Overview of Text Mining Expertise @ SCD

1. Overview of Text Mining Expertise @ SCD

2. Introduction • Text mining team @ SCD • Started around 2000 • Currenty 1 postdoc, 4 PhD students • Tailored, generic text mining analysis • Diverse application areas • Several collaborations and projects. • Supported by more general SCD expertise in a.o. • Data mining • Numerical linear algebra • Optimization Text Mining @ SCD

3. Strategic mission • To consolidate, deepen and extend SCD’s text mining expertise • By combining statistical approaches and domain-specific information • To support knowledge discovery through literature analysis in various domains: • Bio-informatics • Knowledge management • Mapping of science and technology • Bibliometrics Text Mining @ SCD

4. Problem setting • Given a set of documents, • compute a representation, called index • to retrieve, summarize, classify or cluster them  <1 0 0 1 0 1> <1 1 0 0 0 1> <0 0 0 1 1 0> Text Mining @ SCD

5. Problem setting - 2 • Text mining goals InformationRetrieval Document analysis &Extraction of tokens InformationExtraction • Text mining methodology Shallow Statistics Shallow Parsing Full NLP parsing • Overall approach Domain-specific Problemspecific Generic Text Mining @ SCD

6. Overview • Bio-informatics • Knowledge management • Bibliometrics & scientometrics Text Mining @ SCD

7. Overview • Bio-informatics • Knowledge management • Bibliometrics & scientometrics Text Mining @ SCD

8. Document-centered mining • Given a set of documents, • compute a representation, called index • to retrieve, summarize, classify or cluster them  <1 0 0 1 0 1> <1 1 0 0 0 1> <0 0 0 1 1 0> Text Mining @ SCD

9. Gene-centered mining • Given a set of genes (and their literature), • compute a representation, called gene index • to retrieve, summarize, classify or cluster them  <1 0 0 1 0 1> <1 1 0 0 0 1> <0 0 0 1 1 0> Text Mining @ SCD

10. Patient-centered mining • Given a set of patients (and their records), • compute a representation, called patient index • to retrieve, classify them • ..and/or associate this information to genes  <1 0 0 1 0 1> <1 1 0 0 0 1> <0 0 0 1 1 0> Text Mining @ SCD

11. Functional genomics : gene profiling gene T 3 T 2 T 1 vocabulary Bert Coessens • Profile documents, genes, … using vocabularies (bag of words approach) • Tailored vocabularies reflect the 'knowledge' of a certain domain: + noise reduction (i.e. irrelevant words) + direct link with other knowledge bases (eg. Gene Ontology) Text Mining @ SCD

12. Functional Genomics - TXTGate Distance matrix &Clustering Other vocabulary Bert Coessens; Steven Van Vooren Text Mining @ SCD

13. Functional genomics – Networks from literature Bert Coessens; Frizo Janssens • gene networks • term networks Text Mining @ SCD

14. Human genetics Text AnalysisNLP; Ontologies Data Analysis Steven Van Vooren • Collaboration with Human Genetics Centre @ University Hospital KU Leuven. • Mining on clinical profile and chromosomal footprint of patients (CGH microarrays) • Knowledge discovery for genomic annotation • Aiming at tools and standards for reporting, data entry and visualisation supporting experts in exploring hypotheses in linking phenotypes to genotypes and in inference of novel gene candidates Text Mining @ SCD

15. Human genetics Steven Van Vooren • Knowledge discovery for genomic annotation • From µA-CGH profiles • From Biomedical text • Similarity measures for biomedical text what: patient records, literature, genes, loci, clones why: retrieval, clustering, inference • Clustering similar patients, genes, loci, documents • Finding genes associated by patient records • Extracting entities from text • gene name symbols, loci, diseases, phenotypes, clinical entities, karyotypes • Text summarization • Profiling of patients, genes, loci, clones, clusters of ~ . Text Mining @ SCD

16. Overview • Bio-informatics • Knowledge management • Bibliometrics & scientometrics Text Mining @ SCD

17. McKnow Project Dries Van Dromme; Frizo Janssens • Automated and User-oriented Methods and algorithms for knowledge management • Collaboration with Center for Industrial Management, KUL Clustering and classification are focal points, as well as scalability because of the huge corpora of available data nowadays. We incorporate user profiles, and as such regard both users and documents as points in a high-dimensional vector space. Furthermore, as environments are typically dynamical, care is taken that used methods are easily updatable. Text Mining @ SCD

18. Case studies knowledge management Dries Van Dromme • Dimensionality of clustered text-mining cases: • sista papers • electronically available publications (ps, pdf) – full text • 1024 x 49.237 • DeStandaard • full text newspaper articles, but a lot of them very short • 1776 x 39.363 - but much more data available • kuleuven papers • electronically available papers pertaining to researchers from different departments (pdf, word,...) • 576 x 68.257 ! less documents, broader spectrum • patent abstracts • international patent abstracts and titles • 16.488 x 21.019 ! a lot more doc’s, denser spectrum • PMA papers • full text publications of the K.U.Leuven dept. of Mechanics • 380 x 18.206 • Locuslink “known genes with proteins” • gene documents from MEDLINE abstracts • 12.263 x 58.924 Text Mining @ SCD

19. Overview • Bio-informatics • Knowledge management • Bibliometrics & scientometrics Text Mining @ SCD

20. Scope • Bibliometricsthe application of mathematical and statistical methods to books and other media of communication • Scientometricsthe application of those quantitative methods which are dealing with the analysis of science viewed as an information process • Patent analysis and miningThe analysis of patent information is considered to be one of the best established, directly available and historically reliable methods of quantifying the output of a science and technology system • Collaboration with Steunpunt O&O Statistieken<< to consolidate and to further develop Flanders position as a European innovation intensive region >> Text Mining @ SCD

21. Projects Dries Van Dromme; Frizo Janssens • 1. Domain Analysis • Mapping of Nanotechnology field from USPTO/EPO patents • Text-based clustering ; identification of sub-domains • comparison with IPC (International Patent Classification) • comparison with FTC (Fraunhofer Technology Classification) • 2. Science-Technology mapping • link scientific publications (WoS) and new technologies (patents) • text-based clustering & analysis of citation network structure • Case study: Ljung • 3. Trend Detection • assess trends & emerging fields from “change over time” in structure and characterization of clusters & citation network Text Mining @ SCD

22. Software • Preprocessing &Indexing • Lucene & TextPack • Search engine and webservices • TXTGate and McKnow Text Mining @ SCD

23. Publications targetted submissions by Dec • Bio-informatics (1-2) • (BMC) bioinformatics, special issues,.. (BC) • More biological journals (BC, SVV) • Knowledge management (1) • Scientometrics, SIAM DM, • Bibliometrics & scientometrics (1) • Case study  Bioinformatics, Trends in.. • IEEE transactions, engineering, webmining journals • SIAM DM High, moderate, fair impact Text Mining @ SCD

24. Collaborations • Formalized • GBOU-McKnow • partner CIB olv Joost Duflou (Joris Vertommen, Dries Cleymans) • User Committee (ICMS, Verhaert, LMS, TriSoft, WTCM) • IWT met Joris V (Steven: aanvullen/corrigeren) • Steunpunt O&O Statistieken, INCENTIM • Patent clustering and detection of emerging trends • Informal • M-F Moens (SBO ?) • IBM – Bart VL • Gasthuisberg en Peter M: TXTGate als ‘vak’ • J&J Text Mining @ SCD