Creating Data-Driven Ontologies: An Agriculture Use Case

Creating Data-Driven Ontologies: An Agriculture Use Case

de Boer, M.H.T.
Verhoosel, J.P.C.

2019

The manual creation of an ontology is a tedious task. In the field of ontology learning, Natural Language Processing (NLP) techniques are used to automatically create ontologies. In this paper, we present a methodology using data-driven techniques to create ontologies from unstructured documents in the agriculture domain. We use state-of-the-art NLP techniques based on Stanford OpenIE, Hearst patterns and co-occurrences to create ontologies. We add an NLP-method that uses dependency parsing and transformation rules based on linguistic patterns. In addition, we use keyword-driven techniques from the query expansion field, based on Word2vec, WordNet and ConceptNet,to create ontologies. We add a method that takes the union of the ontologies produced by the keyword-based methods. The semantic quality of the different ontologies is calculated using automatically extracted keywords. We define recall, precision and F1-score based on the concepts and relations in which the keywords are present. The results show that 1) the method based on co-occurrences has the best F1-score with more than 100 keywords; 2) the keyword-based methods have a higher F1-score than the NLP-based methods with less than 100 keywords in the evaluation and; 3) the combined keyword-based method always has a higher F1-score compared to each single method. In our future work, we will focus on improving the dependency parsing algorithm, improving combining different ontologies, and improving our quality evaluation methodology.

Informatics
Knowledge engineering
Machine learning
Agriculture

http://resolver.tudelft.nl/uuid:8fe50c6c-107a-48e8-b33f-982f1a5599f7

866356

ALLDATA 2019: the Fifth International Conference on Big Data, Small Data, Linked Data and Open Data, Valencia, Spain 24-28 march 2019, 52-57

conference paper