They not only allow users to integrate diverse datasets but can enrich existing data with contextual information which can help to join the dots in research.
Whether your use case is target prioritization, safety profiling or organizing experimental data, SciBite’s technology can empower your team to create the foundational data that feeds graph database systems.
SciBite’s semantic annotation tools scan vast quantities of scientific publications to assign unique IDs to scientific concepts. With supporting evidence from internal and public sources, your team can fully interrogate the data within these graphs and uncover deeper insights within relationships.
Unify data held in internal and external silos according to common identifiers and aligned ontologies.
Query and explore data through connected concepts and relationships to find valuable insights.
Data linked to public standards promotes the sharing and repurposing of data across applications.
Intuitive query capabilities aided by machine learning have reduced the barrier to entry for those who are interested in employing knowledge graphs in their project work.
SciBite can help your team to create semantic knowledge graphs that adhere to our FAIR principles, with the opportunity to add your own proprietary insights. Here’s how SciBite can help you create a comprehensive knowledge graph pipeline that can also be automated depending on your use case.
Ontologies form the backbone of any knowledge graph project. SciBite has an extensive set of expertly curated ontologies to provide unrivalled coverage of over 120 science entities. Our ontologies cover many more topics and at greater depth than those that are publicly available, and with SciBite you have the necessary tools including CENtree to extend, merge and manage these ontologies for effective graph building.
Harmonized datasetsThe ability to create knowledge graphs hinges on the ability to integrate data from multiple sources. With SciBite you’re able to clean up unstructured data and align entities to single IDs as captured in our ontologies. Our named entity recognition (NER) engine TERMite can identify entities regardless of the abbreviation, synonym or code used. This means all data can be harmonized and integrated with other sources.
Extraction of semantic triples from textThrough machine learning models, SciBite examines patterns and extracts semantic triples to align these entities to their ontologies. Once aligned, this data can then be fed into knowledge graphs alongside other structured datasets.
Schema generationUsing ‘bridging ontologies’ within CENtree, our ontology management platform, SciBite brings together data from both structured and unstructured sources to create a high-level metagraph of all the relevant entities. CENtree makes it simple to export schema to other systems to suit your use case. So whatever initiative you’re working on, SciBite can help you identify the best format for your project.
Find out more about how our knowledge graph facilitation is completely agnostic, meaning it can work alongside any technology platform you wish to use to represent or store your graph.
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