Link Prediction Using Semi-Automated Ontology and Knowledge Graph in Medical Sphere

Varma, Sandeep; Shivam, Shivam; Jamaiyar, Rishu; Anukriti, Anukriti; Tripathi, Priyanka; Sarkar, Anurag

doi:10.1109/indicon49873.2020.9342301

Cited by 2 publications

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“…In fact, ontology describes the basic framework of people's cognition of a domain, and, in contrast, a knowledge graph is rich in entities and relation instances. So far, ontology has been widely used in the research of knowledge graphs [31][32][33][34]. As noted by Paulheim [35], ontology is mainly involved in the construction and data introduction phases of KGs.…”

Section: Classical Research Limitationsmentioning

confidence: 99%

Research on Knowledge Graphs with Concept Lattice Constraints

et al. 2021

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The application of knowledge graphs has been restricted in some domains, especially the industrial and academic domains. One of the reasons is that they require a high reliability of knowledge, which cannot be satisfied by the existing knowledge graph research. By comparison, traditional knowledge engineering has a high correctness, but low efficiency is an inevitable drawback. Therefore, it is meaningful to organically connect traditional knowledge engineering and knowledge graphs. Therefore, we propose a theory from Attribute Implications to Knowledge Graphs, named AIs-KG, which can construct knowledge graphs based on implications. The theory connects formal concept analysis and knowledge graphs. We firstly analyze the mutual transformation based on the ideas of symmetry with a strict proof among the attribute implication, the formal context and the concept lattice, which forms the closed cycle between the three. Particularly, we propose an Augment algorithm (IFC-A) to generate the Implication Formal Context through the attribute implication, which can make knowledge more complete. Furthermore, we regard ontology as a bridge to realize the transformation from the concept lattice to the knowledge graph through some mapping methods. We conduct our experiments on the attribute implication from the rule base of an animal recognition expert system to prove the feasibility of our algorithms.

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Section: Classical Research Limitationsmentioning

confidence: 99%

Research on Knowledge Graphs with Concept Lattice Constraints

et al. 2021

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Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities

et al. 2023

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The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird’s eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.

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Link Prediction Using Semi-Automated Ontology and Knowledge Graph in Medical Sphere

Cited by 2 publications

References 1 publication

Research on Knowledge Graphs with Concept Lattice Constraints

Research on Knowledge Graphs with Concept Lattice Constraints

Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities

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