When linguistics meets web technologies. Recent advances in modelling linguistic linked data

This article provides an up-to-date and comprehensive survey of models (including vocabularies, taxonomies and ontologies) used for representing linguistic linked data (LLD). It focuses on the latest developments in the area and both builds upon and complements previous works covering similar territory. The article begins with an overview of recent trends which have had an impact on linked data models and vocabularies, such as the growing influence of the FAIR guidelines, the funding of several major projects in which LLD is a key component, and the increasing importance of the relationship of the digital humanities with LLD. Next, we give an overview of some of the most well known vocabularies and models in LLD. After this we look at some of the latest developments in community standards and initiatives such as OntoLex-Lemon as well as recent work which has been in carried out in corpora and annotation and LLD including a discussion of the LLD metadata vocabularies META-SHARE and lime and language identifiers. In the following part of the paper we look at work which has been realised in a number of recent projects and which has a significant impact on LLD vocabularies and models

Wiktionary: The Metalexicographic and the Natural Language Processing Perspective

Dictionaries are the main reference works for our understanding of language. They are used by humans and likewise by computational methods. So far, the compilation of dictionaries has almost exclusively been the profession of expert lexicographers. The ease of collaboration on the Web and the rising initiatives of collecting open-licensed knowledge, such as in Wikipedia, caused a new type of dictionary that is voluntarily created by large communities of Web users. This collaborative construction approach presents a new paradigm for lexicography that poses new research questions to dictionary research on the one hand and provides a very valuable knowledge source for natural language processing applications on the other hand. The subject of our research is Wiktionary, which is currently the largest collaboratively constructed dictionary project. In the first part of this thesis, we study Wiktionary from the metalexicographic perspective. Metalexicography is the scientific study of lexicography including the analysis and criticism of dictionaries and lexicographic processes. To this end, we discuss three contributions related to this area of research: (i) We first provide a detailed analysis of Wiktionary and its various language editions and dictionary structures. (ii) We then analyze the collaborative construction process of Wiktionary. Our results show that the traditional phases of the lexicographic process do not apply well to Wiktionary, which is why we propose a novel process description that is based on the frequent and continual revision and discussion of the dictionary articles and the lexicographic instructions. (iii) We perform a large-scale quantitative comparison of Wiktionary and a number of other dictionaries regarding the covered languages, lexical entries, word senses, pragmatic labels, lexical relations, and translations. We conclude the metalexicographic perspective by finding that the collaborative Wiktionary is not an appropriate replacement for expert-built dictionaries due to its inconsistencies, quality flaws, one-fits-all-approach, and strong dependence on expert-built dictionaries. However, Wiktionary's rapid and continual growth, its high coverage of languages, newly coined words, domain-specific vocabulary and non-standard language varieties, as well as the kind of evidence based on the authors' intuition provide promising opportunities for both lexicography and natural language processing. In particular, we find that Wiktionary and expert-built wordnets and thesauri contain largely complementary entries. In the second part of the thesis, we study Wiktionary from the natural language processing perspective with the aim of making available its linguistic knowledge for computational applications. Such applications require vast amounts of structured data with high quality. Expert-built resources have been found to suffer from insufficient coverage and high construction and maintenance cost, whereas fully automatic extraction from corpora or the Web often yields resources of limited quality. Collaboratively built encyclopedias present a viable solution, but do not cover well linguistically oriented knowledge as it is found in dictionaries. That is why we propose extracting linguistic knowledge from Wiktionary, which we achieve by the following three main contributions: (i) We propose the novel multilingual ontology OntoWiktionary that is created by extracting and harmonizing the weakly structured dictionary articles in Wiktionary. A particular challenge in this process is the ambiguity of semantic relations and translations, which we resolve by automatic word sense disambiguation methods. (ii) We automatically align Wiktionary with WordNet 3.0 at the word sense level. The largely complementary information from the two dictionaries yields an aligned resource with higher coverage and an enriched representation of word senses. (iii) We represent Wiktionary according to the ISO standard Lexical Markup Framework, which we adapt to the peculiarities of collaborative dictionaries. This standardized representation is of great importance for fostering the interoperability of resources and hence the dissemination of Wiktionary-based research. To this end, our work presents a foundational step towards the large-scale integrated resource UBY, which facilitates a unified access to a number of standardized dictionaries by means of a shared web interface for human users and an application programming interface for natural language processing applications. A user can, in particular, switch between and combine information from Wiktionary and other dictionaries without completely changing the software. Our final resource and the accompanying datasets and software are publicly available and can be employed for multiple different natural language processing applications. It particularly fills the gap between the small expert-built wordnets and the large amount of encyclopedic knowledge from Wikipedia. We provide a survey of previous works utilizing Wiktionary, and we exemplify the usefulness of our work in two case studies on measuring verb similarity and detecting cross-lingual marketing blunders, which make use of our Wiktionary-based resource and the results of our metalexicographic study. We conclude the thesis by emphasizing the usefulness of collaborative dictionaries when being combined with expert-built resources, which bears much unused potential

Semantic Enrichment of Ontology Mappings

Schema and ontology matching play an important part in the field of data integration and semantic web. Given two heterogeneous data sources, meta data matching usually constitutes the first step in the data integration workflow, which refers to the analysis and comparison of two input resources like schemas or ontologies. The result is a list of correspondences between the two schemas or ontologies, which is often called mapping or alignment. Many tools and research approaches have been proposed to automatically determine those correspondences. However, most match tools do not provide any information about the relation type that holds between matching concepts, for the simple but important reason that most common match strategies are too simple and heuristic to allow any sophisticated relation type determination. Knowing the specific type holding between two concepts, e.g., whether they are in an equality, subsumption (is-a) or part-of relation, is very important for advanced data integration tasks, such as ontology merging or ontology evolution. It is also very important for mappings in the biological or biomedical domain, where is-a and part-of relations may exceed the number of equality correspondences by far. Such more expressive mappings allow much better integration results and have scarcely been in the focus of research so far. In this doctoral thesis, the determination of the correspondence types in a given mapping is the focus of interest, which is referred to as semantic mapping enrichment. We introduce and present the mapping enrichment tool STROMA, which obtains a pre-calculated schema or ontology mapping and for each correspondence determines a semantic relation type. In contrast to previous approaches, we will strongly focus on linguistic laws and linguistic insights. By and large, linguistics is the key for precise matching and for the determination of relation types. We will introduce various strategies that make use of these linguistic laws and are able to calculate the semantic type between two matching concepts. The observations and insights gained from this research go far beyond the field of mapping enrichment and can be also applied to schema and ontology matching in general. Since generic strategies have certain limits and may not be able to determine the relation type between more complex concepts, like a laptop and a personal computer, background knowledge plays an important role in this research as well. For example, a thesaurus can help to recognize that these two concepts are in an is-a relation. We will show how background knowledge can be effectively used in this instance, how it is possible to draw conclusions even if a concept is not contained in it, how the relation types in complex paths can be resolved and how time complexity can be reduced by a so-called bidirectional search. The developed techniques go far beyond the background knowledge exploitation of previous approaches, and are now part of the semantic repository SemRep, a flexible and extendable system that combines different lexicographic resources. Further on, we will show how additional lexicographic resources can be developed automatically by parsing Wikipedia articles. The proposed Wikipedia relation extraction approach yields some millions of additional relations, which constitute significant additional knowledge for mapping enrichment. The extracted relations were also added to SemRep, which thus became a comprehensive background knowledge resource. To augment the quality of the repository, different techniques were used to discover and delete irrelevant semantic relations. We could show in several experiments that STROMA obtains very good results w.r.t. relation type detection. In a comparative evaluation, it was able to achieve considerably better results than related applications. This corroborates the overall usefulness and strengths of the implemented strategies, which were developed with particular emphasis on the principles and laws of linguistics

Language technologies for a multilingual Europe

This volume of the series “Translation and Multilingual Natural Language Processing” includes most of the papers presented at the Workshop “Language Technology for a Multilingual Europe”, held at the University of Hamburg on September 27, 2011 in the framework of the conference GSCL 2011 with the topic “Multilingual Resources and Multilingual Applications”, along with several additional contributions. In addition to an overview article on Machine Translation and two contributions on the European initiatives META-NET and Multilingual Web, the volume includes six full research articles. Our intention with this workshop was to bring together various groups concerned with the umbrella topics of multilingualism and language technology, especially multilingual technologies. This encompassed, on the one hand, representatives from research and development in the field of language technologies, and, on the other hand, users from diverse areas such as, among others, industry, administration and funding agencies. The Workshop “Language Technology for a Multilingual Europe” was co-organised by the two GSCL working groups “Text Technology” and “Machine Translation” (http://gscl.info) as well as by META-NET (http://www.meta-net.eu)

Composing Measures for Computing Text Similarity

We present a comprehensive study of computing similarity between texts. We start from the observation that while the concept of similarity is well grounded in psychology, text similarity is much less well-defined in the natural language processing community. We thus define the notion of text similarity and distinguish it from related tasks such as textual entailment and near-duplicate detection. We then identify multiple text dimensions, i.e. characteristics inherent to texts that can be used to judge text similarity, for which we provide empirical evidence. We discuss state-of-the-art text similarity measures previously proposed in the literature, before continuing with a thorough discussion of common evaluation metrics and datasets. Based on the analysis, we devise an architecture which combines text similarity measures in a unified classification framework. We apply our system in two evaluation settings, for which it consistently outperforms prior work and competing systems: (a) an intrinsic evaluation in the context of the Semantic Textual Similarity Task as part of the Semantic Evaluation (SemEval) exercises, and (b) an extrinsic evaluation for the detection of text reuse. As a basis for future work, we introduce DKPro Similarity, an open source software package which streamlines the development of text similarity measures and complete experimental setups

Proceedings of the 15th ISWC workshop on Ontology Matching (OM 2020)

15th International Workshop on Ontology Matching co-located with the 19th International Semantic Web Conference (ISWC 2020)International audienc

Liage de données RDF : évaluation d'approches interlingues

The Semantic Web extends the Web by publishing structured and interlinked data using RDF.An RDF data set is a graph where resources are nodes labelled in natural languages. One of the key challenges of linked data is to be able to discover links across RDF data sets. Given two data sets, equivalent resources should be identified and linked by owl:sameAs links. This problem is particularly difficult when resources are described in different natural languages.This thesis investigates the effectiveness of linguistic resources for interlinking RDF data sets. For this purpose, we introduce a general framework in which each RDF resource is represented as a virtual document containing text information of neighboring nodes. The context of a resource are the labels of the neighboring nodes. Once virtual documents are created, they are projected in the same space in order to be compared. This can be achieved by using machine translation or multilingual lexical resources. Once documents are in the same space, similarity measures to find identical resources are applied. Similarity between elements of this space is taken for similarity between RDF resources.We performed evaluation of cross-lingual techniques within the proposed framework. We experimentally evaluate different methods for linking RDF data. In particular, two strategies are explored: applying machine translation or using references to multilingual resources. Overall, evaluation shows the effectiveness of cross-lingual string-based approaches for linking RDF resources expressed in different languages. The methods have been evaluated on resources in English, Chinese, French and German. The best performance (over 0.90 F-measure) was obtained by the machine translation approach. This shows that the similarity-based method can be successfully applied on RDF resources independently of their type (named entities or thesauri concepts). The best experimental results involving just a pair of languages demonstrated the usefulness of such techniques for interlinking RDF resources cross-lingually.Le Web des données étend le Web en publiant des données structurées et liées en RDF. Un jeu de données RDF est un graphe orienté où les ressources peuvent être des sommets étiquetées dans des langues naturelles. Un des principaux défis est de découvrir les liens entre jeux de données RDF. Étant donnés deux jeux de données, cela consiste à trouver les ressources équivalentes et les lier avec des liens owl:sameAs. Ce problème est particulièrement difficile lorsque les ressources sont décrites dans différentes langues naturelles.Cette thèse étudie l'efficacité des ressources linguistiques pour le liage des données exprimées dans différentes langues. Chaque ressource RDF est représentée comme un document virtuel contenant les informations textuelles des sommets voisins. Les étiquettes des sommets voisins constituent le contexte d'une ressource. Une fois que les documents sont créés, ils sont projetés dans un même espace afin d'être comparés. Ceci peut être réalisé à l'aide de la traduction automatique ou de ressources lexicales multilingues. Une fois que les documents sont dans le même espace, des mesures de similarité sont appliquées afin de trouver les ressources identiques. La similarité entre les documents est prise pour la similarité entre les ressources RDF.Nous évaluons expérimentalement différentes méthodes pour lier les données RDF. En particulier, deux stratégies sont explorées: l'application de la traduction automatique et l'usage des banques de données terminologiques et lexicales multilingues. Dans l'ensemble, l'évaluation montre l'efficacité de ce type d'approches. Les méthodes ont été évaluées sur les ressources en anglais, chinois, français, et allemand. Les meilleurs résultats (F-mesure > 0.90) ont été obtenus par la traduction automatique. L'évaluation montre que la méthode basée sur la similarité peut être appliquée avec succès sur les ressources RDF indépendamment de leur type (entités nommées ou concepts de dictionnaires)