Abstract

The discovery of new and potentially meaningful relationships between named entities in biomedical literature can take great advantage from the application of multi-relational data mining approaches in text mining. This is motivated by the peculiarity of multi-relational data mining to be able to express and manipulate relationships between entities. We investigate the application of such an approach to address the task of identifying informative syntactic structures, which are frequent in biomedical abstract corpora. Initially, named entities are annotated in text corpora according to some biomedical dictionary (e.g. MeSH taxonomy). Tagged entities are then integrated in syntactic structures with the role of subject and/or object of the corresponding verb. These structures are represented in a first-order language. Multi-relational approach to frequent pattern discovery allows to identify the verb-based relationships between the named entities which frequently occur in the corpora. Preliminary experiments with a collection of abstracts obtained by querying Medline on a specific disease are reported.

Autore Pugliese

• Appice Annalisa , Loglisci Corrado , Ceci Michelangelo

Tutti gli autori

• APPICE A.;LOGLISCI C.;CECI M.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2010

ISSN

Non Disponibile

ISBN

978-076953981-2

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

2

Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

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