Review on the advancements of disambiguation in semantic question answering system. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Hazrina, Sofian; Sharef, Nurfadhlina Mohd; Ibrahim, Hamidah; Murad, Masrah Azrifah Azmi; Noah, Shahrul Azman Mohd
• Is Part Of: Information processing & management. Volume 53:Issue 1(2017:Jan.)
• Page Start: 52
• Page End: 69
• Publication Date: 2017-01
• Subjects: Natural language question -- Semantic question answering -- Ambiguity types -- SQA disambiguation solution; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2016.06.006
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: Ambiguity is a potential problem in any semantic question answering (SQA) system due to the nature of idiosyncrasy in composing natural language (NL) question and semantic resources. Thus, disambiguation of SQA systems is a field of ongoing research. Ambiguity occurs in SQA because a word or a sentence can have more than one meaning or multiple words in the same language can share the same meaning. Therefore, an SQA system needs disambiguation solutions to select the correct meaning when the linguistic triples matched with multiple KB concepts, and enumerate similar words especially when linguistic triples do not match with any KB concept. The latest development in this field is a solution for SQA systems that is able to process a complex NL question while accessing open-domain data from linked open data (LOD). The contributions in this paper include (1) formulating an SQA conceptual framework based on an in-depth study of existing SQA processes; (2) identifying the ambiguity types, specifically in English based on an interdisciplinary literature review; (3) highlighting the ambiguity types that had been resolved by the previous SQA studies; and (4) analysing the results of the existing SQA disambiguation solutions, the complexity of NL question processing, and the complexity of data retrieval from KB(s) or LOD. The results of this review demonstrated that out of thirteen types of ambiguity identified in the literature, only six types had been successfully resolved by the previous studies. Efforts to improve the disambiguation are in progress for the remaining unresolved ambiguity types to improve the accuracy of the formulated answers by the SQA system. The remaining ambiguity types are potentially resolved in the identified SQA process based on ambiguity scenarios elaborated in this paper. The results of this review also demonstrated that most existing research on SQA systems have treated the processing of the NL question complexity separate from the processing of the KB structure complexity.