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

This paper presents an outline of an Ontological and Semantic understanding-based model (SEMONTOQA) for an open-domain factoid Question Answering (QA) system. The outlined model analyses unstructured English natural language texts to a vast extent and represents the inherent contents in an ontological manner. The model locates and extracts 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 questions. The system model converts the unstructured texts into a minimalistic, labelled, directed graph that we call a Syntactic Sentence Graph (SSG). An Automatic Text Interpreter using a set of pre-learnt Text Interpretation Subgraphs and patterns tries to understand the contents of the SSG in a semantic way. The system proposes a new feature and action based Cognitive Entity-Relationship Network 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 incorporates an effective Text Inference Engine which takes the responsibility of inferring the text contents and isolating entities, 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 designed 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. Empirical 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.