In the present era of Big Data the demand for developing efficient information processing techniques for different applications is expanding steadily.One such possible application is automatic creation of ontology.Suc...In the present era of Big Data the demand for developing efficient information processing techniques for different applications is expanding steadily.One such possible application is automatic creation of ontology.Such an ontology is often found to be helpful for answering queries for the underlying domain.The present work proposes a scheme for designing an ontology for agriculture domain.The proposed scheme works in two steps.In the first step it uses domain-dependent regular expressions and natural language processing techniques for automatic extraction of vocabulary pertaining to agriculture domain.In the second step semantic relationships between the extracted terms and phrases are identified.A rulebased reasoning algorithm RelExOnt has been proposed for the said task.Human evaluation of the term extraction output yields precision and recall of 75.7%and 60%,respectively.The relation extraction algorithm,RelExOnt performs well with an average precision of 86.89%.展开更多
文摘In the present era of Big Data the demand for developing efficient information processing techniques for different applications is expanding steadily.One such possible application is automatic creation of ontology.Such an ontology is often found to be helpful for answering queries for the underlying domain.The present work proposes a scheme for designing an ontology for agriculture domain.The proposed scheme works in two steps.In the first step it uses domain-dependent regular expressions and natural language processing techniques for automatic extraction of vocabulary pertaining to agriculture domain.In the second step semantic relationships between the extracted terms and phrases are identified.A rulebased reasoning algorithm RelExOnt has been proposed for the said task.Human evaluation of the term extraction output yields precision and recall of 75.7%and 60%,respectively.The relation extraction algorithm,RelExOnt performs well with an average precision of 86.89%.
