Providing accurate approaches for keyword search or question answering to access the data available on the Linked Data Web is of central importance to ensure that it can be used by non-experts. In many cases, these approaches return a large number of results that need to be provided in the right order so as to be of relevance to the user. Achieving the goal of improving the access to the Linked...

Given a classifier trained on relatively few training examples, active learning (AL) consists in ranking a set of unlabeled examples in terms of how informative they would be, if manually labeled, for retraining a (hopefully) better classifier. An important text learning task in which AL is potentially useful is information extraction (IE), namely, the task of identifying within a text the expr...

Increasingly many knowledge bases are published as Linked Data, driving the need for effective and efficient techniques for information access. Knowledge repositories are naturally organised around objects or entities and constitute a promising data source for entity-oriented search. There is a growing body of research on the subject, however, it is almost always (implicitly) assumed that a cen...

University rankings generally present users with the problem of placing the results given for an institution in context. Only a comparison with the performance of all other institutions makes it possible to say exactly where an institution stands. In order to interpret the results of the SCImago Institutions Ranking (based on Scopus data) and the Leiden Ranking (based on Web of Science data), i...

This paper describes our participation in the Entity List Completion (ELC) task at Entity track 2011. Our approach combined the work done for the Related Entity Finding 2010 task with some new criteria as the proximity or the similarity between a candidate answer with the correct answers given as examples or their cooccurrences.

Wikipedia is a useful knowledge source that benefits many applications in language processing and knowledge representation. An important feature of Wikipedia is that of categories. Wikipedia pages are assigned different categories according to their contents as human-annotated labels which can be used in information retrieval, ad hoc search improvements, entity ranking and tag recommendations. ...

Previous work for relation extraction from free text is mainly based on intra-sentence information. As relations might be mentioned across sentences, inter-sentence information can be leveraged to improve distantly supervised relation extraction. To effectively exploit inter-sentence information , we propose a ranking-based approach, which first learns a scoring function based on a listwise lea...

This work investigates the use of linguistically motivated features to improve search, in particular for ranking answers to non-factoid questions. We show that it is possible to exploit existing large collections of question–answer pairs (from online social Question Answering sites) to extract such features and train ranking models which combine them effectively. We investigate a wide range of ...

In this article, we describe the architecture of the OntoGene Relation mining pipeline and its application in the triage task of BioCreative 2012. The aim of the task is to support the triage of abstracts relevant to the process of curation of the Comparative Toxicogenomics Database. We use a conventional information retrieval system (Lucene) to provide a baseline ranking, which we then combine...

In this paper we introduce a method for exploiting entities from the emerging Web of Data for enhancing various Information Retrieval (IR) services. The approach is based on named-entity recognition applied in a set of search results, and on a graph of documents and identified entities that is constructed dynamically and analyzed stochastically using a Random Walk method. The result of this ana...