GWC 2008

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30 Manuela Angioni, Roberto Demontis, Massimo Deriu, and Franco Tuveri 4 An Example of Specific Context An example of a specific context is referred to a GIS context. In order to execute a coherent analysis of documents, two relevant problems have to be resolved. First is the choice of the taxonomy and thesaurus describing the specific semantic domain. The multilingual 2004 GEMET (GEneral Multilingual Environmental Thesaurus, version 1.0) [14] was chosen. GEMET consists in 5.298 terms hierarchically ordered with 109 top terms, 40 themes, 35 groups and 3 super groups. Then the problem of mapping GEMET concepts into the WordNet concepts. For example, the GEMET term “topography” has the right meaning in the WordNet semantic net, while the term “photogrammetry” does not appear at all. In such cases, and generally in all specialized contexts or specific domains, we need to define a new semantic key for this kind of term that is the identifier used in the GEMET thesauri. In order to generate the conceptual mapping, a semi-automatic procedure was implemented. This procedure is very similar to the evaluation of the Wikipedia page for enriching the SemanticNet and it is based on two similar properties of GEMET and WordNet: GEMET and WordNet have a hierarchical structure. For example, the GEMET relations “narrower” and “broader” are similar to the “hypernym” and “hyponym” relations of WordNet. This similarity is also used to build the relation “IS- A” in the sSN. Another important property is the presence of textual description of concepts in both GEMET and WordNet. Starting from the top term in the GEMET thesauri, the semantic keys from the textual description of a concept are extracted and its semantic vicinity is calculated with semantic keys extracted from the textual descriptions of concepts found in WordNet using the term related to the GEMET concept. The presence of some concepts such as the “narrower/broader” of the GEMET concept mapped to concepts “hyperonymy/hyponymy” of the concepts found in WordNet are also evaluated. The final evaluation of results was performed by a supervisor. A similar approach can be found at [15]. If the GEMET concept is not found in WordNet, the term related to the GEMET concept is used as the mapped semantic key. Using such mapping makes it possible to classify the documents analysed in the generic context with the GEMET taxonomy and to generate the sSN which contains the relations “IS-A”, derived from the “narrower” and “broader” GEMET relations, and the 'SAME-AS' relation, described in the section 2, which is derived from the conceptual mapping and connects the sSN node with the SN nodes. An example is showed in figure 4. 5 3D User Interface for SemanticNet As described before, the SemanticNet allows users to navigate in the concepts through relations. This feature include the need to overcome the limits of traditional user interface, specially in terms of effectiveness and usability. For this reason, in parallel with the development of the SemanticNet we investigate new paradigm and model of UI, focusing on 3D visualization to allows the user to change the point of view, improving the perception and the understanding of contents [16]. The main idea is to
SemanticNet: a WordNet-based Tool for the Navigation of Semantic… 31 Fig. 4: A segment of the specialized SemanticNet relative to the concept “Tiger”. improve the user experience during the phases of searching and extracting of the concepts. An essential requirement for such goal is to guarantee a better usability in information navigation by concrete representations and simplicity [17]. We develop an alternative tool (3DUI4SemanticNet) for the browsing of the Web resources by means of the map of concept. Formulating the query through the search engine, the user can move through the SemanticNet and extract the concepts which really interest him, limiting the search field and obtaining a more specific result. This tool works according to the user preferences and current user context, providing a 3D interactive scene that represent the selected portion of SemanticNet. Moreover, it provides a three-dimensional view that guarantee a better usability in
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Page 3 and 4: Preface We are very pleased to hold
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Page 30 and 31: 22 Manuela Angioni, Roberto Demonti
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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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80 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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KYOTO: A System for Mining, Structu
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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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484 Piek Vossen et al. COLING/ACL 2
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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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508 Ming-Wei Xu, Jia-Fei Hong, Shu-
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524 Mahsa A. Yarmohammadi et al. 3.
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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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