Foreword to the Special Issue: "Towards the Multilingual Web of Data"

We are pleased to introduce this special issue on the topic of “Towards theMultilingualWeb of Data”, which we feel is a timely and valuable topic in our increasingly multilingual and interconnected world. TheWeb of Data has increasingly become a space where concepts are described not only with logic and ontologies but also with linguistic information in the form of multilingual lexicons, terminologies and thesauri. In particular, this has led to the creation of a growing cloud of linguistic linked open data, which bridges the world of ontologies with dictionaries, corpora and other linguistic resources. This raises several challenges, such as ontology localization, cross-lingual question answering, cross-lingual ontology and data matching, representation of lexical information on theWeb of Data, etc. Furthermore, Natural Language Processing (NLP) and machine learning for linked data can benefit from exploiting multilingual language resources, such as annotated corpora, wordnets, bilingual dictionaries, etc., if they are themselves formally represented and linked by following the linked data principles. A critical mass of language resources as linked data on the Web are leading to a new generation of linked data-aware NLP techniques and tools which, in turn, will serve as basis for a richer, multilingualWeb..

Complex correspondences for query patterns rewriting

International audienceThis paper discusses the use of complex alignments in the task of automatic query patterns rewriting. We apply this approach in SWIP, a system that allows for querying RDF data from natural language-based queries, hiding the complexity of SPARQL. SWIP is based on the use of query patterns that characterise families of queries and that are instantiated with respect to the initial user query expressed in natural language. However, these patterns are specific to the vocabulary used to describe the data source to be queried. For rewriting query patterns, we experiment ontology matching approaches in order to find complex correspondences between two ontologies describing data sources. From the alignments and initial query patterns, we rewrite these patterns in order to be able to query the data described using the target ontology. These experiments have been carried out on an ontology on the music domain and DBpedia ontology

MultiFarm: A benchmark for multilingual ontology matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism

Technology Integration around the Geographic Information: A State of the Art

One of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented

Biomedical ontology alignment: An approach based on representation learning

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results

MeLinDa: an interlinking framework for the web of data

The web of data consists of data published on the web in such a way that they can be interpreted and connected together. It is thus critical to establish links between these data, both for the web of data and for the semantic web that it contributes to feed. We consider here the various techniques developed for that purpose and analyze their commonalities and differences. We propose a general framework and show how the diverse techniques fit in the framework. From this framework we consider the relation between data interlinking and ontology matching. Although, they can be considered similar at a certain level (they both relate formal entities), they serve different purposes, but would find a mutual benefit at collaborating. We thus present a scheme under which it is possible for data linking tools to take advantage of ontology alignments.Comment: N° RR-7691 (2011