Overview of Text Mining Expertise @ SCD

Text Mining @ SCD

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

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

Problem setting

Given a set of documents,

compute a representation, called index

to retrieve, summarize, classify or cluster them

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Text Mining @ SCD

Problem setting - 2

InformationRetrieval

InformationExtraction

Full NLP parsing

Shallow Statistics

GenericProblemspecific

Domain-specific

Shallow Parsing

Document analysis &Extraction of tokens

Text mining goals

Text mining methodology

Overall approach

Text Mining @ SCD

Overview

Bio-informatics Knowledge management Bibliometrics & scientometrics

Text Mining @ SCD

Overview

Bio-informatics Knowledge management Bibliometrics & scientometrics

Text Mining @ SCD

Document-centered mining

Given a set of documents,

compute a representation, called index

to retrieve, summarize, classify or cluster them

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Text Mining @ SCD

Gene-centered mining

Given a set of genes (and their literature),

compute a representation, called gene index

to retrieve, summarize, classify or cluster them

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Text Mining @ SCD

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

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Text Mining @ SCD

Functional genomics : gene profiling

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)

vocabulary

T 1

T 3

T 2

gene

Bert Coessens

Text Mining @ SCD

Functional Genomics - TXTGate

Distance matrix &Clustering

Other vocabulary

Bert Coessens; Steven Van Vooren

Text Mining @ SCD

Functional genomics – Networks from literature

gene networks

term networks

Bert Coessens; Frizo Janssens

Text Mining @ SCD

Human genetics

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

Steven Van Vooren

Data Analysis Text AnalysisNLP; Ontologies

Text Mining @ SCD

Human genetics

Knowledge discovery for genomic annotation• From µA-CGH profiles• From Biomedical text

Similarity measures for biomedical text what: patient records, literature, genes, loci, cloneswhy: 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 ~ .

Steven Van Vooren

Text Mining @ SCD

Overview

Bio-informatics

Knowledge management Bibliometrics & scientometrics

Text Mining @ SCD

McKnow Project

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.

Dries Van Dromme; Frizo Janssens

Automated and User-oriented Methods and algorithms for knowledge management

Collaboration with Center for Industrial Management, KUL

Text Mining @ SCD

Case studies knowledge management Dimensionality of clustered text-mining cases:

sista papers• electronically available publications (ps, pdf) – full text• 1024 x 49.237

De Standaard• 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

Dries Van Dromme

Text Mining @ SCD

Overview

Bio-informatics Knowledge management

Bibliometrics & scientometrics

Text Mining @ SCD

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

Projects

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

Dries Van Dromme; Frizo Janssens

Text Mining @ SCD

Software

Preprocessing &Indexing Lucene & TextPack

Search engine and webservices TXTGate and McKnow

Text Mining @ SCD

Publicationstargetted 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

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