Category-based Equational Reasoning: An Approach to Ontology Integration

Geldart, Joe; Song, William Wei

doi:10.1093/logcom/exn044

Cited by 3 publications

(1 citation statement)

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“…We look to such an infomorphism theoretic model to form a foundation upon which we are able to seek for explanation for other models and systems of the knowledge grid. We initially use the infomorphism model for transferring and reasoning about RDF/OWL in the context of the semantic web [8], where we found it successful in concept inheritance and composition with its abundant properties (refer to Information Flow [1] for detail discussion).…”

Section: VIImentioning

confidence: 99%

An Analysis Model for Knowledge Grid and Flows

Song

Geldart

2009

2009 Fifth International Conference on Semantics, Knowledge and Grid

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The knowledge grid (KG) has gradually been becoming a keen research topic in the joint field of the Semantic Web, the Semantic Grid, and the Web Services for its potential impact in making full use of various resources, being it conceptual or physical, from the web and grid. Many research activities in this direction focus on the architecture and its components, and base themselves technically on data mining technologies or the like, which obviously considers (web) information systems to be a foundational layer for the grid and web. However, further study on the web has revealed that the web cannot, and unnecessarily, be viewed simply as a closed world model (CWM). The web is an incomplete and inaccurate (knowledge) system, from which we will seek answers regardless their inconsistency. In this paper, we will first investigate the current researches into the knowledge grid, then discuss the problems of incompleteness of knowledge processing and reasoning and propose a category theory based model to handle such incomplete knowledge, and finally provide an analysis of the model.

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Section: VIImentioning

confidence: 99%

An Analysis Model for Knowledge Grid and Flows

Song

Geldart

2009

2009 Fifth International Conference on Semantics, Knowledge and Grid

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A framework of genealogy knowledge reasoning and visualization based on a knowledge graph

Wang

Deng

Guan³

et al. 2023

LHT

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PurposeWith the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.Design/methodology/approachBased on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.FindingsThe case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.Originality/valueThis study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

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