In this paper, we describe a supervised approach for extracting relations from Wikipedia. In particular, we exploit a self-training strategy for enriching a small number of manually labeled triples with new self-labeled examples. We integrate the supervised stage in WikiOIE, an existing framework for unsupervised extraction of relations from Wikipedia. We rely on WikiOIE and its unsupervised pipeline for extracting the initial set of unlabelled triples. An evaluation involving different algorithms and parameters proves that self-training helps to improve performance. Finally, we provide a dataset of about three million triples extracted from the Italian version of Wikipedia and perform a preliminary evaluation conducted on a sample dataset,...
In this paper, we present work on enhancing the basic data resource of a context-aware system. First...
The final publication is available at Springer via http://dx.doi.org/10.1007/11428817_7Proceedings o...
The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one sti...
In this paper, we describe a supervised approach for extracting relations from Wikipedia. In particu...
This dataset contains relations extracted from the Italian Wikipedia by the WikiOIE framework. WikiO...
This dataset contains relations extracted from the Italian Wikipedia by the WikiOIE framework. WikiO...
Abstract. Wikipedia is the largest encyclopedia on the web and has been widely used as a reliable so...
We present a minimally-supervised approach for learning part whole relations from texts. Unlike prev...
In this paper we present solutions for the crucial task of extracting structured information from ma...
Proceedings of the First Workshop on Semantic Wikis - From Wiki to Semantics co-located with the ESW...
Abstract. The exponential growth of Wikipedia recently attracts the attention of a large number of r...
Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extrac...
Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extrac...
Extracting hypernym relations from text is one of the key steps in the automated construction and en...
The present paper describes the approach proposed by the UNIGE_SE team to tackle the EVALITA 2020 sh...
In this paper, we present work on enhancing the basic data resource of a context-aware system. First...
The final publication is available at Springer via http://dx.doi.org/10.1007/11428817_7Proceedings o...
The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one sti...
In this paper, we describe a supervised approach for extracting relations from Wikipedia. In particu...
This dataset contains relations extracted from the Italian Wikipedia by the WikiOIE framework. WikiO...
This dataset contains relations extracted from the Italian Wikipedia by the WikiOIE framework. WikiO...
Abstract. Wikipedia is the largest encyclopedia on the web and has been widely used as a reliable so...
We present a minimally-supervised approach for learning part whole relations from texts. Unlike prev...
In this paper we present solutions for the crucial task of extracting structured information from ma...
Proceedings of the First Workshop on Semantic Wikis - From Wiki to Semantics co-located with the ESW...
Abstract. The exponential growth of Wikipedia recently attracts the attention of a large number of r...
Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extrac...
Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extrac...
Extracting hypernym relations from text is one of the key steps in the automated construction and en...
The present paper describes the approach proposed by the UNIGE_SE team to tackle the EVALITA 2020 sh...
In this paper, we present work on enhancing the basic data resource of a context-aware system. First...
The final publication is available at Springer via http://dx.doi.org/10.1007/11428817_7Proceedings o...
The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one sti...