Using ART2 Neural Network and Bayesian Network for Automating the Ontology Constructing Process
| Autor: | Gholam Ali Montazer, Maryam Hourali |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Ontology Inference Layer
Information retrieval Ontology learning Ontology ART Neural Network business.industry Computer science Ontology-based data integration Process ontology Suggested Upper Merged Ontology General Medicine Lexico-Syntactic Patterns Ontology (information science) Term Frequency–Inverse Document Frequency (TF-IDF) computer.software_genre Bayesian network C-value Method Upper ontology Artificial intelligence business Ontology alignment computer Engineering(all) Natural language processing |
| Zdroj: | Procedia Engineering. 29:3914-3923 |
| ISSN: | 1877-7058 |
| DOI: | 10.1016/j.proeng.2012.01.594 |
| Popis: | Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semiautomatically, can overcome the bottleneck of ontology acquisition in ontology development.. In this article a novel automated method for ontology learning is proposed. First, domain-related documents were collected. Secondly, the C-value method was implemented for extracting meaningful terms from documents. Then, an ART neural network was used to cluster documents, and terms’ weight was calculated by TF–IDF method in order to find candidate keyword for each cluster. Next, the Bayesian network and lexico-syntactic patterns were applied to construct the initial ontology. Finally, the proposed ontology was evaluated by expert's views and using the ontology for query expansion purpose. The primary results show that the proposed ontology learning method has higher precision than similar studies. |
| Databáze: | OpenAIRE |
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