GWC 2008

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26 Manuela Angioni, Roberto Demontis, Massimo Deriu, and Franco Tuveri document it can associate to the term the most correct meaning and can assign a weight to each category related to the content. In fact, each term in WordNet has more than one meaning each corresponding to a Wikipedia page. We therefore need to extract the specific meaning described in the Wikipedia page content, in order to build a conceptual map where the nodes are the “senses” (synset of WordNet or term+category) and the links are given both by the WordNet semantical-linguistic relations and by the conceptual associations built by the Wikipedia authors. For example, the term tiger in WordNet corresponds to the Wikipedia page having http://en.wikipedia.org/wiki/Tiger as URL and the same title as the term, as showed in figure 1. Such a conceptual map, allows the user to move on an information structure that connects several topics through the semantical-lexical relations extracted from WordNet but also through the new associations made by the conceptual connections inserted by Wikipedia users and extracted by the system. From each synset a new node of the conceptual map is defined, containing information such as the synsetID, the categories and their weight. Through the information extracted from Wikipedia it is possible to build nodes, having a conceptual proximity with the beginning node, and to define, through these relations, a data structure linking all nodes of the SemanticNet. Each node is then uniquely identified by a semantic key, by the term referred to the Wikipedia page and by the extracted categories. 3.1 The Data Structure The text of about 60.000 Wikipedia articles corresponding to a WordNet term and independently from their content has been analyzed, in order to extract new relations and concepts, and it has been classified, using the same convention used for the mapping of the terms of WordNet in WordNet Domains, assigning to each category a weight of relevance. Only the categories having a greater importance are taken in consideration. The success in the assigning the categories has been evaluated to reach about 90%. Measures are made choosing a set of categories and analyzing manually the correctness of the resources classified under each category. Through the content classification of Wikipedia pages, the system assigns to their title a synsetID of WordNet, if it exists. By analyzing the content of the Wikipedia pages, a new relation named “COMMONSENSE” is defined, that delineates the SemanticNet in union with the semantic relations “IS-A” and “PART-OF” given by WordNet. The COMMONSENSE relation is a connection between a term and the links defined by the author of the Wikipedia page related to that term. These links are associations that the author identified and put in evidence. Sometimes this relation closes with a direct connection a circle of semantic relations between concepts of the WordNet semantic net. In such situation we consider valid these direct links because someone, in the Wikipedia resource, certified a relation between them. In fact, the vicinity between concepts is justified with the logic expressed from the author in the
SemanticNet: a WordNet-based Tool for the Navigation of Semantic… 27 Wikipedia article. The importance of these relations comes out each time the system give back results to a query, providing the user concepts that are related in WordNet or in the Wikipedia derived conceptual map. Fig. 1: The node of the SemanticNet which describes the concept “Tiger” In the SemanticNet, relations are also defined as oriented edges of a graph. The inverse relations are then a usable feature of the net. An example is the Hyponym/Hypernym relation, labeled in the SemanticNet as a “IS-A” relation with direction. The concept map defined is still under development and improvement. It is constituted by 25010 nodes each corresponding to a “sense” of WordNet and related
Page 1 and 2: A. Tanács, D. Csendes, V. Vincze,
Page 3 and 4: Preface We are very pleased to hold
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Page 16 and 17: 8 Javier Álvez et al. that WordNet
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Page 30 and 31: 22 Manuela Angioni, Roberto Demonti
Page 44 and 45: 36 Irina V.Azarova, Anna S. Marina,
Page 52 and 53: Some Issues in the Construction of
Page 54 and 55: 46 Valentina Balkova, Andrey Sukhon
Page 64 and 65: A Comparison of Feature Norms and W
Page 66 and 67: 58 Eduard Barbu and Massimo Poesio
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76 Sonja Bosch, Christiane Fellbaum
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92 Cristina Butnariu and Tony Veale
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100 Cristina Butnariu and Tony Veal
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102 Thatsanee Charoenporn et al. la
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104 Thatsanee Charoenporn et al. Fi
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106 Thatsanee Charoenporn et al. L:
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108 Thatsanee Charoenporn et al. Ta
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110 Thatsanee Charoenporn et al. 6.
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112 Peter Clark, Christiane Fellbau
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An Evaluation Procedure for Word Ne
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122 Irene Cramer and Marc Finthamme
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148 Gerard de Melo and Gerhard Weik
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Words, Concepts and Relations in th
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164 Magdalena Derwojedowa et al. Wo
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166 Magdalena Derwojedowa et al. ku
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168 Magdalena Derwojedowa et al. ev
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170 Magdalena Derwojedowa et al. Fi
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172 Magdalena Derwojedowa et al. 3.
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174 Magdalena Derwojedowa et al. of
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176 Magdalena Derwojedowa et al. Re
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Exploring and Navigating: Tools for
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180 Marc Finthammer and Irene Crame
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186 Darja Fišer 2 Methodology 2.1
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188 Darja Fišer (1) a Det-Noun phr
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190 Darja Fišer 4 Evaluation 4.1 S
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192 Darja Fišer Acknowledgements I
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The Global WordNet Grid Software De
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196 Aleš Horák, Karel Pala, and A
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198 Aleš Horák, Karel Pala, and A
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The Development of a Complex-Struct
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202 Aleš Horák, Piek Vossen, and
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210 Chu-Ren Huang, Chiyo Hotani, Tz
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Paranymy: Enriching Ontological Kno
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222 Chu-Ren Huang, Pei-Yi Hsiao, I-
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230 Kadri Kerner The modal verbs ar
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232 Kadri Kerner Sometimes the near
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234 Kadri Kerner Some researchers [
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236 Kadri Kerner Ettevõtmine Toime
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238 Kadri Kerner Conference on Lang
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240 Svetla Koeva, Cvetana Krstev, a
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Language Independent and Language D
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256 Judit Kuti, Károly Varasdi, Á
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270 Jurie le Roux, Koliswa Moropa,
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282 Harald Lüngen, Claudia Kunze,
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298 Niloofar Mansoory and Mahmood B
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Towards a Morphodynamic WordNet of
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306 Nazaire Mbame 3.1 The semantic
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308 Nazaire Mbame from the proto mi
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310 Nazaire Mbame 4 Conclusion As s
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312 Márton Miháltz et al. word se
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314 Márton Miháltz et al. to the
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316 Márton Miháltz et al. 2.3 Adv
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318 Márton Miháltz et al. 3.2 Eva
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320 Márton Miháltz et al. and sem
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322 Rajat Kumar Mohanty et al. 1 In
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324 Rajat Kumar Mohanty et al. Tabl
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326 Rajat Kumar Mohanty et al. Mara
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328 Rajat Kumar Mohanty et al. Tabl
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330 Rajat Kumar Mohanty et al. Fig.
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332 Rajat Kumar Mohanty et al. Once
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Estonian WordNet: Nowadays Heili Or
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336 Heili Orav, Kadri Vider, Neeme
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338 Heili Orav, Kadri Vider, Neeme
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340 Bolette Sandford Pedersen and S
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350 Ida Raffaelli, Marko Tadić, Bo
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Towards Automatic Evaluation of Wor
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362 J. Ramanand and Pushpak Bhattac
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376 Nils Reiter and Paul Buitelaar
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388 Horacio Rodríguez et al. synse
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390 Horacio Rodríguez et al. Since
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392 Horacio Rodríguez et al. they
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394 Horacio Rodríguez et al. 3.2.2
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396 Horacio Rodríguez et al. Table
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398 Horacio Rodríguez et al. A bas
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400 Horacio Rodríguez et al. ‏:
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402 Horacio Rodríguez et al. Table
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404 Horacio Rodríguez et al. Ackno
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Building a WordNet for Persian Verb
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408 Masoud Rouhizadeh, Mehrnoush Sh
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414 Mehrnoush Shamsfard semantic re
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416 Mehrnoush Shamsfard 1- Providin
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418 Mehrnoush Shamsfard FarsNet for
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420 Virach Sornlertlamvanich et al.
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Extraction of Selectional Preferenc
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430 Dennis Spohr defined as follows
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432 Dennis Spohr the most recent ma
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434 Dennis Spohr ... 00058624-n 00
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436 Dennis Spohr hierarchy (cf. Sec
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438 Dennis Spohr 5.2 Evaluation of
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440 Dennis Spohr ington, DC (1997)
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442 Dan Tufiş, Radu Ion, Luigi Boz
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454 Tony Veale and Yanfen Hao this
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456 Tony Veale and Yanfen Hao 3 Est
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458 Tony Veale and Yanfen Hao peaco
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460 Tony Veale and Yanfen Hao While
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Comparing WordNet Relations to Lexi
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464 Veronika Vincze, Attila Almási
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476 Piek Vossen et al. KYOTO's prin
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478 Piek Vossen et al. [13, 14, 15,
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480 Piek Vossen et al. 7 Sharing Kn
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482 Piek Vossen et al. discuss the
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486 Piek Vossen et al. structures w
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488 Piek Vossen et al. original ide
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500 Piek Vossen et al. Interchange
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502 Piek Vossen et al. There exists
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504 Piek Vossen et al. does not dow
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508 Ming-Wei Xu, Jia-Fei Hong, Shu-
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Using WordNet in Extracting the Fin
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522 Mahsa A. Yarmohammadi et al. an
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524 Mahsa A. Yarmohammadi et al. 3.
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526 Mahsa A. Yarmohammadi et al. A
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528 Mahsa A. Yarmohammadi et al. -V
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530 Mahsa A. Yarmohammadi et al. sa
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532 Hamza Zidoum conceptual identif
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534 Hamza Zidoum 2.2 Semantic Relat
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536 Hamza Zidoum WordNet, since it
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538 Hamza Zidoum The lexicographer
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540 Hamza Zidoum Table 3. Test Case
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542 Hamza Zidoum 5.3 Future Work Ou
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544 Hamza Zidoum 26. Modern Standar
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546 Moropa, K. 269 Neri, F. 474 Nim
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