Dbnary: Wiktionary as a LMF based Multilingual RDF network

International audienceContributive resources, such as wikipedia, have proved to be valuable in Natural Language Processing or Multilingual Information Retrieval applications. This article focusses on Wiktionary, the dictionary part of the collaborative resources sponsored by the Wikimedia foundation. In this article we present a word net that has been extracted from French, English and German wiktionaries. We present the structure of this word net and discuss the specific extraction problems induced by this kind of contributive resources and the method used to overcome them. Then we show how we represent the extracted data as a Lexical Markup Framework (LMF) compatible lexical network represented in Resource Description Framework (RDF) format

Wiktionary and NLP: Improving synonymy networks

International audienceWiktionary, a satellite of the Wikipedia initiative, can be seen as a potential resource for Natural Language Processing. It requires however to be processed before being used efficiently as an NLP resource. After describing the relevant aspects Wiktionary for our purposes, we focus on its structural properties. Then, we describe how we extracted synonymy networks from this resource. We provide an in-depth study of these synonymy networks and compare them to those extracted from traditional resources. Finally, we describe two methods for semiautomatically improving this network by adding missing relations: (i) using a kind of semantic proximity measure; (ii) using translation relations of Wiktionary itself

Learning of a multilingual bitaxonomy of Wikipedia and its application to semantic predicates

The ability to extract hypernymy information on a large scale is becoming increasingly important in natural language processing, an area of the artificial intelligence which deals with the processing and understanding of natural language. While initial studies extracted this type of information from textual corpora by means of lexico-syntactic patterns, over time researchers moved to alternative, more structured sources of knowledge, such as Wikipedia. After the first attempts to extract is-a information fromWikipedia categories, a full line of research gave birth to numerous knowledge bases containing information which, however, is either incomplete or irremediably bound to English. To this end we put forward MultiWiBi, the first approach to the construction of a multilingual bitaxonomy which exploits the inner connection between Wikipedia pages and Wikipedia categories to induce a wide-coverage and fine-grained integrated taxonomy. A series of experiments show state-of-the-art results against all the available taxonomic resources available in the literature, also with respect to two novel measures of comparison. Another dimension where existing resources usually fall short is their degree of multilingualism. While knowledge is typically language agnostic, currently resources are able to extract relevant information only in languages providing highquality tools. In contrast, MultiWiBi does not leave any language behind: we show how to taxonomize Wikipedia in an arbitrary language and in a way that is fully independent of additional resources. At the core of our approach lies, in fact, the idea that the English version of Wikipedia can be linguistically exploited as a pivot to project the taxonomic information extracted from English to any other Wikipedia language in order to have a bitaxonomy in a second, arbitrary language; as a result, not only concepts which have an English equivalent are covered, but also those concepts which are not lexicalized in the source language. We also present the impact of having the taxonomized encyclopedic knowledge offered by MultiWiBi embedded into a semantic model of predicates (SPred) which crucially leverages Wikipedia to generalize collections of related noun phrases to infer a probability distribution over expected semantic classes. We applied SPred to a word sense disambiguation task and show that, when MultiWiBi is plugged in to replace an internal component, SPred’s generalization power increases as well as its precision and recall. Finally, we also published MultiWiBi as linked data, a paradigm which fosters interoperability and interconnection among resources and tools through the publication of data on the Web, and developed a public interface which lets the users navigate through MultiWiBi’s taxonomic structure in a graphical, captivating manner

DBpedia Mashups

If you see Wikipedia as a main place where the knowledge of mankind is concentrated, then DBpedia – which is extracted from Wikipedia – is the best place to find machine representation of that knowledge. DBpedia constitutes a major part of the semantic data on the web. Its sheer size and wide coverage enables you to use it in many kind of mashups: it contains biographical, geographical, bibliographical data; as well as discographies, movie meta-data, technical specifications, and links to social media profiles and much more. Just like Wikipedia, DBpedia is a truly cross-language effort, e.g., it provides descriptions and other information in various languages. In this chapter we introduce its structure, contents, its connections to outside resources. We describe how the structured information in DBpedia is gathered, what you can expect from it and what are its characteristics and limitations. We analyze how other mashups exploit DBpedia and present best practices of its usage. In particular, we describe how Sztakipedia – an intelligent writing aid based on DBpedia – can help Wikipedia contributors to improve the quality and integrity of articles. DBpedia offers a myriad of ways to accessing the information it contains, ranging from SPARQL to bulk download. We compare the pros and cons of these methods. We conclude that DBpedia is an un-avoidable resource for pplications dealing with commonly known entities like notable persons, places; and for others looking for a rich hub connecting other semantic resources

Web 2.0, language resources and standards to automatically build a multilingual named entity lexicon

This paper proposes to advance in the current state-of-the-art of automatic Language Resource (LR) building by taking into consideration three elements: (i) the knowledge available in existing LRs, (ii) the vast amount of information available from the collaborative paradigm that has emerged from the Web 2.0 and (iii) the use of standards to improve interoperability. We present a case study in which a set of LRs for different languages (WordNet for English and Spanish and Parole-Simple-Clips for Italian) are extended with Named Entities (NE) by exploiting Wikipedia and the aforementioned LRs. The practical result is a multilingual NE lexicon connected to these LRs and to two ontologies: SUMO and SIMPLE. Furthermore, the paper addresses an important problem which affects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The different steps of the procedure (mapping, disambiguation, extraction, NE identification and postprocessing) are comprehensively explained and evaluated. The resulting resource contains 974,567, 137,583 and 125,806 NEs for English, Spanish and Italian respectively. Finally, in order to check the usefulness of the constructed resource, we apply it into a state-of-the-art Question Answering system and evaluate its impact; the NE lexicon improves the system’s accuracy by 28.1%. Compared to previous approaches to build NE repositories, the current proposal represents a step forward in terms of automation, language independence, amount of NEs acquired and richness of the information represented