Abstract
Existing academic search systems like Google Scholar usually return a long list of scientific articles for a given research domain or topic (e.g. “document summarization”, “information extraction”), and users need to read volumes of articles to get some ideas of the research progress for a domain, which is very tedious and time-consuming. In this paper, we propose a novel system called AKMiner (Academic Knowledge Miner) to automatically mine useful knowledge from the articles in a specific domain, and then visually present the knowledge graph to users. Our system consists of two major components: a) the extraction module which extracts academic concepts and relations jointly based on Markov Logic Network, and b) the visualization module which generates knowledge graphs, including concept-cloud graphs and concept relation graphs. Experimental results demonstrate the effectiveness of each component of our proposed system.
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Huang, S., Wan, X. (2013). AKMiner: Domain-Specific Knowledge Graph Mining from Academic Literatures. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds) Web Information Systems Engineering – WISE 2013. WISE 2013. Lecture Notes in Computer Science, vol 8181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41154-0_18
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DOI: https://doi.org/10.1007/978-3-642-41154-0_18
Publisher Name: Springer, Berlin, Heidelberg
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