2008 UnsupervisedLearningOfSemRelsForMolBioOnts

Jump to navigation Jump to search

Subject Headings: Semantic Relation Recognition Task, Unsupervised Learning Algorithm.


Cited By



  • Manual ontology building in the biomedical domain is a work-intensive task requiring the participation of both domain and knowledge representation experts. The representation of biomedical knowledge has been found of great use for biomedical text mining and integration of biomedical data. In this chapter we present an unsupervised method for learning arbitrary semantic relations between ontological concepts in the molecular biology domain. The method uses the GENIA corpus and ontology to learn relations between annotated named-entities by means of several standard natural language processing techniques. An in-depth analysis of the output evaluates the accuracy of the model and its potentials for text mining and ontology building applications. The proposed learning method does not require domain-specific optimization or tuning and can be straightforwardly applied to arbitrary domains, provided the basic processing components exist.

2. Problem statement and related work

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2008 UnsupervisedLearningOfSemRelsForMolBioOntsMassimiliano Ciaramita
Aldo Gangemi
Esther Ratsch
Jasmin Šarić
Isabel Rojas
Unsupervised Learning of Semantic Relations for Molecular Biology Ontologieshttp://wtlab.um.ac.ir/parameters/wtlab/filemanager/resources/Ontology Learning/ONTOLOGY LEARNING AND POPULATION BRIDGING THE GAP BETWEEN TEXT AND KNOWLEDGE.pdf#page=107