MELISA An ontology-based agent for information retrieval in medicine Jose Maria Abásolo & Mario Gómez Institut d´Investigaciò en Intel.ligència Artificial (IIIA) Spanish Scientific Research Council (CSIC)

Index Motivation Overview MELISA process –Query Generation –Query Evaluation, Filter & Combination Results Conclusions Future Work

Motivation Nowadays Internet gives us a great quantity of information Most users find difficult to formulate well-designed queries for retrieval purposes Usually a user makes a first query and then he has to reformulate the query (one or more times) to get useful information This project try to solve this problem within a professional domain (biomedical literature)

Overview 1. Input Interface 2. Query Generation 3. Query Evaluation 4. Filter & Combination 5. Output Interface 6. Query Models 7. Medical Ontology 8. PubMed (Medline) 9. MeSH Browser (Medline)

Medical Ontology

GUIDELINES is-an-instance-of EVIDENCE_INTEGRATION Name: Guidelines MeSH_Terms: Guidelines, “Practice Guidelines”, “Clinical Protocol” Publication_Type: guideline, “practice guideline” Related_MeSH_Terms: “Guideline Adherence”

Query Model ConsultationConceptual queriesSpecific queries Very abstract, is given by the user Link the consultation to the ontology Queries valid for some data source

Generation of queries

Pneumonia &Ofloxacin Decomposition Level 1 Good EvidenceTherapy Guidelines EBMCost Analysis Decomposition Level 2 Specific Query1Specific Query2Specific Query nSpecific Query3 SQ1 : pneumonia * ofloxacin AND guidelines [MAJR] SQ2 : pneumonia * ofloxacin AND guidelines [MH:NOEXP] SQ3 : pneumonia * ofloxacin AND guidelines [MH] …..

Query evaluation & combination Scoring documents inside a Conceptual Query Combine documents from different conceptual queries

Scoring documents inside a Conceptual Query LIST UID CONCEPTUAL QUERY LIST SCORED UID Weighted Sum SPECIFIC QUERY SPECIFIC QUERY LIST UID SPECIFIC QUERY LIST UID SPECIFIC QUERY LIST UID SPECIFIC QUERY LIST UID

Combine documents from different Conceptual Queries List of Documents Categories To Combine

Combine documents from different Conceptual Queries (II) LIST SCORED UID LIST OF DOCUMENTS Aggregation Function CONCEPTUAL QUERY LIST SCORED UID CONCEPTUAL QUERY LIST SCORED UID CONCEPTUAL QUERY LIST SCORED UID CONCEPTUAL QUERY LIST SCORED UID CONCEPTUAL QUERY

Results Comparison between MELISA and a human user working with PubMed 5 queries (evaluating best 40 documents for any query) For example: –Human user query “Osteoporosis AND Women AND (Therapy OR Guideline OR Cost) “ –MELISA Keywords: Osteoporosis, Women Selected categories: Therapy, Guideline, Cost analysis

Results (II)

Conclusions The system is able to integrate a big amount of information and show the results in a dynamic way The use of the ontology has two main benefits: –Helps user to make a consultation –Allow to use synonymous and related terms Our architecture seems to be a good approach to solve the problem of domain and source independence, but it needs to be improved A great problem is the combination of results from different categories The first empirical test shows that the system improves the traditional retrieve using PubMed

Future work To develop user profiles To work with multiple information sources To study and compare different evaluation functions To study more complex criteria to reformulate the specific queries To develop algorithms for learning the weight coefficients To apply the system in other domains To study other query (reformulation) operators (generalization, specification, source selection)