Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood. In this paper we study the different types of links in Wikipedia, and contrast the use of the full graph with respect to just direct links. We apply a well-known random walk algorithm on two tasks, word relatedness and named-entity disambiguation. We show that using the full graph is more effective than just direct links by a large margin, that non-reciprocal links harm performance, and that there is no benefit from categories and infoboxes, with coherent results on both tasks. We set new state-of-the-art figures for systems based on Wikipedia links, comparable to systems exploiting several information sources and or supervised machine learning. Our approach is open source, with instruction to reproduce results, and amenable to be integrated with complementary text-based methods.
For easier exposition, we will examine the results by row section simultaneously on relatedness and NED. The in Table report four relatedness systems which have already been presented in Sect. , showing that our system is best in all five datasets. Note that the @cite_0 row was obtained running their publicly available system with the supervised Machine Learning component turned off (see below for the results using SUP). The top rows of table report the most frequent baseline (as produced by our dictionary) and three link-based systems (cf. Sect. ), showing that our method is best in all five datasets. These results show that the use of the full graph as devised in this paper is a winning strategy.
@cite_0
2031046392
The online encyclopedia Wikipedia is a vast, constantly evolving tapestry of interlinked articles. For developers and researchers it represents a giant multilingual database of concepts and semantic relations, a potential resource for natural language processing and many other research areas. This paper introduces the Wikipedia Miner toolkit, an open-source software system that allows researchers and developers to integrate Wikipedia@?s rich semantics into their own applications. The toolkit creates databases that contain summarized versions of Wikipedia@?s content and structure, and includes a Java API to provide access to them. Wikipedia@?s articles, categories and redirects are represented as classes, and can be efficiently searched, browsed, and iterated over. Advanced features include parallelized processing of Wikipedia dumps, machine-learned semantic relatedness measures and annotation features, and XML-based web services. Wikipedia Miner is intended to be a platform for sharing data mining techniques.