Review of Biomedical Knowledge and Data Representation with Conceptual Graphs

Abstract

Abstract:The basis of conceptual graphs theory is an ontology of types of concepts. Concepts issued from the ontology are interlinked by semantic relationships and constitute canonical conceptual graphs. Canonical graphs may be combined to derive new conceptual graphs by means of formation rules. This formalism allows to separate knowledge representation into a conceptual level and a domain-dependent level, and enables to share and reuse a representation. This paper presents conceptual graph applications to biomedical data and concept representation, classification systems, information retrieval, and natural language understanding and processing. A discussion on the unifying role conceptual graphs theory plays in the implementation of knowledge-based systems is also presented.

ARAX: a graph-based modular reasoning tool for translational biomedicine

“…The query and its results are available as online supplementary material. 10 The "N-methyl-D-aspartate receptors" result (which we expected to see based on prior literature [44]) is in the top 10 highest-scoring results, based on ARAX's result-graph ranking algorithm (see Sec. 2.1.5).…”

“…ARAX can also be used via its publicly-accessible API 10 , which is listed in the SmartAPI registry [31]. The ARAX API includes /query and /asyncquery endpoints that accept TRAPI input, as well as an /entity endpoint that exposes the ARAX Node Synonymizer service, allowing users to programmatically retrieve equivalent identifiers and other synonym information for a given concept.…”

“…requires integrating facts spanning a variety of concept types including drugs, targets, pathways, genetic variants, phenotypes, and diseases. To facilitate such computational reasoning, there have been numerous efforts to integrate knowledge from various biomedical databases using knowledge graph (KG) abstractions [9, 10, 11] consisting of a labeled multigraph in which each node represents a concept and each edge represents a concept-concept relationship, i.e., a “triple”. Several such biomedical KGs have been described [12, 13, 14, 15, 16, 17, 18], including RTX-KG2, which we developed and described previously [19] and which integrates all of the aforementioned primary databases with a semantic layer that is described by the open-standard Biolink model [20].…”

ARAX: a graph-based modular reasoning tool for translational biomedicine

“…requires integrating facts spanning a variety of concept types including drugs, targets, pathways, genetic variants, phenotypes and diseases. To facilitate such computational reasoning, there have been numerous efforts to integrate knowledge from various biomedical databases using knowledge graph (KG) abstractions ( Joubert et al , 1998a , b ; Sowa, 1992 ) consisting of a labeled multigraph in which each node represents a concept and each edge represents a concept–concept relationship, that is, a ‘triple’. Several such biomedical KGs have been described ( Dumontier et al , 2014 ; Himmelstein et al , 2017 ; Messina et al , 2018a ; Morton et al , 2019 ; Piñero et al , 2017 ; Sanders et al , 2020 ; github.com/gnn4dr/DRKG), including RTX-KG2, which we developed and described previously ( Wood et al , 2021 ) and which integrates all of the aforementioned primary databases with a semantic layer that is described by the open-standard Biolink model ( Unni et al , 2022 ).…”