Masters Theses Department of Mathematics and Statistics

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Browsing Masters Theses Department of Mathematics and Statistics by Author "Mugo, David M."

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    Connecting People using Latent Semantic Analysis for Knowledge Sharing
    (2010-01) Mugo, David M.
    A shift from technology-oriented knowledge management to people-oriented knowledge management is indispensable. To achieve this, organizations must understand the nature of knowledge. In this work, knowledge has been found to be both a process and a collection of artifacts. This makes knowledge and the knower to be two inseparable entities. Consequently, the appropriate way to share both the explicit and the implicit knowledge components is through people-with-people connection. However, from existing barriers like location and time differences among others, people-with-documents connection is proposed as an intermediate step. The investigation of latent semantic analysis (LSA) in achieving people-with-documents connection has revealed decreased precision performance at higher recall performance. A solution to include annotations in the technique has been proposed to refine knowledge representation into the LSA technique. Annotation process based on domain ontologies has been proposed to compliment the LSA knowledge mining process from documents with domain knowledge represented by ontologies
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    An Investigation of the Latent Semantic Analysis Technique for Document Retrieval
    (2014) Mugo, David M.
    Latent semantic analysis (LSA) application in information retrieval promises to offer better performance by overcoming some limitations that plagues traditional termmatching techniques. These term-matching techniques have always relied on matching query terms with document terms to retrieve the documents having terms matching the query terms. However, by use of these traditional retrieval techniques, users’ needs have not been adequately served. While users want to search through information based on conceptual content, natural languages have limited the expression of these concepts. They present synonymy problem (a situation where several words may have the same meaning) and polysemy problem (a situation where a word may have several meanings). Due to these natural language problems, individual words contained in users’ queries, may not explicitly specify the intended user’s concept, which may result in the retrieval of some irrelevant documents. LSA seems to be a promising technique in overcoming these natural language problems especially synonymy problem. It deals with exploiting the global relationships between terms and documents and then mapping these documents and terms in a proximity space, where terms and documents that are closely related are mapped close to each other in this space. Queries are then mapped to this space with documents being retrieved based on similarity measures. In this report, LSA performance in documents retrieval is investigated and compared with traditional term-matching techniques.