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..

Language-agnostic relation extraction from abstracts in Wikis

Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extraction from text, using the data in the knowledge graph as training data, i.e., using distant supervision. While most existing approaches use language-specific methods (usually for English), we present a language-agnostic approach that exploits background knowledge from the graph instead of language-specific techniques and builds machine learning models only from language-independent features. We demonstrate the extraction of relations from Wikipedia abstracts, using the twelve largest language editions of Wikipedia. From those, we can extract 1.6 M new relations in DBpedia at a level of precision of 95%, using a RandomForest classifier trained only on language-independent features. We furthermore investigate the similarity of models for different languages and show an exemplary geographical breakdown of the information extracted. In a second series of experiments, we show how the approach can be transferred to DBkWik, a knowledge graph extracted from thousands of Wikis. We discuss the challenges and first results of extracting relations from a larger set of Wikis, using a less formalized knowledge graph