Challenges as enablers for high quality linked data: Insights from the semantic publishing challenge

While most challenges organized so far in the Semantic Web domain are focused on comparing tools with respect to different criteria such as their features and competencies, or exploiting semantically enriched data, the Semantic Web Evaluation Challenges series, co-located with the ESWC Semantic Web Conference, aims to compare them based on their output, namely the produced dataset. The Semantic Publishing Challenge is one of these challenges. Its goal is to involve participants in extracting data from heterogeneous sources on scholarly publications, and producing Linked Data that can be exploited by the community itself. This paper reviews lessons learned from both (i) the overall organization of the Semantic Publishing Challenge, regarding the definition of the tasks, building the input dataset and forming the evaluation, and (ii) the results produced by the participants, regarding the proposed approaches, the used tools, the preferred vocabularies and the results produced in the three editions of 2014, 2015 and 2016. We compared these lessons to other Semantic Web Evaluation Challenges. In this paper, we (i) distill best practices for organizing such challenges that could be applied to similar events, and (ii) report observations on Linked Data publishing derived from the submitted solutions. We conclude that higher quality may be achieved when Linked Data is produced as a result of a challenge, because the competition becomes an incentive, while solutions become better with respect to Linked Data publishing best practices when they are evaluated against the rules of the challenge

Amélioration des performances des annotateurs sémantiques

RÉSUMÉ Les annotateurs sémantiques jouent un rôle important dans la transition du Web actuel au Web sémantique. Ils s’occupent d’extraire des informations structurées à partir de textes bruts, permettant ainsi de pointer vers des bases des connaissances telles que DBpedia, YAGO ou Babelnet. De nombreuses compétitions sont organisées chaque année pour promouvoir les travaux de recherche de ce domaine. Nous présentons dans ce mémoire notre participation à la compétition Open Knowledge Extraction que nous avons remportée à la conférence European Semantic Web Conference 2016. Dans le cadre de cette compétition, nous avons implémenté une approche générique que nous avons testée sur quatre annotateurs sémantiques. Nous nous concentrons dans ce mémoire à décrire un annotateur sémantique en particulier, DBpedia Spotlight. Nous exposons les différentes limites que présente cet annotateur ainsi que les approches que nous avons développées pour y remédier. Nous avons noté une augmentation d’une moyenne de 20% des performances actuelles de DBpedia Spotlight en testant sur différents corpus. Ces derniers proviennent principalement de journaux internationaux, "Reuters News Stories", "MSNBC" et le "New York Times".----------ABSTRACT Semantic annotators play an important role in the transition from the current Web to the Semantic Web. They take care of extracting structured information from raw texts and thus make it possible to point to knowledge bases such as DBpedia, YAGO or Babelnet. Many competitions are organized every year to promote research works in this field. We present in this thesis our system which was the winner of the Open Knowledge Extraction challenge at the European Semantic Web Conference 2016. For this competition, we implemented a generic approach tested with four semantic annotators. We particularly focus in this thesis on one semantic annotator, DBpedia Spotlight. We present its different limitations along with the approaches that we have developed to remedy them. We noted an improvement of an average of 20% of the current performance of DBpedia Spotlight on different corpora that mainly come from international newspapers, "Reuters News Stories", "MSNBC" and the "New York Times"

Amélioration des performances des annotateurs sémantiques

RÉSUMÉ Les annotateurs sémantiques jouent un rôle important dans la transition du Web actuel au Web sémantique. Ils s’occupent d’extraire des informations structurées à partir de textes bruts, permettant ainsi de pointer vers des bases des connaissances telles que DBpedia, YAGO ou Babelnet. De nombreuses compétitions sont organisées chaque année pour promouvoir les travaux de recherche de ce domaine. Nous présentons dans ce mémoire notre participation à la compétition Open Knowledge Extraction que nous avons remportée à la conférence European Semantic Web Conference 2016. Dans le cadre de cette compétition, nous avons implémenté une approche générique que nous avons testée sur quatre annotateurs sémantiques. Nous nous concentrons dans ce mémoire à décrire un annotateur sémantique en particulier, DBpedia Spotlight. Nous exposons les différentes limites que présente cet annotateur ainsi que les approches que nous avons développées pour y remédier. Nous avons noté une augmentation d’une moyenne de 20% des performances actuelles de DBpedia Spotlight en testant sur différents corpus. Ces derniers proviennent principalement de journaux internationaux, "Reuters News Stories", "MSNBC" et le "New York Times".----------ABSTRACT Semantic annotators play an important role in the transition from the current Web to the Semantic Web. They take care of extracting structured information from raw texts and thus make it possible to point to knowledge bases such as DBpedia, YAGO or Babelnet. Many competitions are organized every year to promote research works in this field. We present in this thesis our system which was the winner of the Open Knowledge Extraction challenge at the European Semantic Web Conference 2016. For this competition, we implemented a generic approach tested with four semantic annotators. We particularly focus in this thesis on one semantic annotator, DBpedia Spotlight. We present its different limitations along with the approaches that we have developed to remedy them. We noted an improvement of an average of 20% of the current performance of DBpedia Spotlight on different corpora that mainly come from international newspapers, "Reuters News Stories", "MSNBC" and the "New York Times"