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MELISA

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

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MELISA

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

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

  3. 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)

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

  5. Medical Ontology

  6. 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”

  7. Consultation Conceptual queries Specific queries Query Model Queries valid for some data source Very abstract, is given by the user Link the consultation to the ontology

  8. Generation of queries

  9. Pneumonia &Ofloxacin Decomposition Level 1 Good Evidence Therapy EBM Cost Analysis Guidelines Decomposition Level 2 ….. Specific Query1 Specific Query2 Specific Query3 Specific Query n SQ1 : pneumonia * ofloxacin AND guidelines [MAJR] SQ2 : pneumonia * ofloxacin AND guidelines [MH:NOEXP] SQ3 : pneumonia * ofloxacin AND guidelines [MH]

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

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

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

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

  14. 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

  15. Results (II)

  16. 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

  17. 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)

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