Abstract
External knowledge bases, both generic and domain specific, available on the Web of Data have the potential of enriching the content of text documents with structured information. We present the Kanopy system that makes explicit use of this potential. Besides the common task of semantic annotation of documents, Kanopy analyses the semantic network that resides in DBpedia around extracted concepts. The system’s main novelty lies in the translation of social network analysis measures to semantic networks in order to find suitable topic labels. Moreover, Kanopy extracts advanced knolwedge in the form of subgraphs that capture the relationships between the concepts.
Keywords
- Noun Phrase
- Semantic Network
- Document Topic
- Topic Labelling
- Keyphrase Extraction
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
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Hulpuş, I., Hayes, C., Karnstedt, M., Greene, D., Jozwowicz, M. (2013). Kanopy: Analysing the Semantic Network around Document Topics. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), vol 8190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40994-3_53
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DOI: https://doi.org/10.1007/978-3-642-40994-3_53
Publisher Name: Springer, Berlin, Heidelberg
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