Using Domain Ontologies to Improve Information Retrieval in Scientific Publications Engineering Informatics Lab at Stanford

Data 3/29/2012 Engineering Informatics Lab at Stanford University 2

TREC Genomics 2007 Data Set Over 162,000 full-text scientific publications from 49 prominent journals in biomedicine Metadata available through MEDLINE Tasks involve passage, document, and feature retrieval Methodologies are evaluated on their response to 36 topics (‘queries’) The topics are categorized based on 13 entity types (Proteins, Genes, etc.) 3/29/2012 Engineering Informatics Lab at Stanford University 3

BioPortal BioPortal is an integrated resource for biomedical ontologies Currently indexes over 300 ontologies including Medical Subject Headings and Gene Ontology Provides a comprehensive web service, abstracting the formats and API’s of all underlying ontologies 3/29/2012 Engineering Informatics Lab at Stanford University 4

Methodology 3/29/2012 Engineering Informatics Lab at Stanford University 5

How is Domain Knowledge Integrated (1)Annotating Documents prior to indexing – Response time is fast – Not flexible, the entire index has to be updated if a new ontology needs to be added – Indexes can grow very large (2) Query Expansion – Response time is slower – Very flexible, ontologies can be dynamically chosen 3/29/2012 Engineering Informatics Lab at Stanford University 6

Query Expansion TREC Queries are first manually pre-processed “What [TUMOR TYPES] are found in zebrafish?” => “[Tumor][MeSH] AND zebrafish” [Tumor] indicates term that has to be expanded [MeSH] indicates ontology that should be used 3/29/2012 Engineering Informatics Lab at Stanford University 7

Query Expansion The pre-processed query is automatically expanded using BioPortal’s API [Tumor][MeSH] => {Tumor, Neoplasm, Carcinoma, Leukemia …} Tumor Leukemia Melanoma Adenocarcinoma Nerve Sheath Neo Synonyms Cancer, Neoplasm, … Synonyms Leucocythaemias Leucocythemia MeSH 3/29/2012 Engineering Informatics Lab at Stanford University 8

Which Domain Knowledge is Integrated The use of synonymy results in inconsistent performance (2007 TREC genomics track) Common reasons include: – Relevant terms may not be classified as expected – Some relevant terms may not be classified in a particular ontology – Incomplete information (such as synonyms) Selection of the appropriate domain ontology is important 3/29/2012 Engineering Informatics Lab at Stanford University 9

Enriching Existing Ontologies Existing ontologies must be enriched to complete missing information Multiple ontologies can be used to provide different classifications 3/29/2012 Engineering Informatics Lab at Stanford University 10 MeSH NCI OntologyNDF ConceptPamidronate Synonyms from NDFAPD, Amidronate,... Synonyms from MeSH pamidronate calcium, pamidronate monosodium, aredia Synonyms from NCIPamidronic acid, pamidronate disodium, …

Evaluations Baseline With Query Expansion (Suggested Sources) Using Enriched Ontologies Multiple Query Expansions per query 3/29/2012 Engineering Informatics Lab at Stanford University 11 Summary of 2007 TREC genomics track Max Min Mean Median0.1897

Queries Topic Number QueryDomain Knowledge 205What [SIGNS OR SYMPTOMS] of anxiety disorder are related to coronary artery disease? Symptom Ontology 206What [TOXICITIES] are associated with zoledronic acid? NCI Thesaurus 207What [TOXICITIES] are associated with etidronate?NCI Thesaurus 211What [ANTIBODIES] have been used to detect protein PSD-95? MeSH 229What [SIGNS OR SYMPTOMS] are caused by human parvovirus infection? Symptom Ontology 231What [TUMOR TYPES] are found in zebrafish?MeSH 3/29/2012 Engineering Informatics Lab at Stanford University 12

Baseline Queries are used without modification, e.g., – “What [ANTIBODIES] have been used to detect protein PSD-95?” – “What [SIGNS OR SYMPTOMS] of anxiety disorder are related to coronary artery disease?” Document MAP: /29/2012 Engineering Informatics Lab at Stanford University 13

Query Expansion Queries are formulated in ‘AND’ clauses: “[Tumor][MeSH] AND zebrafish” => (Tumor, Neoplasm, Carcinoma, Leukemia …) AND zebrafish Document MAP: /29/2012 Engineering Informatics Lab at Stanford University 14

Multiple Query Expansion Terms Expansion can be performed on multiple terms in the query Example: Coronary Artery Disease => {Coronary heart disease, coronary disease, CAD, …} [Tumor][MeSH] AND zebrafish[MeSH} => (tumor, neoplasm, …) AND (zebrafish, danio rerio, …) Document MAP: /29/2012 Engineering Informatics Lab at Stanford University 15

Enriched Ontology Marginal improvement over basic enhanced models Document MAP: Why is the improvement only marginal? – Framework for enrichment based on synonymy is rigid, i.e., relevant terms that are entirely missing in the ontology are still not included – Relevant terms that are classified differently are never included in the search 3/29/2012 Engineering Informatics Lab at Stanford University 16

Visualization Expert knowledge is valuable We extend MINOE, a co-occurrence based visualization tool, originally designed for exploring marine ecosystems User can browse (or search) documents through ontologies and visualize interactions between concepts SEE DEMO 3/29/2012 Engineering Informatics Lab at Stanford University 17

Summary Search methodologies must be based on semantics in order to tackle terminology inconsistency Domain ontologies provide these semantics Domain ontologies need to be modified (or enriched) in order to fulfill information needs User interaction is important 3/29/2012 Engineering Informatics Lab at Stanford University 18

Future Work Using multiple enriched ontologies may provide the necessary terms MeSH Descriptors are provided for every publication during indexing and can potentially improve results Implement Okapi model for scoring documents 3/29/2012 Engineering Informatics Lab at Stanford University 19

Backup Slides 3/29/2012 Engineering Informatics Lab at Stanford University 20

Motivation Scientific literature is an important source of information Retrieving relevant information from scientific publications is challenging Domain terminology is used inconsistently in scientific publications Increasing amounts of information amplify the problem Improved methodologies based on semantics are required 3/29/2012 Engineering Informatics Lab at Stanford University 21

Background Text REtrieval Conference (TREC) organized by NIST has showcased many successful methods The Genomics track focused on full-text scientific publications from 49 prominent journals Methodologies involved: – Use of Synonymy from ontologies – Language based models – Query expansion and annotations – Okapi scoring model 3/29/2012 Engineering Informatics Lab at Stanford University 22

Goals Understand how domain ontologies can be leveraged Understand which domain ontologies can be leveraged Develop a knowledge-based approach to integrate domain knowledge with search mechanism 3/29/2012 Engineering Informatics Lab at Stanford University 23