By Bo Fu, Rob Brennan, Declan O’Sullivan (auth.), Asunción Gómez-Pérez, Yong Yu, Ying Ding (eds.)
This e-book constitutes the refereed complaints of the fourth Asian Semantic internet convention, ASWC 2009, held in Shanghai, China, in December 2009.
The 35 revised complete papers offered have been rigorously reviewed and chosen from sixty three submissions. those submissions hide a huge diversity of issues together with, question languages and optimization, rule and logics, scalable reasoning, semantic content material new release, database and semantics, semantic net prone, eSemantics (e.g., e-Business, e-Science, e-Learning, e-Culture, e-Health), social internet and semantics, semantic graph mining, safety for semantic net, ontology modeling, ontology administration, to call a number of.
Read Online or Download The Semantic Web: Fourth Asian Conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings PDF
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Extra resources for The Semantic Web: Fourth Asian Conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings
Because candidates include all possible models for all possible senses we have to select only the most promising ones at the end. We decided to select the best ones for each triple depth separately because then we can observe the impact of different triple depth on constructed models. For every model candidate we also computed the number of instances that support it and use this number as a guide when choosing the best models. In evaluation we included 5-10 most supported models from selected triple depths.
In particular, this introduces some overlap between examples containing related words. For unsupervised learning we used the k-means clustering method due to its simplicity and reasonable speed. The distance measure was cosine similarity, as commonly used for measuring similarity on text data. edu/ Modeling Common Real-Word Relations Using Triples Extracted from n-Grams 21 determining coverage of concepts in WordNet. We can assume that the majority of cluster mass exists near the centroid and ignore the examples farther off.
Our approach uses a trivial solution to this. Every triple is converted to a set of triples where each 22 R. Sipoš et al. one represents a possible combination of senses based on words from source triple. However, this drastically increases the amount of data we have to process. One possible solution would be using only the most frequently occurring senses and ignoring others. The current approach does not filter senses in any way because we wanted to construct a baseline first so we can compare results and measure effects of not using all possible word senses.