Metalife Semantic Search based on artificial intelligence (AI) methods engine is a semantic system that integrates data from multiple databases: UniProt, PhenomicDB, GenBank, Entrez Gene, GO, PDB, NCBI Taxonomy, Pfam, Enzyme, and RefSeq. It allows searching for connections and relationships between biological objects from different levels of organization of living matter by using an extended biologically oriented ontology.
It is based on the standard representation of the semantic data shaped according to unified bio ontology which incorporates in organized manner all biological entities gathered from the primary sources mentioned above. The key component of the Metalife Semantic Search based on AI methods system is the inferencing of new data interrelations to generate conclusions
Inference based on rule implementation acts as knowledge generator meaning that new relationships are established based on an a priori defined biological rules. Relations generated by one rule are automatically subjected as an input of another rule-based iteration.
The semantic data organization, the ontology and the inference machinery all together provides a new approach to the users' search activities. One does not obtain a set of different entries from distinct databases but could examine a semantic network which brings and visualizes together related biological entities from different sources. This facilitates and makes the search more efficient, as well as reveals new relations through the inference.
Metalife Semantic Search based on AI methods provides a holistic and integrative approach to answer biologically relevant questions.
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- collecting words and sentences.
- generating RDF rules from sentences.
- applying RDF rules to sentences and words.
- inference machine derives conclusions.