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Addressing common challenges with knowledge graphs - SciBite scibite.com 10 facts
accountJoe Mullen has been employed at SciBite since 2017, where he has led the Data Science and Professional Services teams.
claimSciBite semantic technologies facilitate knowledge graph construction by aligning and harmonizing data with standards, extracting relations, and supporting schema generation to create integrated networks from unstructured literature and structured data.
claimSciBite consultants provide expertise in Stardog and Neo4J technologies to assist in selecting appropriate tools for knowledge graph applications.
claimSciBite's CENtree platform provides a centralized resource for managing, extending, and creating new ontologies for domains not covered by existing vocabularies.
claimSciBite enhances its VOCabs using a combination of manual curation and proprietary ontology enrichment software to provide greater coverage and depth than public ontologies like MeSH and MeDDRA.
measurementSciBite provides a range of ontologies called VOCabs, which comprise tens of millions of synonyms for more than 120 life science entity types, including gene, drug, and disease.
claimSciBite defines a knowledge graph as a semantic graph that integrates information into an ontology.
claimSciBite semantic technologies are provided as microservices that can be embedded into automated knowledge graph creation pipelines.
procedureTo extract relationships from text, SciBite defines semantic patterns called 'bundles' in the form of Gene-Verb-Drug, which are then processed using the TExpress tool to generate semantic triples aligned to ontologies.
claimSciBite's TERMite Named Entity Recognition (NER) engine identifies scientific entities in unstructured text and aligns them to unique identifiers in ontologies to produce structured data.