ML Anthology

Unsupervised Learning of Semantic Relations Between Concepts of a Molecular Biology Ontology

Massimiliano Ciaramita, Aldo Gangemi, Esther Ratsch, Jasmin Saric, Isabel Rojas
IJCAI 2005 pp. 659-664
/ijcai/2005/ciaramita2005ijcai-unsupervised/

Abstract

In this paper we present an unsupervised model for learning arbitrary relations between concepts of a molecular biology ontology for the purpose of supporting text mining and manual ontology building. Relations between named-entities are learned from the GENIA corpus by means of several standard natural language processing techniques. An in-depth analysis of the output of the system shows that the model is accurate and has good potentials for text mining and ontology building applications.

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Text

Ciaramita et al. "Unsupervised Learning of Semantic Relations Between Concepts of a Molecular Biology Ontology." International Joint Conference on Artificial Intelligence, 2005.

Markdown

[Ciaramita et al. "Unsupervised Learning of Semantic Relations Between Concepts of a Molecular Biology Ontology." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/ciaramita2005ijcai-unsupervised/)

BibTeX

@inproceedings{ciaramita2005ijcai-unsupervised,
  title     = {{Unsupervised Learning of Semantic Relations Between Concepts of a Molecular Biology Ontology}},
  author    = {Ciaramita, Massimiliano and Gangemi, Aldo and Ratsch, Esther and Saric, Jasmin and Rojas, Isabel},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2005},
  pages     = {659-664},
  url       = {https://mlanthology.org/ijcai/2005/ciaramita2005ijcai-unsupervised/}
}