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Faculty of Computer Science

2006CMPUT 605 March 31, 2013

Towards Applying Text Mining and Natural

Language Processing for Biomedical

Ontology Acquisition

Inniss T., Light M., Thomas G., Lee J., Grassi M., Williams A.

TMBIO(2006)

John G

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Focus

Ontology for describing age-related macular degeneration

(AMD)

Comparison of the accuracy of three methods for Ontology

Natural Language Processing (NLP)Text Mining (SAS Text Miner)Human Expert

Manual and adhoc knowledge acquisition

IDOCS (Intelligent Distributed Ontology Consensus System)

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Introduction

No existing common and standardized vocabularyfor classification of disease types for certain eye-diseases

Clinicians, dispersed geographically, may usedifferent terms to describe the same condition

Research aimed at extracting the feature and

attribute descriptions for the vocabulary of AMD,

and build an Ontology from that.

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Related Work

Lot of research done, since 1990s, for applying

NLP techniques in medicine, bio-medicine etc.

NLP & Text Data Mining have been recognized to

play an important role in this endeavor

Research focused on online repositories such as

Medline & PubMed

NLP systems developed: MedLee, UMLS, GENIES

etc.

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IDOCS

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Methodology

Four clinical experts in retinal diseases enlisted to

view 100 eye sample images of AMD

Experts in different geographic locations

Described the observations using digital voice

recordersno artificially imposed vocabulary

constraints

Another retinal expert for manual parsing of the

transcribed textextracting key words,

organization of key-words into categories etc.

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Methodology: NLP

NLP: Used for information extraction and automatic

summarization.

Identify short sequences of words having meaning

over and above a meaning composed directly fromtheir partsextreme programming

Ngram Statistics Package (NSP) used for

collocation discovery in case of bi-grams

Word-pair associations measured by PMI

8/12/2019 Data Mining: Ontologies

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Department of Computing Science

CMPUT 605

Methodology: NLP

Large PMI for larger degree of association between

the words

s

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Department of Computing Science

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Methodology:Text Mining (SAS Text Miner)

Collection of documents (corpus) used as input to

any text mining algorithm

Corpus broken into tokens or terms (tokens in a

particular language)

Term weighting Measures: Entropy, Inverse

Document Frequency (IDF), Global Frequency (GF) -

IDF, None (Global weight of 1) & Normal term wt.

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Department of Computing Science

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Results: Human Experts

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Department of Computing Science

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Results: NLP

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Results: Text Miner

Frequency wt. None

Term wt. Normal

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Department of Computing Science

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Comparison

sss

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Comparison

Thus text mining is a viable and effective method for

determining vocabulary to describe a particular disease

Text Mining found a lot of terms that NLP found

Human Expert is the best Ground Truth

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Ontology Generation

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Department of Computing Science

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Conclusion and Future Work

Human experts are the best, but they did miss

some key descriptors

Text Mining and NLP can enhance the generation of

feature generations, by preventing the above case

As a consequence more robust vocabulary can be

generated

Extensionevaluate the effectiveness of the

automated tools, text mining & NLP

Different weighting schemes will be tried in the

future

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Thank You For Your Attention!