A Study on Components, Benchmark Criteria and Techniques used in Ontology-based Question Answering Systems
Keywords:
Question Answering, Question Answering Components, Question Answering Systems, QAS Domain, Natural Language, OntologyAbstract
With the massive shift in advancement of technology from early ages to present world, there is record change in techniques related to data storage to data access. Principally, Search Engines aims to provide information relevant to the user needs from huge archive of data storage units adopting Information Retrieval (IR) and Information Extraction (IE)techniques where the retrieved results display the long list of web links concerned with the user’s interest topic and to present the information to the user in human understandable form that can enhance the user experience.
With the introduction of Artificial Intelligence (AI) and Computational Linguistics, the technology era shifted towards Question Answering – the Answer Driven Search. The main aim of QAS is to provide exact answer instead of big pack of words to the user’s query automatically in minimal amount of time. Present QASs are built on state-of-art technology attempting to answer user queries from heterogeneous and scattered data sources like semantic web. The preciseness of answering the questions may be enhanced with the integration of ontological enhanced processing. Ontology-based QAS helps better in identification of query context and query words semantics understanding, thus may satisfy the queries in a better way. Presently there are a number of ontology-based QAS evolved in last twenty years and practically comparing all of them in systematic manner is not possible. Hence, some method is required to compare these ontological QAS. Thus, we focused on some benchmarking criteria and techniques used to differentiate and compare these ontology-based QAS.
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