Automatic relationship extraction from agricultural text for ontology construction

Abstract 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 rule-based 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%.

[1]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[2]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[3]  Natalia Konstantinova,et al.  Review of Relation Extraction Methods: What Is New Out There? , 2014, AIST.

[4]  Nanda Kambhatla,et al.  Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Information Extraction , 2004, ACL.

[5]  Marti A. Hearst.  Automatic Acquisition of Hyponyms from Large Text Corpora , 1992, COLING.

[6]  Niladri Chatterjee,et al.  Word Alignment in English-Hindi Parallel Corpus Using Recency-Vector Approach: Some Studies , 2006, ACL.

[7]  Niladri Chatterjee,et al.  RENT: Regular Expression and NLP-Based Term Extraction Scheme for Agricultural Domain , 2017 .

[8]  Hend S. Al-Khalifa,et al.  Semantic Relationship Extraction and Ontology Building using Wikipedia: A Comprehensive Survey , 2010 .

[9]  Enrique Alfonseca,et al.  Pattern Learning for Relation Extraction with a Hierarchical Topic Model , 2012, ACL.

[10]  Scott Miller,et al.  A Novel Use of Statistical Parsing to Extract Information from Text , 2000, ANLP.

[11]  Iryna Gurevych,et al.  Context-Aware Representations for Knowledge Base Relation Extraction , 2017, EMNLP.

[12]  Mário J. Silva,et al.  Semi-Supervised Bootstrapping of Relationship Extractors with Distributional Semantics , 2015, EMNLP.

[13]  S. R. Mousavi,et al.  A General Overview on Intercropping and Its Advantages in Sustainable Agriculture , 2011 .

[14]  Mirella Lapata,et al.  Similarity-Driven Semantic Role Induction via Graph Partitioning , 2014, CL.

[15]  Andrew McCallum,et al.  Relation Extraction with Matrix Factorization and Universal Schemas , 2013, NAACL.

[16]  Andrew McCallum,et al.  Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text , 2006, NAACL.

[17]  Marco A. Casanova,et al.  Distant Supervision for Relation Extraction Using Ontology Class Hierarchy-Based Features , 2014, ESWC.

[18]  Daniel Jurafsky,et al.  Distant supervision for relation extraction without labeled data , 2009, ACL.