Browsing by Subject "Hypergraphs"

Now showing 1 - 2 of 2
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    Biomedical Literature Mining with Transitive Closure and Maximum Network Flow
    (http://doi.acm.org/10.1145/1851476.1851552, 2011-05-15) Hoblitzell, Andrew P.; Mukhopadhyay, Snehasis; Xia, Yuni; Fang, Shiafoen
    The biological literature is a huge and constantly increasing source of information which the biologist may consult for information about their field, but the vast amount of data can sometimes become overwhelming. Medline, which makes a great amount of biological journal data available online, makes the development of automated text mining systems and hence “data-driven discovery” possible. This thesis examines current work in the field of text mining and biological literature, and then aims to mine documents pertaining to bone biology. The documents are retrieved from PubMed, and then direct associations between the terms are computers. Potentially novel transitive associations among biological objects are then discovered using the transitive closure algorithm and the maximum flow algorithm. The thesis discusses in detail the extraction of biological objects from the collected documents and the co-occurrence based text mining algorithm, the transitive closure algorithm, and the maximum network flow which were then run to extract the potentially novel biological associations. Generated hypotheses (novel associations) were assigned with significance scores for further validation by a bone biologist expert. Extension of the work in to hypergraphs for enhanced meaning and accuracy is also examined in the thesis.
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    TEXT MINER FOR HYPERGRAPHS USING OUTPUT SPACE SAMPLING
    (2011-08-16) Tirupattur, Naveen; Mukhopadhyay, Snehasis; Fang, Shiaofen; Xia, Yuni
    Text Mining is process of extracting high-quality knowledge from analysis of textual data. Rapidly growing interest and focus on research in many fields is resulting in an overwhelming amount of research literature. This literature is a vast source of knowledge. But due to huge volume of literature, it is practically impossible for researchers to manually extract the knowledge. Hence, there is a need for automated approach to extract knowledge from unstructured data. Text mining is right approach for automated extraction of knowledge from textual data. The objective of this thesis is to mine documents pertaining to research literature, to find novel associations among entities appearing in that literature using Incremental Mining. Traditional text mining approaches provide binary associations. But it is important to understand context in which these associations occur. For example entity A has association with entity B in context of entity C. These contexts can be visualized as multi-way associations among the entities which are represented by a Hypergraph. This thesis work talks about extracting such multi-way associations among the entities using Frequent Itemset Mining and application of a new concept called Output space sampling to extract such multi-way associations in space and time efficient manner. We incorporated concept of personalization in Output space sampling so that user can specify his/her interests as the frequent hyper-associations are extracted from the text.