KnowNet: building a large net of knowledge from the web

KnowNet: Building a Large Net of Knowledge from the Web Montse Cuadros TALP Research Center, UPC Barcelona, Spain [email protected] German Rigau IXA NLP Group, UPV/EHU Donostia, Spain [email protected] Abstract This paper presents a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method uses a wide-coverage and accurate knowledge-based Word Sense Disambiguation algorithm to assign the most appropriate senses to large sets of topically related words acquired from the web. KnowNet, the resulting knowledge-base which connects large sets of semanticallyrelated concepts is a major step towards the autonomous acquisition of knowledge from raw corpora. In fact, KnowNet is several times larger than any available knowledge resource encoding relations between synsets, and the knowledge KnowNet contains outperform any other resource when is empirically evaluated in a common framework. 1 Introduction Using large-scale knowledge bases, such as WordNet (Fellbaum, 1998), has become a usual, often necessary, practice for most current Natural Language Processing (NLP) systems. Even now, building large and rich enough knowledge bases for broad–coverage semantic processing takes a great deal of expensive manual effort involving large research groups during long periods of development. In fact, hundreds of person-years have been invested in the development of wordnets for various languages (Vossen, 1998). For example, in c 2008. Licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported license (http://creativecommons.org/licenses/by-nc-sa/3.0/). Some rights reserved. more than ten years of manual construction (from 1995 to 2006, that is from version 1.5 to 3.0), WordNet grew from 103,445 to 235,402 semantic relations1. But this data does not seem to be rich enough to support advanced concept-based NLP applications directly. It seems that applications will not scale up to work in open domains without more detailed and rich general-purpose (and also domain-specific) semantic knowledge built by automatic means. Obviously, this fact has severely hampered the state-of-the-art of advanced NLP applications. However, the Princeton WordNet (WN) is by far the most widely-used knowledge base (Fellbaum, 1998). In fact, WordNet is being used world-wide for anchoring different types of semantic knowledge including wordnets for languages other than English (Atserias et al., 2004), domain knowledge (Magnini and Cavagli` a, 2000) or ontologies like SUMO (Niles and Pease, 2001) or the EuroWordNet Top Concept Ontology ( ´ Alvez et al., 2008). It contains manually coded information about English nouns, verbs, adjectives and adverbs and is organized around the notion of a synset. A synset is a set of words with the same part-of-speech that can be interchanged in a certain context. For example, <party,political party>form a synset because they can be used to refer to the same concept. A synset is often further described by a gloss, in this case: ”an organization to gain political power” and by explicit semantic relations to other synsets. Fortunately, during the last years the research community has devised a large set of innovative methods and tools for large-scale automatic acquisition of lexical knowledge from structured and unstructured corpora. Among others we can men1Symmetric relations are counted only once. tion eXtended WordNet (Mihalcea and Moldovan, 2001), large collections of semantic preferences acquired from SemCor (Agirre and Martinez, 2001; Agirre and Martinez, 2002) or acquired from British National Corpus (BNC) (McCarthy, 2001), large-scale Topic Signatures for each synset acquired from the web (Agirre and de Lacalle, 2004) or knowledge about individuals from Wikipedia (Suchanek et al., 2007). Obviously, all these semantic resources have been acquired using a very different methods, tools and corpora. As expected, each semantic resource has different volume and accuracy figures when evaluated in a common and controlled framework (Cuadros and Rigau, 2006). However, not all these large-scale resources encode semantic relations between synsets. In some cases, only relations between synsets and words have been acquired. This is the case of the Topic Signatures acquired from the web (Agirre and de Lacalle, 2004). This is one of the largest semantic resources ever built with around one hundred million relations between synsets and semantically related words 2. A knowledge net or KnowNet (KN), is an extensible, large and accurate knowledge base, which has been derived by semantically disambiguating small portions of the Topic Signatures acquired from the web. Basically, the method uses a robust and accurate knowledge-based Word Sense Disambiguation algorithm to assign the most appropriate senses to the topic words associated to a particular synset. The resulting knowledge-base which connects large sets of topically-related concepts is a major step towards the autonomous acquisition of knowledge from raw text. Table 1 compares the different volumes of semantic relations between synset pairs of available knowledge bases and the newly created KnowNets3. Varying from five to twenty the number of processed words from each Topic Signature, we created automatically four different KnowNet versions with millions of new semantic relations between synsets. In fact, KnowNet is several times larger than WordNet, and when evaluated empirically in a common framework, the knowledge it contains outperforms any other semantic resource. After this introduction, section 2 describes the Topic Signatures acquired from the web. Section 2Available at http://ixa.si.ehu.es/Ixa/resources/sensecorpus 3These KnowNet versions are available at http://adimen.si.ehu.es Source #relations Princeton WN3.0 235,402 Selectional Preferences from SemCor 203,546 eXtended WN 550,922 Co-occurring relations from SemCor 932,008 New KnowNet-5 231,163 New KnowNet-10 689,610 New KnowNet-15 1,378,286 New KnowNet-20 2,358,927 Table 1: Number of synset relations 3 presents the approach we followed for building highly dense and accurate knowledge bases from the Topic Signatures. In section 4, we present the evaluation framework used in this study. Section 5 describes the results when evaluating different versions of KnowNet and finally, section 6 presents some concluding remarks and future work. 2 Topic Signatures Topic Signatures (TS) are word vectors related to a particular topic (Lin and Hovy, 2000). Topic Signatures are built by retrieving context words of a target topic from a large corpora. This study considers word senses as topics. Basically, the acquisition of TS consists of: •acquiring the best possible corpus examples for a particular word sense (usually characterizing each word sense as a query and performing a search on the corpus for those examples that best match the queries) •building the TS by selecting the context words that best represent the word sense from the selected corpora. The Topic Signatures acquired from the web (hereinafter TSWEB) constitutes one of the largest semantic resource available with around 100 million relations (between synsets and words) (Agirre and de Lacalle, 2004). Inspired by the work of (Leacock et al., 1998), TSWEB was constructed using monosemous relatives from WN (synonyms, hypernyms, direct and indirect hyponyms, and siblings), querying Google and retrieving up to one thousand snippets per query (that is, a word sense), extracting the salient words with distinctive frequency using TFIDF. Thus, TSWEB consist of large ordered lists of words with weights associated to the polysemous nouns of WN1.6. The number of constructed topic signatures is 35,250 with an average size per signature of 6,877 words. tammany#n 0.0319 federalist#n 0.0315 whig#n 0.0300 missionary#j 0.0229 Democratic#n 0.0218 nazi#j 0.0202 republican#n 0.0189 constitutional#n 0.0186 conservative#j 0.0148 socialist#n 0.0140 Table 2: TS of party#n#1 (first 10 out of 12,890 total words) When evaluating TSWEB, we used at maximum the first 700 words while for building KnowNet we used at maximum the first 20 words. For example, table 2 presents the first words (lemmas and part-of-speech) and weights of the Topic Signature acquired for party#n#14. 3 Building highly connected and dense knowledge bases We acquired by fully automatic means highly connected and dense knowledge bases by disambiguating small portions of the Topic Signatures obtained from the web, increasing the total number of semantic relations from less than one million (the current number of available relations) to millions of new and accurate semantic relations between synsets. We applied a knowledge–based all–words Word Sense Disambiguation algorithm to the Topic Signatures for deriving a sense vector from each word vector. 3.1 SSI-Dijkstra We have implemented a version of the Structural Semantic Interconnections algorithm (SSI), a knowledge-based iterative approach to Word Sense Disambiguation (Navigli and Velardi, 2005). The SSI algorithm is very simple and consists of an initialization step and a set of iterative steps (see algorithm 1). Given W, an ordered list of words to be disambiguated, the SSI algorithm performs as follows. During the initialization step, all monosemous words are included into the set I of already interpreted words, and the polysemous words are included in P (all of them pending to be disambiguated). At each step, the set I is used to disambiguate one word of P, selecting the word sense which is closer to the set I of already disam4This format stands for word#pos#sense. biguated words. Once a sense is selected, the word sense is removed from P and included into I. The algorithm finishes when no more pending words remain in P. Algorithm 1 SSI-Dijkstra Algorithm SSI (T: list of terms) for each {t∈T}do I[t] = ∅ if t is monosemous then I[t] := the only sense of t else P:= P∪ {t} end if end for repeat P0:= P for each {t∈P}do BestSense := ∅ MaxV alue := 0 for each {sense s of t}do W[s] := 0 N[s] := 0 for each {sense s0∈I}do w:= DijsktraShortestPath(s, s0) if w > 0then W[s] := W[s] + (1/w) N[s] := N[s]+1 end if end for if N[s]>0then NewV alue := W[s]/N[s] if NewV alue > MaxV alue then MaxV alue := NewV alue BestSense := s end if end if end for if MaxV alue > 0then I[t] := BestSense P:= P\ {t} end if end for until P6=P0 return (I, P); Initially, the list I of interpreted words should include the senses of the monosemous words in W, or a fixed set of word senses5. However, when dis5If no monosemous words are found or if no initial senses are provided, the algorithm could make an initial guess based on the most probable sense of the less ambiguous word of W. ambiguating a TS of a word sense s(for instance party#n#1), the list I already includes s. In order to measure the proximity of one synset to the rest of synsets of I, we use part of the knowledge already available to build a very large connected graph with 99,635 nodes (synsets) and 636,077 edges. This graph includes the set of direct relations between synsets gathered from WordNet and eXtended WordNet. On that graph, we used a very efficient graph library, BoostGraph6to compute the Dijkstra algorithm. The Dijkstra algorithm is a greedy algorithm for computing the shortest path distance between one node an the rest of nodes of a graph. In that way, we can compute very efficiently the shortest distance between any two given nodes of a graph. We call this version of the SSI algorithm, SSI-Dijkstra. SSI-Dijkstra has very interesting properties. For instance, it always provides the minimum distance between two synsets. That is, the algorithm always provides an answer being the minimum distance close or far. In contrast, the original SSI algorithm not always provides a path distance because it depends on a predefined grammar of semantic relations. In fact, the SSI-Dijkstra algorithm compares the distances between the synsets of a word and all the synsets already interpreted in I. At each step, the SSI-Dijkstra algorithm selects the synset which is closer to I (the set of already interpreted words). Furthermore, this approach is completely language independent. The same graph can be used for any language having words connected to WordNet. 3.2 Building KnowNet We developed KnowNet (KN), a large-scale and extensible knowledge base, by applying SSIDijkstra to each topic signature from TSWEB. We have generated four different versions of KnowNet applying SSI-Dijkstra to only the first 5, 10, 15 and 20 words for each TS. SSI-Dijkstra used only the knowledge present in WordNet and eXtended WordNet which consist of a very large connected graph with 99,635 nodes (synsets) and 636,077 edges (semantic relations). We generated each KnowNet by applying the SSI-Dijkstra algorithm to the whole TSWEB (processing the first words of each of the 35,250 topic signatures). For each TS, we obtained the direct relations from the topic (a word sense) 6http://www.boost.org KB WN+XWN #relations #synsets KN-5 3,1% 231,163 39,864 KN-10 5,0% 689,610 45,817 KN-15 6,9% 1,378,286 48,521 KN-20 8,5% 2,358,927 50,789 Table 3: Size and percentage of overlapping relations between KnowNet versions and WN+XWN to the disambiguated word senses of the TS (for instance, party#n#1–>federalist#n#1), but also the indirect relations between disambiguated words from the TS (for instance, federalist#n#1– >republican#n#1). Finally, we removed symmetric and repeated relations. Table 3 shows the overlaping percentage between each KnowNet and the knowledge contained into WordNet and eXtended WordNet, and the total number of relations and synsets of each resource. For instance, only 8,5% of the total direct relations included into WN+XWN are also present in KnowNet-20. This means that the rest of relations from KnowNet-20 are new. As expected, each KnowNet is very large, ranging from hundreds of thousands to millions of new semantic relations between synsets among increasing sets of synsets. 4 Evaluation framework In order to empirically establish the relative quality of these new semantic resources, we used the evaluation framework of task 16 of SemEval-2007: Evaluation of wide coverage knowledge resources (Cuadros and Rigau, 2007). In this framework all knowledge resources are evaluated on a common WSD task. In particular, we used the noun-sets of the English Lexical Sample task of Senseval-3 and SemEval-2007 exercises which consists of 20 and 35 nouns respectively. All performances are evaluated on the test data using the fine-grained scoring system provided by the organizers. Furthermore, trying to be as neutral as possible with respect to the resources studied, we applied systematically the same disambiguation method to all of them. Recall that our main goal is to establish a fair comparison of the knowledge resources rather than providing the best disambiguation technique for a particular knowledge base. All knowledge bases are evaluated as topic signatures. That is, word vectors with weights associated to a particular synset which are obtained by collecting those word senses appearing in the synsets directly related to the topics. This simple representation tries to be as neutral as possible with respect to the resources used. A common WSD method has been applied to all knowledge resources. A simple word overlapping counting is performed between the topic signature representing a word sense and the test example7. The synset having higher overlapping word counts is selected. In fact, this is a very simple WSD method which only considers the topical information around the word to be disambiguated. Finally, we should remark that the results are not skewed (for instance, for resolving ties) by the most frequent sense in WN or any other statistically predicted knowledge. 4.1 Baselines We have designed a number of baselines in order to establish a complete evaluation framework for comparing the performance of each semantic resource on the English WSD tasks. RANDOM: For each target word, this method selects a random sense. This baseline can be considered as a lower-bound. SEMCOR-MFS: This baseline selects the most frequent sense of the target word in SemCor. WN-MFS: This baseline is obtained by selecting the most frequent sense (the first sense in WN1.6) of the target word. WordNet wordsenses were ranked using SemCor and other senseannotated corpora. Thus, WN-MFS and SemCorMFS are similar, but not equal. TRAIN-MFS: This baseline selects the most frequent sense in the training corpus of the target word. TRAIN: This baseline uses the training corpus to directly build a Topic Signature using TFIDF measure for each word sense and selecting at maximum the first 450 words. Note that in WSD evaluation frameworks, this is a very basic baseline. However, in our evaluation framework, this ”WSD baseline” could be considered as an upper-bound. We do not expect to obtain better topic signatures for a particular sense than from its own annotated corpus. 4.2 Other Large-scale Knowledge Resources In order to measure the relative quality of the new resources, we include in the evaluation a wide 7We also consider those multiword terms appearing in WN. range of large-scale knowledge resources connected to WordNet. WN (Fellbaum, 1998): This resource uses the different direct relations encoded in WN1.6 and WN2.0. We also tested WN2using relations at distance 1 and 2, WN3using relations at distances 1 to 3 and WN4using relations at distances 1 to 4. XWN (Mihalcea and Moldovan, 2001): This resource uses the direct relations encoded in eXtended WN. spBNC (McCarthy, 2001): This resource contains 707,618 selectional preferences acquired for subjects and objects from BNC. spSemCor (Agirre and Martinez, 2002): This resource contains the selectional preferences acquired for subjects and objects from SemCor. MCR (Atserias et al., 2004): This resource integrates the direct relations of WN, XWN and spSemCor. TSSEM (Cuadros et al., 2007): These Topic Signatures have been constructed using SemCor.For each word-sense appearing in SemCor, we gather all sentences for that word sense, building a TS using TFIDF for all word-senses co-occurring in those sentences. 4.3 Integrated Knowledge Resources We also evaluated the performance of the integration (removing duplicated relations) of some of these resources. WN+XWN: This resource integrates the direct relations of WN and XWN. We also tested (WN+XWN)2(using either WN or XWN relations at distances 1 and 2). MCR (Atserias et al., 2004): This resource integrates the direct relations of WN, XWN and spSemCor. WN+XWN+KN-20: This resource integrates the direct relations of WN, XWN and KnowNet20. 5 KnowNet Evaluation We evaluated KnowNet using the same framework explained in section 4. That is, the noun part of the test set from the English Senseval-3 and SemEval2007 English lexical sample tasks. 5.1 Senseval-3 evaluation Table 4 presents ordered by F1 measure, the performance in terms of precision (P), recall (R) and KB P R F1 Av. Size TRAIN 65.1 65.1 65.1 450 TRAIN-MFS 54.5 54.5 54.5 WN-MFS 53.0 53.0 53.0 TSSEM 52.5 52.4 52.4 103 SEMCOR-MFS 49.0 49.1 49.0 MCR245.1 45.1 45.1 26,429 WN+XWN+KN-20 44.8 44.8 44.8 671 MCR 45.3 43.7 44.5 129 KnowNet-20 44.1 44.1 44.1 610 KnowNet-15 43.9 43.9 43.9 339 spSemCor 43.1 38.7 40.8 56 KnowNet-10 40.1 40.0 40.0 154 (WN+XWN)238.5 38.0 38.3 5,730 WN+XWN 40.0 34.2 36.8 74 TSWEB 36.1 35.9 36.0 1,721 XWN 38.8 32.5 35.4 69 KnowNet-5 35.0 35.0 35.0 44 WN335.0 34.7 34.8 503 WN433.2 33.1 33.2 2,346 WN233.1 27.5 30.0 105 spBNC 36.3 25.4 29.9 128 WN 44.9 18.4 26.1 14 RANDOM 19.1 19.1 19.1 Table 4: P, R and F1 fine-grained results for the resources evaluated at Senseval-3, English Lexical Sample Task. F1 measure (F1, harmonic mean of recall and precision) of each knowledge resource on Senseval-3 and the average size of the TS per word-sense. The different KnowNet versions appear marked in bold and the baselines appear in italics. As expected, RANDOM obtains the poorest result. The most frequent senses obtained from SemCor (SEMCOR-MFS) and WN (WN-MFS) are both below the most frequent sense of the training corpus (TRAIN-MFS). However, all of them are far below to the Topic Signatures acquired using the training corpus (TRAIN). The best results are obtained by TSSEM (with F1 of 52.4). The lowest result is obtained by the knowledge directly gathered from WN mainly because of its poor coverage (R of 18.4 and F1 of 26.1). Interestingly, the knowledge integrated in the MCR although partly derived by automatic means performs much better in terms of precision, recall and F1 measures than using them separately (F1 with 18.4 points higher than WN, 9.1 than XWN and 3.7 than spSemCor). Despite its small size, the resources derived from SemCor obtain better results than its counterparts using much larger corpora (TSSEM vs. TSWEB and spSemCor vs. spBNC). Regarding the baselines, all knowledge resources surpass RANDOM, but none achieves neither WN-MFS, TRAIN-MFS nor TRAIN. Only TSSEM obtains better results than SEMCOR-MFS and is very close to the most frequent sense of WN (WN-MFS) and the training (TRAIN-MFS). Regarding the expansions and combinations, the performance of WN is improved using words at distances up to 2, and up to 3, but it decreases using distances up to 4. Interestingly, none of these WN expansions achieve the results of XWN. Finally, (WN+XWN)2performs better than WN+XWN and MCR2slightly better than MCR8. The different versions of KnowNet consistently obtain better performances as they increase the window size of processed words of TSWEB. As expected, KnowNet-5 obtain the lower results. However, it performs better than WN (and all its extensions) and spBNC. Interestingly, from KnowNet-10, all KnowNet versions surpass the knowledge resources used for their construction (WN, XWN, TSWEB and WN+XWN). In fact, KnowNet-10 also outperforms (WN+XWN)2with much more relations per sense. Also interesting is that KnowNet-10 and KnowNet-20 obtain better performance than spSemCor which was derived from annotated corpora. However, KnowNet-20 only performs slightly better than KnowNet-15 while almost doubling the number of relations. These initial results seem to be very promising. If we do not consider the resources derived from manually sense annotated data (spSemCor, MCR, TSSEM, etc.), KnowNet-10 performs better that any knowledge resource derived by manual or automatic means. In fact, KnowNet-15 and KnowNet-20 outperforms spSemCor which was derived from manually annotated corpora. This is a very interesting result since these KnowNet versions have been derived only with the knowledge coming from WN and the web (that is, TSWEB), and WN and XWN as a knowledge source for SSIDijkstra9. Regarding the integration of resources, WN+XWN+KN-20 performs better than MCR and similarly to MCR2(having less than 50 times its size). Also interesting is that WN+XWN+KN20 have better performance than their individual resources, indicating a complementary knowledge. In fact, WN+XWN+KN-20 performs much better than the resources from which it derives (WN, XWN and TSWEB). 8No further distances have been tested 9eXtended WordNet only has 17,185 manually labeled senses. KB P R F1 Av. Size TRAIN 87.6 87.6 87.6 450 TRAIN-MFS 81.2 79.6 80.4 WN-MFS 66.2 59.9 62.9 WN+XWN+KN-20 53.0 53.0 53.0 627 (WN+XWN)254.9 51.1 52.9 5,153 TSWEB 54.8 47.8 51.0 700 KnowNet-20 49.5 46.1 47.7 561 KnowNet-15 47.0 43.5 45.2 308 XWN 50.1 39.8 44.4 96 KnowNet-10 44.0 39.8 41.8 139 WN+XWN 45.4 36.8 40.7 101 SEMCOR-MFS 42.4 38.4 40.3 MCR 40.2 35.5 37.7 149 TSSEM 35.1 32.7 33.9 428 KnowNet-5 35.5 26.5 30.3 41 MCR232.4 29.5 30.9 24,896 WN329.3 26.3 27.7 584 RANDOM 27.4 27.4 27.4 WN225.9 27.4 26.6 72 spSemCor 31.4 23.0 26.5 51.0 WN426.1 23.9 24.9 2,710 WN 36.8 16.1 22.4 13 spBNC 24.4 18.1 20.8 290 Table 5: P, R and F1 fine-grained results for the resources evaluated at SemEval-2007, English Lexical Sample Task. 5.2 SemEval-2007 evaluation Table 5 presents ordered by F1 measure, the performance in terms of precision (P), recall (R) and F1 measure (F1) of each knowledge resource on SemEval-2007 and its average size of the TS per word-sense10. Again, the different KnowNet versions appear marked in bold and the baselines appear in italics. As in the previous evaluation, RANDOM obtains the poorest result. The most frequent senses obtained from SemCor (SEMCOR-MFS) and WN (WN-MFS) are both far below the most frequent sense of the training corpus (TRAIN-MFS), and all of them are below the Topic Signatures acquired using the training corpus (TRAIN). Interestingly, on SemEval-2007, all the knowledge resources behave differently. Now, the best individual results are obtained by TSWEB, while in this case TSSEM obtains very modest results. The lowest result is obtained by the knowledge encoded in spBNC. Regarding the baselines, spBNC, WN (and also WN2and WN4) and spSemCor do not surpass RANDOM, and none achieves neither WN-MFS, TRAIN-MFS nor TRAIN. Now, WN+XWN, XWN, TSWEB and (WN+XWN)2obtain better 10The average size is different with respect Senseval-3 because the words selected for this task are different results than SEMCOR-MFS but far below the most frequent sense of WN (WN-MFS) and the training (TRAIN-MFS). Regarding other expansions and combinations, the performance of WN is improved using words at distances up to 2, and up to 3, but it decreases using distances up to 4. Again, none of these WN expansions achieve the results of XWN. Finally, (WN+XWN)2performs better than WN+XWN and MCR2slightly better than MCR11. On SemEval-2007, the different versions of KnowNet consistently obtain better performances as they incease the window size of processed words of TSWEB. As expected, KnowNet-5 obtain the lower results. However, it performs better than spBNC, WN (and all its extensions), spSemCor and MCR2. This time, all KnowNet versions perform worse than TSWEB. However, as in the previous evaluation, KnowNet-10 outperforms WN+XWN, and this time, also TSSEM and the MCR, with much more relations per sense. Also interesting is that from KnowNet-10, all KnowNet versions perform better than the resources derived from manually sense annotated corpora (spSemCor, MCR, TSSEM, etc.). Regarding the integration of resources, WN+XWN+KN-20 performs better than any knowledge resource derived by manual or automatic means. Again, it is interesting to note that WN+XWN+KN-20 have better performance than their individual resources, indicating a complementary knowledge. In fact, WN+XWN+KN-20 performs much better than the resources from which it derives (WN, XWN and TSWEB). 5.3 Discussion When comparing the ranking of the different knowledge resources, the different versions of KnowNet seem to be more robust and stable across corpora changes. For instance, in both evaluation frameworks (Senseval-3 and SemEval2007), KnowNet-20 ranks 5th and 4th, respectively ((WN+XWN)2ranks 8th and 2nd, TSSEM ranks 1st and 10th, MCR ranks 4th and 9th, TSWEB ranks 11th and 3rd, etc.). In fact, WN+XWN+KN-20 ranks 3rd and 1st, respectively. 11No further distances have been tested 6 Conclusions and future research It is our belief, that accurate semantic processing (such as WSD) would rely not only on sophisticated algorithms but on knowledge intensive approaches. The results presented in this paper suggests that much more research on acquiring and using large-scale semantic resources should be addressed. The knowledge acquisition bottleneck problem is particularly acute for open domain (and also domain specific) semantic processing. The initial results obtained for the different versions of KnowNet seem to be a major step towards the autonomous acquisition of knowledge from raw corpora, since they are several times larger than the available knowledge resources which encode relations between synsets, and the knowledge they contain outperform any other resource when is empirically evaluated in a common framework. It remains for future research the evaluation of these KnowNet versions in combination with other large-scale semantic resources or in a cross-lingual setting. Acknowledgments We want to thank Aitor Soroa for his technical support and the anonymous reviewers for their comments. This work has been supported by KNOW (TIN2006-15049-C03-01) and KYOTO (ICT-2007-211423). References Agirre, E. and O. Lopez de Lacalle. 2004. Publicly available topic signatures for all wordnet nominal senses. In Proceedings of LREC, Lisbon, Portugal. Agirre, E. and D. Martinez. 2001. Learning classto-class selectional preferences. In Proceedings of CoNLL, Toulouse, France. Agirre, E. and D. Martinez. 2002. Integrating selectional preferences in wordnet. In Proceedings of GWC, Mysore, India. ´ Alvez, J., J. Atserias, J. Carrera, S. Climent, A. Oliver, and G. Rigau. 2008. Consistent annotation of eurowordnet with the top concept ontology. In Proceedings of Fourth International WordNet Conference (GWC’08). Atserias, J., L. Villarejo, G. Rigau, E. Agirre, J. Carroll, B. Magnini, and Piek Vossen. 2004. The meaning multilingual central repository. In Proceedings of GWC, Brno, Czech Republic. Cuadros, M. and G. Rigau. 2006. Quality assessment of large scale knowledge resources. In Proceedings of the EMNLP. Cuadros, M. and G. Rigau. 2007. Semeval-2007 task 16: Evaluation of wide coverage knowledge resources. In Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007). Cuadros, M., G. Rigau, and M. Castillo. 2007. Evaluating large-scale knowledge resources across languages. In Proceedings of RANLP. Fellbaum, C., editor. 1998. WordNet. An Electronic Lexical Database. The MIT Press. Leacock, C., M. Chodorow, and G. Miller. 1998. Using Corpus Statistics and WordNet Relations for Sense Identification. Computational Linguistics, 24(1):147–166. Lin, C. and E. Hovy. 2000. The automated acquisition of topic signatures for text summarization. In Proceedings of COLING. Strasbourg, France. Magnini, B. and G. Cavagli` a. 2000. Integrating subject field codes into wordnet. In Proceedings of LREC, Athens. Greece. McCarthy, D. 2001. Lexical Acquisition at the SyntaxSemantics Interface: Diathesis Aternations, Subcategorization Frames and Selectional Preferences. Ph.D. thesis, University of Sussex. Mihalcea, R. and D. Moldovan. 2001. extended wordnet: Progress report. In Proceedings of NAACL Workshop on WordNet and Other Lexical Resources, Pittsburgh, PA. Navigli, R. and P. Velardi. 2005. Structural semantic interconnections: a knowledge-based approach to word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 27(7):1063–1074. Niles, I. and A. Pease. 2001. Towards a standard upper ontology. In Proceedings of the 2nd International Conference on Formal Ontology in Information Systems (FOIS-2001), pages 17–19. Chris Welty and Barry Smith, eds. Suchanek, Fabian M., Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: A Core of Semantic Knowledge. In 16th international World Wide Web conference (WWW 2007), New York, NY, USA. ACM Press. Vossen, P., editor. 1998. EuroWordNet: A Multilingual Database with Lexical Semantic Networks . Kluwer Academic Publishers .