Abstract
This paper presents an exploration engine for text mining and cross-context link discovery, implemented as a web application with a user-friendly interface. The system supports experts in advanced document exploration by facilitating document retrieval, analysis and visualization. It enables document retrieval from public databases like PubMed, as well as by querying the web, followed by document cleaning and filtering through several filtering criteria. Document analysis includes document presentation in terms of statistical and similarity based properties and topic ontology construction through document clustering, while the distinguishing feature of the presented system is its powerful cross context and cross-domain document exploration facility through bridging term discovery aimed at finding potential cross-domain linking terms. Term ranking based on the developed ensemble heuristic enables the expert to focus on cross context terms with greater potential for cross-context link discovery. Additionally, the system supports the expert in finding relevant documents and terms by providing customizable document visualization, a color-based domain separation scheme and highlighted top-ranked bisociative terms.
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Juršič, M., Cestnik, B., Urbančič, T., Lavrač, N. (2013). HCI Empowered Literature Mining for Cross-Domain Knowledge Discovery. In: Holzinger, A., Pasi, G. (eds) Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. HCI-KDD 2013. Lecture Notes in Computer Science, vol 7947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39146-0_12
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DOI: https://doi.org/10.1007/978-3-642-39146-0_12
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