A dictionary for translation from natural to formal data model language. (7th September 2020)

• Record Type: Journal Article
• Authors: Šuman, Sabrina; Jakupović, Alen; Marinac, Mladen
• Is Part Of: Computational intelligence. Volume 37:Number 1(2021)
• Page Start: 87
• Page End: 127
• Publication Date: 2020-09-07
• Subjects: data modeling -- dictionary -- formal language -- knowledge‐based system -- linguistic corpus -- machine translation -- pattern recognition; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12393
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: The paper describes our current research activities and results related to developing knowledge‐based systems to support the creation of entity‐relationship (ER) models. The authors based obtaining an ER model in textual form on translation from one language into another, that is, from an English controlled natural language into the formalized language of an ER data model. Our translation method consisted of creating translation rules of sentential form parts into ER model constructs based on the textual and character patterns detected in the business descriptions. To enable the computer analyses necessary for creating translation mechanisms, we created a linguistic corpus that contains lists of the business descriptions and the texts of other business materials. From the corpus, we then created a specific dictionary and linguistic rules to automate the business descriptions' translation into the ER data model language. Before that, however, the corpus was enriched by adding annotations to the words related to ER data model constructs. In this paper, we also present the main issues uncovered during the translation process and offer a possible solution with utility evaluation: applying information‐extraction performance measures to a set of sentences from the corpus.