By Zareen Syed, Tim Finin (auth.), Joaquim Filipe, Ana Fred, Bernadette Sharp (eds.)
This booklet constitutes the completely refereed post-conference complaints of the second one foreign convention on brokers and synthetic Intelligence, ICAART 2010, held in Valencia, Spain, in January 2010. The 17 revised complete papers awarded including an invited paper have been rigorously reviewed and chosen from 364 submissions. comparable because the convention the papers are equipped in simultaneous tracks: man made Intelligence and brokers. the chosen papers mirror the interdisciplinary nature of the convention. the variety of themes is a vital characteristic of this convention, permitting an total belief of a number of very important medical and technological trends.
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Extra info for Agents and Artificial Intelligence: Second International Conference, ICAART 2010, Valencia, Spain, January 22-24, 2010. Revised Selected Papers
Syed and T. Finin training set and then tested it using the 95% of the remaining scores. Table 3 shows the accuracy obtained for the positive and negative entity matches using the learned thresholds for people, locations and organizations dataset separately and then for the combined dataset. 2% ). 8% ). 9% with the accuracy of positive match being slightly higher than the accuracy for negative match prediction. Table 3. 3 Disambiguation Trees We are developing an approach to disambiguate mentions that refer to a Wikipedia entity.
When we were not able to extract a profession or nationality of the entity from the first sentence, we gave that feature a value of zero. We refer to these decision trees that help in disambiguating entities as disambiguation trees. We constructed several disambiguation trees for different sets of persons having the same name. Disambiguation trees constructed as a result of three of our experiments on persons having name Michael Jackson, Michael Jordan and George Harrison are shown in Tables 4, 5 and 6.
XML data mining. , Pract. Exper. 40(2), 101–130 (2010) 5. : XQuake - An XQuery-like Language for Mining XML Data. In: The Second International Conference on Agents and Artificial Intelligence (ICAART), Valencia, Spain, pp. 20–27 (2010) 38 A. Romei and F. Turini 6. : XMark: A Benchmark for XML Data Management. In: The 28th International Conference on Very Large Data Bases (VLDB), Hong Kong, China, pp. 974–985 (2002) 7. W3C World Wide Web Consortium. org/TR/xpath-full-text-10/ 8. : An XML-Based Database for Knowledge Discovery.