Browsing by Author "Koggalahewa, D.N."

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    Intelligent Ontology based Question Answering System for Medical Domain
    (Uva Wellassa University of Srilanka, 2011) Koggalahewa, D.N.; Amararachchi, J.L.; Tharanga, K.G.D.; Pilapitiya, S.U.
    Note: See the PDF Version Irrespective of the domain, the main aim of a Question Answering system is getting a question from the user, comprehending it, searching the answer in an efficient way and presenting the answers to the user. Many methods have been devised for this purpose. This basic idea is using ontology for representing the knowledge and developing the knowledge base. Although the ultimate aim of question answering is finding the exact answer to any question in any context. In today's world of automated content processing, this is inherently a hard task because without a restriction imposed either on the question type or on the user's vocabulary, the question answering process gets a big hit even at the question interpretation phase. The published medical literature and online medical resources are important sources to help physicians make decisions in patient treatment Cimino et al., 2003. Question answering is a rapid-developing technique that automatically analyses thousands of articles to generate a short text, ideally, in less than a few seconds, to answer questions posed by physicians. Such a technique provides a practical alternative that allows physicians to efficiently seek information at point of patient care. Physicians usually have limited time to browse the retrieved information. For example, studies found that physicians spend on average two minutes or less seeking an answer to a question, and that if a search takes longer, it is likely to be abandoned (Radomski, 1986). Although there are a number of annotated medical knowledge databases available for physicians to use, studies found that most of the resources are not frequently used by physicians in large hospitals due to busy work schedule in their lives (Sackett et al., 2000). Physicians often need to consult literature for the latest information in patient care (Siang et al,. 2001). Information retrieval systems (e.g., PubMed) are frequently used by physicians. Another evaluation study showed that it took an average of more than 30 minutes for a healthcare provider to search for answer from the PubMed, which means "information seeking is practical only 'after hours' and not in the clinical setting" (Wikipedia, 2010).
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    Methodology of Knowledge Representation from Natural language
    (Uva Wellassa University of Sri Lanka, 2010) Koggalahewa, D.N.; Athauda, S.P.B.; Pilapitiya, S.U.; Tharanga, K.G.D.; Fonseka, O.A.R.K.
    Information available in different formats cannot be understood by a computer or a machine due to lack of a proper knowledge representation mechanism. It always requires more human effort in feeding the knowledge to the computers or the knowledge base. XML covers the basic level of knowledge representation, but is incapable of utilizing the concepts and semantics in a proper way. Onto_X is an effort made to automate the process of ontology construction from an annotated xml file or database. The annotation process is done by any natural language processing tool (apart from the system). The system requires an xml file as the input and converts it into ontology in owl format. The system is capable of generating the semantics over annotated content with owl components. Xml entities will be automatically mapped into the owl components such as classes, sub classes, instances and relationships. The conversion mechanism is totally automated inside the Onto_X since it assures all the co-relationships over the annotated content. The conversion process identifies the xml properties and assigns semantics with the integration of word-net 2.1 and owl properties over the parsed content. The system uses the protégé libraries for the conversion process. The most special feature in the conversion process is that it uses its own inference, without just mapping xml properties to owl. The system is capable of visualizing the mapped owl ontology and it allows the user to refine the content of the constructed ontology. The final outcome of the system is ontology in owl format, which is mapped from the xml file or a database. The research ensures a better knowledge representation mechanism and it will assure the creation of domain knowledge from the xml file. The expandability is high since it takes information from the base level. Key words: Information, Knowledge, Onto_X, Natural language