Artificial Intelligence: Theories and Applications: 7th by Wan-Shiou Yang, Shi-Xin Weng (auth.), Ilias Maglogiannis,

By Wan-Shiou Yang, Shi-Xin Weng (auth.), Ilias Maglogiannis, Vassilis Plagianakos, Ioannis Vlahavas (eds.)

This publication constitutes the complaints of the seventh Hellenic convention on synthetic Intelligence, SETN 2012, held in Lamia, Greece, in might 2012. The forty seven contributions integrated during this quantity have been rigorously reviewed and chosen from eighty one submissions. They take care of emergent subject matters of man-made intelligence and are available from the SETN major convention in addition to from the next precise periods on advancing translational organic study during the incorporation of man-made intelligence methodologies; synthetic intelligence in bioinformatics; clever annotation of electronic content material; clever, affective, and normal interfaces; and unified multimedia wisdom illustration and processing.

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Efficient constraint propagation engines. ACM Transactions on Programming Languages and Systems 31(1), 2:1–2:43 (2008) 19. : Modelling. In: Rossi, et al. ) [16], ch. 11, pp. 377–406 20. : Nearly optimum timetable construction through CLP and intelligent search. Int’l J. on AI Tools 7(4), 415–442 (1998) 21. : Compiling finite linear CSP into SAT. Constraints 14(2), 254–272 (2009) 22. : A glimpse of constraint satisfaction. AI Review 13(3), 215–227 (1999) 23. : Constraint-Based Local Search. Logic Programming.

We have applied this approach in the framework of a large content base (about 4 million objects) that our team – through our MINT mapping tool – has injected into Europeana. Applying this approach in a variety of subareas, such as archaeology, photography, modern arts, fashion, where specific thematic knowledge can be derived and used, as well as combining it with the evolving field of linked open data for cultural heritage are future extensions of the presented work. Acknowledgment. gr. References 1.

It is also possible to use statistical sense relatedness measures estimated from sense-tagged corpora, turning our method into a supervised one, but we reserve this for future work. Section 2 below introduces our ilp model; Section 3 defines the two sense relatedness measures we adopted; Section 4 presents experimental results against two other prominent unsupervised wsd methods; and Section 5 concludes. 2 Our ILP Model of Word Sense Disambiguation Let w1 , . . , wn be the word occurrences of an input sentence; sij denotes the j-th possible sense of wi , and rel(sij , si j ) the relatedness between senses sij and si j .

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