Towards Constructing Linguistic Ontologies:

Abstract

Cross-language ontology matching methods leverage translation based approach in mapping ontological resources (e.g., concepts) lexicalized in di erent languages. Such methods are largely dependent on the structural information encoded in the matched ontologies. The paper presents an approach for large scale cross-language linguistic ontology matching. In particular, it shows how synsets belonging to two distinct language wordnets can be mapped, by means of machine translation and frequency analysis on word senses. A preliminary experimental analysis of the problem is presented, through which we conduct experiments on existing gold-standard datasets. The results are encouraging and show the feasibility of the approach and demonstrate that the sense-selection task is a crucial step towards high quality mappings. We conclude with our observations and draw our potential future directions.

Keywords

• ontology matching
• linguistic ontology
• wordnet
• translation

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The last decades witnessed remarkable e orts in the development of ontologies that aim at capturing relationships between words and concepts aiming to represent a commonsense knowledge in natural languages, and hence making the semantic connections between words in di erent languages explicit, such ontologies are often called wordnets or linguistic ontologies [ 11 ][ 13 ]. One such commonly used linguistic ontology is the English WordNet [ 4 ].

The success of WordNet motivated the construction of similarly structured lexicons for individual and multiple languages (multi-language lexicons). These include, among others, EuroWordNet [ 27 ], MultiWordNet [ 19 ]. Recently, wordnets for many languages have been constructed under the guidelines of Global WordNet Association. However, the manual method of constructing these ontologies is expensive and time-consuming. Automatic construction of wordnets is another method for building and linking wordnets. De Melo and Weikum [ 5 ] used bilingual dictionaries to automatically provide equivalents in various languages for the English WordNet synsets. However, translation tools might remove the language barrier but not necessarily the socio-cultural one [ 2 ]. The main challenge is to nd the appropriate word sense of the translated word [ 17 ]. To enable semantic interoperability across di erent languages, ontology based cross-language matching was explored in the last years [ 23 ]. However, the culturallinguistic barriers [ 8 ][ 2 ] still need to be overcome in terms of the mapping process and techniques, as well as to formally de ne the semantic of mappings that align concepts lexicalized across di erent natural languages[ 1 ]. In addition, languages do not cover exactly the same part of the lexicon and, even where they have common concepts, these concepts are lexicalized di erently [ 11 ]. One of the problems in creating linguistic ontologies via cross-language matching approach is that one needs to map an unstructured or a weakly structured lexicon, to a structured lexicon [ 13 ]. This introduce an extremely di cult and challenging matching problem for many reasons: (i) the lack of structural information, which is often used in matching techniques [ 23 ] (ii) the large mapping space (e.g., WordNet(3.0) has 117659 concepts), (iii) the quality (uncertainty) and coverage of the translation resources. We need to assume that all sense distinctions given by the translations are correct and available in a translation resource, and (iv) the words ambiguity, that is, we need to select the most commonly and accepted meaning (sense) of a word [ 17 ]. The di culties of this task rise due to the words polysemy (if a word can convey more than one meaning) and synonymy (if two or more words can convey the same meaning).

The research presented here aims to contribute to the construction of large linguistic ontologies [ 12 ]. The idea is to semi-automate this process by rst matching source language concepts (synsets) to an established wordnet concepts in a target language, and then deriving the semantic relations among the source concepts using relations among concepts in the target wordnet. We argue that, the resultant relations can provide an initial set of relations that can be manually validated and corrected [ 1 ]. Before selecting and/or extending the more appropriate existing cross-language mapping techniques, we need to be able to compare alternative methods and to assess the quality of their output. Contribution.

In this paper I introduce a semi-automatic mapping framework that tries to map synsets in di erent languages by combining translation tools and word sense disambiguation (WSD) into a hybrid task. I de ne a mapping algorithm for constructing linguistic ontologies, through mapping unstructured concepts to structured one, as a maximization problem [ 26 ] that retrieve top k mappings from a set of sorted candidate mappings.

Since the creation of wordnets uses mappings among concepts expressed (lexicalized) in di erent languages, one of the research areas more relevant to the ontology creation problem is cross-language ontology matching (CLOM) . CLOM techniques [ 6 ] can play a crucial role in bootstrapping the creation of large linguistic ontologies and, for analogous reasons, in enriching existent ontologies. We also remark that the above considerations are general and can be reused for di erent languages. We demonstrate this by considering benchmark datasets for di erent pairs of languages. In particular, we discuss the results of investigating and assessing the mapping of an unstructured set of synsets in one language to an existent wordnet in di erent language. The proposed algorithm leverages on translation tools and tries to map synsets lexicalized in one language (e.g., Arabic) to their correspondence synsets in other language (e.g., English), di erent translating settings were considered to investigate the appropriate translation methods for obtaining the correct translation. the algorithm ranks the translated synsets in order to select the most appropriate senses. This is followed by an experimental analysis and discussion. By this experiment we aim to respond on the following questions: (i) what is the best translation tool in term of coverage and correctness. (ii) what is the impact of the correct translation on the sense disambiguation task. (iii) what is the impact of providing (partial/semi)structural knowledge among the source synsets. This paper will focus on the rst two questions while leaving the third question for more investigation in the future work.

The rest of the paper is structured as follows. Section 2 overviews the related work. Section 3 illustrates the mapping algorithm. In section 4 the experiment, the evaluation settings, and a discussion about the obtained results are given. In section 5 we conclude and outline the future steps. 2

Background and State of the Art

The last decade witnessed a wide range of ontology matching methods which have been successfully developed and evaluated in OAEI [ 23 ]. The majority of the proposed techniques in these systems have mainly focused on mapping between ontological resources that are lexicalized in the same natural language (socalled, mono-language ontology matching, MOM). However, methods developed for MOM systems cannot directly access semantic information when ontologies are lexicalized in di erent natural languages [ 6 ]. There is a need for a method that automatically reconciles information when ontologies are lexicalized in different natural languages [ 8 ].

Manual mapping (by experts) was used to generate and review the mappings quality [ 14 ]. The mappings generated by such approaches are likely to be accurate and reliable. However, this can be a resource consuming process specially for maintaining large and complex ontologies. An unsupervised method was suggested based on (non-parallel) bilingual corpora [ 16 ].This approach, as it happens with most unsupervised learning methods, heavily relies on corpus statistics. De Melo and Weikum [ 5 ] constructed a binary classi cation learning problem to automatically determine the appropriate senses among the translated candidates. To create their classi er they used several scores that take into account structural properties as well as semantic relatedness and corpus frequency information where used. The authors claimed that, this technique is imperfect in terms of their quality and coverage of language-speci c phenomena [ 5 ].

In general, to resolve the cross-lingual issue, a translation based approach [ 6 ] is considered in order to transform the CLOM problem into a MOM one. These systems deeply leverage the structural information derived from the mapped ontologies. Furthermore, Spohr et al. [ 25 ] approach, like all supervised learning methods, requires a signi cant number of labeled training samples and welldesigned features to achieve good performance. Another interesting work for resolving the cross-lingual issue exploits Wikipedia, a collaborative and multilingual resource of world and linguistic knowledge. Hassan and Mihalcea [ 9 ] developed a cross-lingual version of Explicit Semantic Analysis (ESA, [ 7 ]), CL-ESA. Similar works that used ESA in linking concepts across di erent languages are also presented in [ 3 ] and [ 15 ]. WikiMatch, a matching system presented in [ 10 ], searches Wikipedia pages (articles) titles for a given term (e.g., the ontology labels, and comments) and retrieve all language links describing the term, making use of the inter-lingual links between Wikipedia pages. However, such approaches are limited and highly dependent on the lexical coverages which are provided by Wikipedia inter-lingual links.

A notable approach for disambiguating and linking cross-lingual senses was presented in BabelNet [ 18 ]. Since Wikipedia inter-lingual links do not exist for all Wikipedia pages, Navigli and Ponzetto [ 18 ] proposed a context-translation approach. They automatically translate (using Google translator) a set of English sense-tagged sentences. After they applied the automatic translation, the most frequent translation is detected and included as a variant for the mapped senses in the given language. However, it is not clear if they employed any speci c NLP techniques in this process, or if they aligned the translated words with words in the original (English) sentence (cf, the word aligner KNOWA [ 20 ]). Moreover, such frequency counts do not necessarily preserve the part of speech of the translated words. They only translated Wikipedia entries whose lemmas (page titles) do not refer to named entities 1. They contextless translated lemmas in WordNet which are monosemous (i.e., words that have only one meaning), they simply included the translations returned by Google Translate. They [ 18 ] reported that the majority of the translated senses are monosemous.

BabelNet [ 18 ] mappings were evaluated against manually mapped random sample and gold-standard datasets. However, it is important to understand if the obtained mappings were achieved through the context- or contextless-translation approaches. More importantly, the monosemous and polysemy translated senses were not quanti ed in their evaluation, noticing that monosemous senses form a substantial large portion of the evaluated wordnets. It is important to measure both the contribution and the quality of the context-translation approach. For instance, the Italian wordnet contains about 66% monosemous senses, while in BabelNet they covered less than 53% and 74% of the Italian WordNet's [ 19 ] senses (words) and synsets, respectively. They determined the coverage as the percentage of gold-standard synsets that share a term (one synonym overlap) with the correspondence synset obtained from the mapping algorithm. This does not necessarily implies a high quality mappings. It worth noting that BabelNet context-translation approach covers about 25% of the Arabic WordNet's [ 22 ] words (which was used as benchmark). 1 About 90% of Wikipedia pages are named entities which were included directly in BabelNet without translation [ 18 ] 3

Mapping Algorithm

Given a pair of wordnets wnL1 and wnL2 in di erent languages (L1 6= L2), respectively called the source and target wordnet (we call it source wordnet although it has no relations among the synsets), the mapping algorithm tries to nd for each synset synL1 2 wnL1 an equivalent synset from the target wordnet synL2 2 wnL2 , such that it maximizes the probability of providing an appropriate correspondence to synL1 . In order to map the synsets, we make use of the mapping algorithm whose pseudocode is presented in Algorithm 1. The following steps are performed for each synset synL1 2 wnL1 : line 3: the translation function trans lookup all the possible translations in the target language for each word in the source synset, wiL1 2 syniL1 . line 4: the sense function (sense(wL) = fsyn1L; ::; synnLg) lookup all the candidate senses, candSenses, from the target wordnet for each translated word. line 5: the rank function accepts the candSenses and returns a ranked senses (rSenses) ordered by the most appropriate senses, this is performed by counting the frequency of each sense in the candSenses set, the rank function also gives a higher priority (weight) to the synsets which obtained from translating di erent words in the source synset (i.e., synonym words give the same translation). line 6: the select function selects the top k mappings (a set of mappings) from the (rSenses). For a single W SD mapping task k = 1. If a tie occurs, the senses will be selected based on the higher ratio between the number of translated words and the number of synonym words in the candidate senses (i.e., the ratio between the size of the mapped synsets), else it is randomly selected. As a result of executing the algorithm, a set of ranked mappings is returned. 4

Experiment

Wordnets. Three wordnets have been used in the experiment; the Arabic WordNet (2.0) [ 22 ], the Italian component of the MultiWordNet [ 19 ], and the English WordNet (3.0) [ 4 ]. The wordnets, respectively, have 11214, 34728 and 117659 synsets, such that each wordnet has 15964, 40178 and 155287 words, this forms 23481, 61558 and 206941 total word senses, respectively for each wordnet. The Arabic WordNet and the Italian WordNet were used to benchmarked the mapping algorithm. Moreover, details on each wordnet are provided. - The Arabic WordNet (ArWN) consists of 11214 Arabic synsets which were constructed from 15964 vocalized Arabic words (13867 unvocalized word; i.e., without diacritics). The ArWN authors [ 22 ] mapped the Arabic synsets to their equivalent synsets in the English WordNet (2.0). As a result 10413 synsets have been manually mapped to the correspondent English WordNet (EnWN) synsets. Out of the 10413 Arabic-English mapped synsets, 54 mappings do not have Arabic lexicon ( Arabic concepts without lexicalization, such concepts were considered as lexical gaps [ 27 ]), and 10 synsets have no English correspondence synsets in EnWN (3.0) due to the part of speech mismatching. Overall, the resulted mappings are 10349 Arabic-English equivalent synsets mappings. - The Italian WordNet (ItWN)2 authors [ 19 ] have manually mapped the Italian synsets to their equivalent synsets in the EnWN (1.6). Later under the Open Multilingual Wordnet initiative the ItWN was mapped to EnWN (3.0). As a result we have 34728 Italian-English equivalent synsets mappings including 997 ( lexical gaps), mappings do not have the correspondence Italian lexicon. Overall, the resulted mappings were 33731 Italian-English equivalent synsets mappings. Evaluation. The goal is to disambiguate the senses and to nd the appropriate mappings between synsets lexicalized in di erent languages. The disambiguation task was evaluated using evaluation measures borrowed from the information retrieval eld [ 17 ]. The coverage is de ned as the percentage of mappings in the test set for which the mapping method provides a sense mapping. The precision of the mapping method is computed as the percentage of the correct mappings given by the method, this re ects how good are the mappings which obtained by the assessed mapping method. The recall is de ned as the ratio between the number of the correct mappings provided by the method being assessed, and the total number of mappings to be provided (number of mappings in the test set; the benchmark dataset). The F measure is also used, the F-measure is de ned as the weighted harmonic mean of precision and recall.

In addition, a Baseline (lower bound) setting was used; the rst sense heuristic was used to measure the lower bound of the experiment. Note that in general most of the WSD algorithms hardly can beat these bounds [ 17 ]. The upper bound (having the correct translations, Oracle translation) was also computed in order to specify the highest expected performance of the proposed approach. Translation Methods. In the experiment we used di erent resource for translation. (i) Machine translation tools : Google translator was used to obtain the English translation for all the Arabic and Italian words in the benchmark dataset. (ii) Machine readable dictionaries : Sina dictionary was used for Arabic to English translations, Sina dictionary is a result of the ongoing Arabic Ontology project [ 12 ], the dictionary was constructed by integrating several specialized and general domain dictionaries. An Italian to English translation dictionary should be considered in the future work. (iii) Oracle translation: An oracle is a hypothetic system which is always supposed to know the correct answer (i.e., the correct translation). We used the translations provided in the benchmark wordnets as an oracle (correct translation). An oracle translation was used to demonstrate 2 http://multiwordnet.fbk.eu/ the upper bounds. Moreover, (iv) an extended-oracle translation was obtained for the Arabic-English translations by considering all the synonyms of the translated word, not only the translations provided in the ArWN. For the Italian words one can only obtain the extend-oracle translation from the ItWN dataset. Finally,(v) all dictionaries : the translations above were combined to investigate the di erent translation resources accuracy.

Results. The results are reported in Figure 1, the "Experiment" column species the translation method. The evaluation measures were reported, the reported measures evaluate (as %) if the equivalent mappings are among the top k mappings, k 2 [1; 100]. The lower bounds (baseline) experiments were also reported. In Figure 2, four variants that exploit the structural information of the target wordnet were considered to select the equivalent mappings: (1)isEquivalent (isCorrect): the correct equivalent mappings appear among the top k candidate synsets. (2)isHypernym: the candidate synset is a hyponym of the correct mapping. (3)hasHypernym (or isHyponym): the hypernym of the candidate synset is an equivalent mapping. (4)isSister : the candidate synset is a sister node of a equivalent mapping (has the same hypernym synset). Figure 2, also shows the upper and lower bounds, and the precision of the obtained mappings using the Google translator for the ArWN synsets (Figure 1, experiment No.1). Discussion. In experiment No.2, (Sina & Google translation) although it has a similar coverage to (experiment No.1) about 100 mappings more were covereddue to the correct translations by using Sina dictionary. From experiment No.3 (Oracle translation) we can notice that the missed mappings are only the lexical gaps synsets. From experiment No.4 (Extended oracle translation) the important observation is that as more accurate translation we have, the better we can rank and select the candidate senses, that is, the di erent between using the oracle translation and adding the synonyms words is obtaining the equivalent mappings with lower value of k.

Experiment No.5 (All dictionaries ) highlights the fact that even we have the correct translations; the existence of inaccurate translations (by combining Google translations and the domain speci c translations in Sina dictionary) introduced some noises and rise the ambiguity. This ranked the correct sense in lower positions, and increases the needed value of k to nd the correct mappings. This means it is important to lter and discriminate the incorrect translations at rst and then perform the ranking and selection steps. In the Baseline experiments all the performed experiments outperformed the lower bound settings. Having the correct translation is an important step, still selecting the correct senses is a crucial task; One can notice from the results that although we have high (about 100%) coverage, still we need to consider a high value of k to obtain these correct mappings. In fact, the goodness and the better performance of the mapping algorithm is to provide the correct mappings while minimizing the value of k, and this depends on the rank function. Moreover, considering the structural information in the target wordnet (EnWN) improves the results. For instance, in experiment No.1 (Google dictionary) considering the neighbor nodes (e.g.,isSister ) improves the results around 10% for small k values, k 2 [5; 20]. For that, we believe that exploiting the structural information (e.g., graph based and the similarity based approaches [ 21 ]) is expected to help in ranking the correct senses in higher positions. Considering the candidate synsets hypernyms (hasHypernym plot, Figure2) is also expected to improve the results. Error Analysis. The main reasons for missing some mappings can be divided into the following errors: (a) Incorrect translation, number of mappings were missed due to the incorrect translation, this can be divided into 4 errors categories (i) compound words, (ii) named entity, (iii) translation (word) do not exist in WordNet, and (iv) bad translation, by bad translation we mean that Google translator only re-spell the source word in English alphabet. For instance, regarding the ArWN-EnWN mappings, out of the 988 missing mappings (about 10% of the mappings) 760 are compound (one) word synsets, and 33 mappings are named entity synsets. Moreover, 799 mappings do not have an equivalent EnWN lemma, and 189 mappings were considered as bad translations. (b) Monosemous words, the ArWN has 4197 one word synsets, and it has 4370 monosemous synsets (all words in the synsets are monosemous). 2111 synsets are compound (one) word synsets. The majority of the compound one words (about 94%) which used in mapping the Arabic and the English synsets are monosemous synsets. Due to this fact, about 10% of the ArWN-EnWN mappings have only one candidate sense, and about 24% have up to 10 candidate senses. Regarding the ItWN-EnWN mappings, about 35% of the mappings have only one candidate sense, this high values is due to the fact that most of the ItWN synsets (about 66%) are monosemous synsets. About 74% synsets have up to 10 candidate senses. By studying the distribution of the rank values for the Google translation for both the ArWN and ItWN, about 85% of the candidate senses were equally ranked with one (the sense appear one time in the candSenses), thus the increment of k retrieves more correct equivalent mappings. (c) Polysemous words, the ArWN has 3641 polysemous words, such that about 30% and 16% of the polysemous synsets are respectively, two and three words synsets. The ItWN has 10366 polysemous words, such that about 64% and 20% of the polysemous synsets are respectively, two and three words synsets. This highlights the fact that most of the benchmark polysemous sysnets are small size (number of words), this make it more di cult to distinguish the correct sense. For instance, in experiment No.4, when the synonym translations were considered (we increased the size of the translated synsets) the raking functions performs better than experiment No.3. 5

Conclusions & Future Works

This paper investigated how synsets from di erent languages can be mapped, especially the impact of translation tools and the selection of candidate synsets for mappings. A cross language mapping algorithm was presented, the algorithm tries to maximizes the probability that mappings with higher rank are considered correct mappings by users, based on the frequency of translated synsets and majority voting approaches. The experiments have demonstrated several outcomes that can summarized as follow: 1. the approach was successfully tested over two di erent pairs of languages, which demonstrates its adoptability across di erent languages. 2. using structural information encoded in the target wordnet improves the sense selection task. 3. combining the translations of Machine Translation (MT) tools with a bilingual dictionary translation improves the results (Figure 1, experiment No.2). 4. the proposed approach outperforms the baseline settings. The upper bounds indicate that there is a space for more improvements in terms of obtaining the correct translations and to better rank the candidate senses. Moreover, features obtained from the MT tools (Google translation) such as the translation score and the synset translations need to be explored in order to lter the correct translations and to better rank the candidate senses. In addition, NLP techniques (e.g., stemming, headword extraction, ..etc) are expected to improve the MT coverage, and obtain more candidate senses (instead of using pure translation-lookup and word-sense exact matching).

Currently I am planning to consider other languages datasets, and to investigate the construction of partial structural source synsets and its impact on the mapping algorithm inspired by the work presented in [ 21 ]. Another interesting direction is to crowdsource the construction process by providing the workers with the top k mappings [ 24 ], at the same time this should simulates the majority of speakers agreement [ 1 ].

Acknowledgments. This work was funded by EU FP7 SIERA project.

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