SEMONTOQA: A Semantic Understanding-Based Ontological Framework for Factoid Question Answering

This paper presents an outline of an Ontological and Se- mantic understanding-based model (SEMONTOQA) for an open-domain factoid Question Answering (QA) system. The outlined model analyses unstructured English natural lan- guage texts to a vast extent and represents the inherent con- tents in an ontological manner. The model locates and ex- tracts useful information from the text for various question types and builds a semantically rich knowledge-base that is capable of answering different categories of factoid ques- tions. The system model converts the unstructured texts into a minimalistic, labelled, directed graph that we call a Syntactic Sentence Graph (SSG). An Automatic Text In- terpreter using a set of pre-learnt Text Interpretation Sub- graphs and patterns tries to understand the contents of the SSG in a semantic way. The system proposes a new fea- ture and action based Cognitive Entity-Relationship Net- work designed to extend the text understanding process to an in-depth level. Application of supervised learning allows the system to gradually grow its capability to understand the text in a more fruitful manner. The system incorpo- rates an effective Text Inference Engine which takes the re- sponsibility of inferring the text contents and isolating enti- ties, their features, actions, objects, associated contexts and other properties, required for answering questions. A similar understanding-based question processing module interprets the user’s need in a semantic way. An Ontological Mapping Module, with the help of a set of pre-defined strategies de- signed for different classes of questions, is able to perform a mapping between a question’s ontology with the set of ontologies stored in the background knowledge-base. Em- pirical verification is performed to show the usability of the proposed model. The results achieved show that, this model can be used effectively as a semantic understanding based alternative QA system

Question Answering System : A Review On Question Analysis, Document Processing, And Answer Extraction Techniques

Question Answering System could automatically provide an answer to a question posed by human in natural languages. This system consists of question analysis, document processing, and answer extraction module. Question Analysis module has task to translate query into a form that can be processed by document processing module. Document processing is a technique for identifying candidate documents, containing answer relevant to the user query. Furthermore, answer extraction module receives the set of passages from document processing module, then determine the best answers to user. Challenge to optimize Question Answering framework is to increase the performance of all modules in the framework. The performance of all modules that has not been optimized has led to the less accurate answer from question answering systems. Based on this issues, the objective of this study is to review the current state of question analysis, document processing, and answer extraction techniques. Result from this study reveals the potential research issues, namely morphology analysis, question classification, and term weighting algorithm for question classification