This is the product roll out presentation at the AFRL on creating a focused knowledge base, search, and retrieval system for the domain of human performance and cognition.

1. An Up-to-date Knowledge Base and Focused Exploration System for Human Performance and Cognition AmitSheth LexisNexis Ohio Eminent Scholar Director, Kno.e.sis Center Wright State University RamakanthKavuluru Postdoctoral Research Scientist Kno.e.sis Center Thanks to Dr. Victor Chan for support and guidance HPC-KB team: Christopher Thomas, Wenbo Wang, Alan Smith, Paul Fultz, and Delroy Cameron

2. Focused Knowledge Bases A knowledge base (KB) functions as a standalone reference for a particular domain of interest backend for knowledge-based search, browsing, and exploration of literature

3. What is a KB? “A body of knowledge describing a topic or domain of interest” categories or classes – Neurotransmitters, Disease individuals (instances of classes) Dopamine, Magnesium, Migraine roles (properties/predicates) – inhibits, is a, part of assertions (triples) Dopamine is a neurotransmitter Magnesium treats Migraine

4. Then, What are Ontologies? “Ontology is the basic structure or armature around which a knowledge base can be built” (Swartout and Tate, 1999) “An ontology is an explicit representation of a shared understanding of the important concepts in some domain of interest.” (Kalfoglou, 2001) So, mostly static blocks of well accepted and consensual knowledge

5. Ontologies in Life Sciences The National Center for Biomedical Ontology (NCBO) - Open Biomedical Ontologies (OBO) About 200 ontologies and 1.5 million terms Only part_of and is_a relations in the Gene Ontology Histolysis is_a positive regulation of cell size Requests for changes are expert reviewed before modifications

6. What about Emergent Knowledge, Richer Relationships? New scientific results and insights published everyday backed by experimentation PubMed: 18+ million articles; 1300 new per day Also, what about other predicates besides is_a and part_of (eg., UMLS Semantic Network of 54 predicates). Need a way of capturing and meaningfully utilizing this emerging knowledge

7. Enter HPC-KB NLP Patterns SCOONER

8. Steps in Creating the HPC-KB Carve a focused domain hierarchy out of Wikipedia Extract mentions of entities and relationships in the relevant scientific literature (Pubmed abstracts) to support non-hierarchical guidance. Map extracted entity mentions to concepts and extracted predicates to relationships to create the knowledge-base

9. Workflow Overview HPC keywords Doozer: Base Hierarchy from Wikipedia Focused Pattern based extraction SenseLab Neuroscience Ontologies Initial KB Creation Meta Knowledgebase PubMed Abstracts Knoesis: Parsing based NLP Triples Enrich Knowledge Base NLM: Rule based BKR Triples Final Knowledge Base

10. Hierarchy Using Wikipedia Categories and Graph Structure

11. Triple Extraction Open Extraction No fixed number of predetermined entities and predicates At Knoesis – NLP (parsing and dependency trees) Supervised Extraction Predetermined set of entities and predicates At Knoesis – Pattern based extraction to connect entities in the base hierarchy using statistical techniques At NLM – NLP and rule based approaches

12. Mapping of Triples to Hierarchy Entities in both subject and object must contain at least one concept from the hierarchy to be mapped to the KB Preliminary synonyms based on anchor labels and page redirects in Wikipedia Prolactostatin redirects to Dopamine Predicates (verbs) and entities are subjected to stemming using Wordnet

15. Full Architecture

16. Scooner Features Knowledge-based browsing: Relations window, inverse relations, creating trails Persistent projects: Work bench, browsing history, comments, filtering Collaboration: comments, dashboard, exporting (sub)projects, importing projects

17. Comments on Scooner “The ability to browse predications together with documents will likely reduce the cognitive load required for encountering interesting facts, both for novice users and domain experts.” – Thomas Rindflesch, Researcher in Biomedical Informatics, NLM. “Being able to keep track of multiple articles is a really nice tool to have, and it cuts down on time between jumping back and forth between articles” - Anonymous comment from an AFRL researcher

18. Future Work: Knexpace Automatic Updates Index new abstracts as they arrive Extract relationships as new abstracts arrive Periodically update indices for abstracts and triples Other Maintenance Admin interfaces Application software support

19. Improving the KB Quality & Filtering Adhere to a stricter schema Having a fixed number of predicates and a fixed range and domain for each predicate Ex: For the immunology and chemical warfare agents Predicate: activates Restricted Domain: Cytokines OR μRNA Restricted Range: Macrophages Also useful to directly launch queries of the form Question: ?x activates Macrophages Answer: TNF-α and IFN-γ

20. Normalize Entities and Predicates How do we find Bovine spongiform encephalopathy and mad cow disease are same (Use UMLS Metathesaurus! ) long term memory and long lasting memory are the same computationally (UMLS does not work) More complex similarities: HepG(2) cell line and Human Hepatoma G2 cells which textual forms map to the fixed set of predicates: Do modulates, regulates, stimulates all map to affects?(NLM expert collaboration & AFRL help) NLM tools: MetaMap, SemRep, and other lexical tools

21. Provenance and other meta data Original abstract PMIDs will be captured for each triple. Other data: authors, journal names How about other meta data for filtering: In Vitro / In Vivo If, In Vivo, which organism. If Mice, which type: Ames Dwarf Which techniques are used. ex: Flow Cytometry

22. New Knowledge Example VIP Peptide – increases – Catecholamine Biosynthesis Catecholamines – induce – β-adrenergic receptor activity β-adrenergic receptors – are involved – fear conditioning VIP Peptide – affects – fear conditioning In Cattle In Rats In Humans

23. Domain Specific Provenance For Immunology and Warfare Agent effects Which pathogen: Francisellatularensis Which Strains: U112, LVS, … What is measured: Cells (dendritic, macrophages, monocytes), Proteins, cytokines, chemokines Need to find preexisting taxonomies for organisms, techniques, pathogens; or need to build some and integrate The more the specificity, the more the KB quality

24. Better Ranking of Abstracts and Triples Use search phrases to fine tune ranking of abstracts and triples Just because the user clicked on neurogenesis, does not mean she wants to know everything about it. Predict which triples or abstracts the user might be more interested in the current search session. Show top-k entities in the result set to facilitate filtering based on top concepts Visualize networks of a set of browsed triples

25. Semantic Integration Famous OBO Ontologies (total 7 foundry ontologies) Gene Ontology PRotein Ontology Other domain specific OBO candidateontologies NCBI organismal classification Infectious Disease Human Disease (Tularemia found here, and also in SNOMED, & others too, what to choose for mapping?) Pathogen transmission

26. Semantic Integration, contd… Linked Open Drug Data DrugBank: drugs and drug targets (pathways, structures, sequences) Diseasome: disorders and disease-gene associations LinkedCT: Clinical trials Mendelian Inheritance Online MendelianInhertance in Animals (OMIA) Online Mendelian Inheritance in Man (OMIM) Different formats: owl, obo, rrf

27. Tools, Vocabularies, Ontologies UMLS