topic maps applied to pubmed

Topic Maps applied to PubMed, Extreme’07, August 2007 1

Topic Maps applied to Topic Maps applied to PubMedPubMed

Giovani Rubert Librelotto Mirkos Martins

Henrique MachadoFranciscan University

UNIFRABrazil

José Carlos RamalhoPedro Gabriel FerreiraPedro R. Henriques

Department of InformaticsUniversity of Minho

Portugal


Outline

• Motivation

• PubMed

• Topic Maps

• Metamorphosis

• PubMed knowledge representation

• Conclusion


Motivation

• Write down what you want to find!

• Underline the key concepts

• Consider how best to combine and refine the concepts


Identify Key Concepts

Is it risky for pregnant women to take antidepressants?

Risky – (Synonyms) Harm, adverse effects

Anti-depressants – Name of drug or drug(s)?


PubMed

• PubMed is a free search engine that provides very full coverage of the related biomedical sciences, such as biochemistry and cell biology.

• It also offers access to the MEDLINE database with citations and abstracts of biomedical research articles.


PubMed (2)

• The PubMed core subject is medicine and its related fields.

• It is offered by the United States National Library of Medicine as part of the Entrez information retrieval system.


PubMed (3)

• The inclusion of an article in PubMed does not endorse the article's contents, as other indexes.

• Nevertheless, many PubMed citations contain links to full text articles which are freely available, often in the PubMed Central digital library.


MEDLINE

• MEDLINE database covers over 4.900 journals published around the world primarily from 1966 to the present and is composed of more than 17 millions of citations.


Milestones

• MEDLINE 35 years young– 15.8 million records

• PubMed approximately 10 years old– 17 million records

• MEDLINE database covers over 4.900 journals


PubMed architecture


PubMed web site


Querying PubMed


Query Results Formats

Summary

Basic Bibliographic Citation (default)

Abstract

Includes abstract if available

Brief

First author, first few words, PMID

Citation

Citation, Abstract & Subject Headings


Changing Results Format

• Pull down menu on display bar

• Select desired format

• Change occurs automatically


Information indexed in PubMed

• Subject or journal title

• Title Abbreviation

• NLM ID (NLM'sunique journal identifier)

• ISO abbreviation

• Both the print and electronic International Standard Serial Numbers (pISSN and eISSN).


PubMed data structure - CFG

MedlineCitation ==> PMID, DateCreated, DateCompleted, Article, MedlineJournalInfo, ChemicalList, CitationSubset, MeshHeadingList

Article ==> Journal, ArticleTitle, Pagination, Abstract, Affiliation, AuthorList, Language, PublicationTypeList

Journal ==> ISSN, JournalIssue, TitleJournalIssue ==> Volume, Issue, PubDatePubDate ==> Year, Month, Day, Hour?, Minute?, Second?MedlineJournalInfo ==> Country, MedlineTA, NlmUniqueIDChemicalList ==> Chemical+Chemical ==> RegistryNumber, NameOfSubstanceMeshHeadingList ==> MeshHeading+MeshHeading ==> DescriptorName, QualifierName?AuthorList ==> Author+Author ==> LastName, ForeName, InitialsPublicationTypeList ==> PublicationType+


PubMedXML Schema


Available PubMed Concepts

• Publication Date• Language• Literature Type• Journal

• Author• Gender• Age• Journal Subset


Available PubMed Association

• Author writes article;• Keyword describes article;• Article was published in an year;• Article is published in a journal;• Article is written in a language;• Article refers to chemical substances; • Author publishes in an year;• Author writes paper in a language;• Journal refers to the keywords;


What’s the idea?

1.To create an ontology based on PubMed concepts and associations

2.To use that ontology to present query results.


How?

Structure level

Catalog level

1. Prebuild ontology’s structure level

2. Populate the ontology with query results (occurrences)


Metamorphosis


XSTM editor


Structure level through XSTM

<?xml version="1.0" encoding="ISO-8859-1"?><xstm> <topicTypes> <topicType> <id>Article</id> <instanceOf>Article</instanceOf> <name>Article</name> </topicType> <topicType> <id>Year</id> <instanceOf>Year</instanceOf> <name>Year</name> </topicType> ...

<topics> <topic> <xpath id="PMID” name="Article/ArticleTitle"> //MedlineCitation</xpath> <type>Article</type> <resourceData scope="Abstract"> Article/Abstract </resourceData> <resourceData scope="PMID"> PMID</resourceData> <resourceData scope="Pagination"> Article/Pagination/MedlinePgn</resourceData> </topic>


Topic Maps applied to PubMed


New architecture

Metamorphosis

Extracting the base ontology

Qresults intersection

Semantic Browser


• One of the advantages of this approach is that no new database is created and no redundant data is produced;

• Information is interconnected within a huge knowledge network navigable in any direction.


Future work

• Integration of Topic Maps and MeSH headings

• Minimizing “false hits” and saving time

• Identifying other useful – but frequently overlooked – features of the PubMed database.


MeSH

• Medical Subject Headings (MeSH) is a controlled vocabulary designed by the National Library of Medicine to search MEDLINE and other health sciences databases


MeSH example by Wikipedia


MeSH qualifiers

• In addition to the descriptor hierarchy, MeSH contains a small number of standard qualifiers (also known as subheadings), which can be added to descriptors to narrow down the topic.


MeSH qualifiers example

• For example, "Measles" is a descriptor and "epidemiology" is a qualifier; "Measles/epidemiology" describes the subheading of epidemiological articles about Measles.

• The "epidemiology" qualifier can be added to all other disease descriptors. Not all descriptor/qualifier combinations are allowed since some of them may be meaningless. In all there are 83 different qualifiers


Conclusion

• This paper described the integration of data from PubMed information system using the ontology paradigm, in order to generate an homogeneous view of this resources.

• The proposal uses an environment, called Metamorphosis, for the automatic construction of Topic Maps with data extracted from the various data sources, and a semantic browser to navigate among the information resources.


Questions?

Short announcement:

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topic maps applied to pubmed

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