Browsing by Author "De Silva, NHND"

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    item: Conference-Abstract
    Document analysis based automatic concept map generation for enterprises
    (2014-06-25) Karannagoda, EL; Herath, HMTC; Fernando, KNJ; Karunarathne, MWID; De Silva, NHND; Perera, AS
    Ever growing knowledge bases of enterprises present the demanding challenge of proper organization of information that would enable fast retrieval of related and intended information. Document repositories of enterprises consist of large collections of documents of varying size, format and writing styles. This diversified and unstructured nature of documents restrict the possibilities of developing uniform techniques for extracting important concepts and relationships for summarization, structured representation and fast retrieval. The documented textual content is used as the input for the construction ofa concept map. Here a rule based approach is used to extract concepts and relationships among them. Sentence level breakdown enables these rules to identify those concepts and relationships. These rules are based on elements in a phase structure tree of a sentence. For improving accuracy and the relevance of the extracted concepts and relationships, the special features such as titles, bold and upper case texts are used. This paper discusses how to overcome the above mentioned challenges by utilizing high level natural language processing techniques, document pre-processing techniques and developing easily understandable and extractable compact representation of concept maps. Each document in the repository is converted to a concept map representation to capture concepts and relationships among concepts described in the said document. This organization would represent a summary of the document. These individual concept maps are utilized to generate concept maps that represent sections of the repository or the entire document repository. This paper discusses how statistical techniques are used to calculate certain metrics which are used to facilitate certain requirements of the solution. Principle component analysis is used in ranking the documents by importance. The concept map is visualized using force directed type graphs which represent concepts by nodes and relationships by edges.
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    item: Conference-Abstract
    SeMap - mapping dependency relationships into semantic frame relationships
    (2011) Fernando, CSNJ; Maldeniya, MKDT; Wijeratne, DNC; Perera, AS; Goertze, B; De Silva, NHND
    We describe the refacloring process of the RelEx2Frame component of OpenCog AGI Framework, a method for expanding concept variables used in RelEx and automatic generation of a common sense knowledge base specifically with relation to concept relationships. The well-known Drools rule engine is used instead of handcoded rides; an asynchronous concurrent architecture and an indexing mechanism are designed to gain performance of re-factored RelEx2Frame. WordNet aided supervised learning mechanism is applied to expand concept variables. Association mining is used on semantic frames acquired through processing an instance of Wikipedia in order to generate a common sense knowledge base.
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    item: Conference-Abstract
    Semi-supervised algorithm for concept ontology based word set expansion
    (2014-06-26) De Silva, NHND; Perera, AS; Maldeniya, MKDT
    Word lists that contain closely related sets of words is a critical requirement in machine understanding and processing of natural languages. Creating and maintaining such closely related word lists is a critical and complex process that requires human input and carried out manually in the absence of tools. We describe a supervised learning mechanism which employs a word ontology to expand word lists containing closely related sets of words. The approach described in this paper uses two novel supervised learning techniques that complement each other for the purpose of expanding existing lists of related words. Expanding concept variable lists of RelEx2Frame component of OpenCog Artificial General Intelligence Framework using WordNet is used as a proof of concept. Intervention of this project would enable OpenCog applications to attempt to understand words that they were not able to understand before, due to the limited size of existing lists of related words.