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Using Domain Ontologies to Improve Information Retrieval in Scientific Publications. Kincho H. Law, Siddharth Taduri, Gloria T. Lau Engineering Informatics Lab at Stanford University. Motivation. PMID: 12897095

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Using Domain Ontologies to Improve Information Retrieval in Scientific Publications


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    1. Using Domain Ontologies to Improve Information Retrieval in Scientific Publications Kincho H. Law, Siddharth Taduri, Gloria T. Lau Engineering Informatics Lab at Stanford University

    2. Motivation PMID: 12897095 Regional variability in the incidence of end-stage renal disease: an epidemiological approach. …. Regional variability in the incidence of end-stage renal disease (ESRD)in Austria is reported. Our aim was …. low rates in the state of Tyrol. …. ESRD incidence data were obtained from …. …. Between 1995 and 1999, 4811 new cases of ESRD were recorded; the state of Tyrol (T) …. incidence of ESRD patients with type 2 diabetes mellitus…. the difference in the overall ESRD incidence …. prevalence of DM, a highly significant correlation was found between ESRD incidence and DM. …. variability in the ESRD incidence in Austria is explained mainly by regional differences in DM-2. Data from similar studies …. allocation for ESRD…. …. Engineering Informatics Lab at Stanford University

    3. Data Set and Knowledge TREC 2007 Genomics 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.) Domain Knowledge • Over 250 biomedical ontologies from BioPortal Engineering Informatics Lab at Stanford University

    4. XML Representation of Scientific Publications in PubMed <PubmedArticle> <MedlineCitation Owner="NLM" Status="MEDLINE"> <PMID>10022466</PMID> <DateCreated> <Year>1999</Year> <Month>02</Month> <Day>25</Day> </DateCreated> …. <Article PubModel="Print"> <Journal> …. <JournalIssueCitedMedium="Print"> <Volume>84</Volume> <Issue>2</Issue> …. </JournalIssue> <Title>The Journal of clinical endocrinology and metabolism</Title> <ISOAbbreviation>J. Clin. Endocrinol. Metab.</ISOAbbreviation> </Journal> <ArticleTitle>About the use … of an ACTH 1-39 ….</ArticleTitle> …. Engineering Informatics Lab at Stanford University

    5. Domain Knowledge Integration • 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 Engineering Informatics Lab at Stanford University

    6. Query Expansion Tumor MeSH • The pre-processed query is automatically expanded using BioPortal’s API [Tumor][MeSH] => {Tumor, Neoplasm, Carcinoma, Leukemia …} Melanoma Adenocarcinoma Leukemia Nerve Sheath Neo Engineering Informatics Lab at Stanford University

    7. Choosing Domain Knowledge • 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 Engineering Informatics Lab at Stanford University

    8. Enriching Existing Ontologies • Existing ontologies can be enriched to complete some missing information • Multiple ontologies can be used to provide different classifications MeSH NCI Engineering Informatics Lab at Stanford University

    9. Evaluations • Baseline • With Query Expansion (Suggested Sources) • Using Enriched Ontologies • Multiple Query Expansions per query Engineering Informatics Lab at Stanford University

    10. Queries Engineering Informatics Lab at Stanford University

    11. 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: 0.277 Engineering Informatics Lab at Stanford University

    12. Query Expansion • Original Query: What [TUMOR TYPES] are found in zebrafish? • Queries are formulated in ‘AND’ clauses: “[Tumor][MeSH] AND zebrafish” => (Tumor, Neoplasm, Carcinoma, Leukemia …) AND zebrafish • Document MAP: 0.347 Engineering Informatics Lab at Stanford University

    13. 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, daniorerio, …) • Document MAP: 0.352 Engineering Informatics Lab at Stanford University

    14. Enriched Ontology – Current Status • Marginal improvement over basic enhanced models • Document MAP: 0.352 (Marginal improvement from 0.347) • Issues: • 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 Engineering Informatics Lab at Stanford University

    15. IR Tool • Expert knowledge is valuable • Developed a search tool which automatically integrates with knowledge sources and searches documents • 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 Engineering Informatics Lab at Stanford University

    16. Snapshots of the Tool Engineering Informatics Lab at Stanford University

    17. I. Enter Query Terms II. Domain Knowledge Integration III. Shows Expanded Query, and other filters that are added to the search Engineering Informatics Lab at Stanford University

    18. TREC Topic 220 • Query: What [PROTEINS] are involved in the activation or recognition mechanism for PmrD? • Domain Knowledge: MeSH Engineering Informatics Lab at Stanford University

    19. Engineering Informatics Lab at Stanford University

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    25. Changed Engineering Informatics Lab at Stanford University

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    27. MeSH Descriptors Engineering Informatics Lab at Stanford University

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    31. Shows Association Between Concepts (>1500 Documents) (>1500 Documents) Engineering Informatics Lab at Stanford University

    32. Stronger Association: ~270 Documents Weaker Association: ~57 Documents CHILD CONCEPTS Engineering Informatics Lab at Stanford University

    33. Retrieving Information Across Multiple Diverse Information Sources Patent System • Technology Firms’ Concerns • Can I get patent protection for my innovation? • Do I build or do I buy related technologies? • What are my competitors doing? • How strong are their patents? • Am I perhaps infringing on someone else’s patents? • Is so, are those patents valid? • Have they been enforced in court? • Has their validity been challenged in court? Issued Patents and Applications File Wrappers Court Cases Technical Publications Regulations and Laws Engineering Informatics Lab at Stanford University

    34. Cross-Referencing between Information Sources COURT CASE 314 F.3d 1313 (2003) AMGEN INC., Plaintiff-Cross Appellant v. HOECHST MARION ROUSSEL, INC. (now known as Aventis Pharmaceuticals, Inc.) and Transkaryotic Therapies, Inc., Defendants-Appellants. … Plaintiff-Cross Appellant Amgen Inc. is the owner of numerous patents directed to the production of erythropoietin ("EPO"), …alleging that TKT's Investigational New Drug Application ("INDA") infringed United States Patent Nos. 5,547,933; 5,618,698; and 5,621,080.The complaint was amended in October 1999 to include United States Patent Nos. 5,756,349 and 5,955,422, which issued after suit was filed. REGULATIONS: U.S. Code Title 35, C. F. R Title 37, M. P. E. P. … Publication Database FILE WRAPPER U.S. Patent 5,955,422 … Claims 61-63 are rejected under 35 U.S.C. § 103 as being unpatentable over any one of Miyake et al., 1977 (R) … In accordance with the provisions of 37 C.F.R. §1.607, the present continuation is being filed for the purpose of … PATENT United States Patent, 5,955,422 September 21, 1999 Production of erthropoietin Abstract: Disclosed are novel polypeptides possessing part or all of the primary structural conformation and one or more of the biological properties of mammalian erythropoietin ("EPO") … Inventors: Lin; Fu-Kuen (Thousand Oaks, CA) Assignee: Kirin-Amgen, Inc. (Thousand Oaks, CA) Appl. No.: 08/100,197 Filed: August 2, 1993. BIOPORTAL: DOMAIN KNOWLEDGE Solution: Patent System Ontology Engineering Informatics Lab at Stanford University

    35. Patent System Ontology • Facilitate information integration across multiple diverse information sources • This requires a standardized representation (a formal semantic model) - Patent System Ontology Integrate Domain Semantics into existing Information Retrieval and Text mining methodologies to improve retrieval of information Engineering Informatics Lab at Stanford University

    36. Information Retrieval Framework Patent System Ontology Engineering Informatics Lab at Stanford University

    37. 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 Engineering Informatics Lab at Stanford University

    38. Thank You Engineering Informatics Lab at Stanford University

    39. Backup Slides Engineering Informatics Lab at Stanford University

    40. 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 Engineering Informatics Lab at Stanford University

    41. 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 Engineering Informatics Lab at Stanford University

    42. 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 Engineering Informatics Lab at Stanford University

    43. 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 Engineering Informatics Lab at Stanford University

    44. 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 Engineering Informatics Lab at Stanford University

    45. 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 Engineering Informatics Lab at Stanford University