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Using pattern structures for analyzing ontology-based annotations of biomedical data

Adrien Coulet 1 Florent Domenach 2 Mehdi Kaytoue 3 Amedeo Napoli 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Annotating data with concepts of an ontology is a common practice in the biomedical domain. Resulting annotations, i.e., data-concept relationships, are useful for data integration whereas the reference ontology can guide the analysis of integrated data. Then the analysis of annotations can provide relevant knowledge units to consider for extracting and understanding possible cor- relations between data. Formal Concept Analysis (FCA) which builds from a binary context a concept lattice can be used for such a knowledge discovery task. However annotated biomedical data are usually not binary and a scaling procedure for using FCA is required as a prepro- cessing, leading to problems of expressivity, ranging from loss of information to the generation of a large num- ber of additional binary attributes. By contrast, pattern structures o er a general FCA-based framework for buil- ding a concept lattice from complex data, e.g., a set of objects with partially ordered descriptions. In this pa- per, we show how to instantiate this general framework when descriptions are ordered by an ontology. We illus- trate our approach with the analysis of annotations of drug related documents, and we show the capabilities of the approach for knowledge discovery.
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Submitted on : Thursday, December 26, 2013 - 2:32:52 PM
Last modification on : Saturday, October 16, 2021 - 11:26:07 AM
Long-term archiving on: : Friday, March 28, 2014 - 5:02:01 PM


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  • HAL Id : hal-00922392, version 1


Adrien Coulet, Florent Domenach, Mehdi Kaytoue, Amedeo Napoli. Using pattern structures for analyzing ontology-based annotations of biomedical data. Septièmes Journées d'Intelligence Artificielle Fondamentale, Jun 2013, Aix-en-Provence, France. pp.97--106. ⟨hal-00922392⟩



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