaBiO Struktur in Mangas und die Humanities bringen (2021)
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- Date
- 22. 5.2021 12:43:05
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Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024)
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- Abstract
- Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
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Jia, J.: From data to knowledge : the relationships between vocabularies, linked data and knowledge graphs (2021)
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- Date
- 22. 1.2021 14:24:32
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Hocker, J.; Schindler, C.; Rittberger, M.: Participatory design for ontologies : a case study of an open science ontology for qualitative coding schemas (2020)
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- Date
- 20. 1.2015 18:30:22
