ERIC Number: ED558457
Record Type: Non-Journal
Publication Date: 2013-Apr
Abstractor: As Provided
Reference Count: 27
Unsupervised Ontology Generation from Unstructured Text. CRESST Report 827
Mousavi, Hamid; Kerr, Deirdre; Iseli, Markus R.
National Center for Research on Evaluation, Standards, and Student Testing (CRESST)
Ontologies are a vital component of most knowledge acquisition systems, and recently there has been a huge demand for generating ontologies automatically since manual or supervised techniques are not scalable. In this paper, we introduce "OntoMiner", a rule-based, iterative method to extract and populate ontologies from unstructured or free text. OntoMiner transforms text into a graph structure called a "textGraph" in which nodes are candidate terms and words from the text and edges are grammatical, semantic, and categorical relations between nodes. OntoMiner iteratively uses graph pattern rules over the textGraphs to mine ontological information and at the end of each iteration, based on the newly found information, OntoMiner improves the existing ontology. Our preliminary experiments indicate that OntoMiner achieves up to 93.4% accuracy, which to our knowledge exceeds the accuracy levels of previous work.
National Center for Research on Evaluation, Standards, and Student Testing (CRESST). 300 Charles E Young Drive N, GSE&IS Building 3rd Floor, Mailbox 951522, Los Angeles, CA 90095-1522. Tel: 310-206-1532; Fax: 310-825-3883; Web site: http://www.cresst.org
Publication Type: Reports - Research
Education Level: N/A
Sponsor: Bill and Melinda Gates Foundation
Authoring Institution: National Center for Research on Evaluation, Standards, and Student Testing
Grant or Contract Numbers: OPP1003019