Abstract
In this paper, we aim to move ontology-based Arabic NLP forward by experimenting with the generation of a comprehensive Arabic lexical ontology using multiple language resources. We recommend a combination of MUHIT, WordNet and SUMO and use a simple method to link them, which results in the generation of an Arabic-lexicalized version of the SUMO ontology. Then, we evaluate the generated ontology, and propose a method for increasing its named entity coverage using DBpedia, English-to-Arabic Transliteration, and Named Entity Recognition. We end up with an Arabic lexical ontology that has 228K Arabic synsets, linked to 7.8K concepts and 143K instances. This ontology achieves a precision of 96.9% and recall of 75.5% for NLU scenarios.
Author supplied keywords
Cite
CITATION STYLE
Kamal, E., Rashwan, M., & Alansary, S. (2015). High quality Arabic lexical ontology based on MUHIT, WordNet, SUMO and DBpedia. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9041, pp. 98–111). Springer Verlag. https://doi.org/10.1007/978-3-319-18111-0_8
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
