Bruno Grilheres

A Platform for Semantic Annotations and Ontology Population Using Conditional Random Fields

Ontologies are widely used for organising and sharing knowledge. But elaborating these resources is a heavy and time-consuming task. This paper is two-fold: it describes EADS DCS text-mining platform, in particular, its service to annotate documents with semantic tags and it presents its extension for incremental learning of ontologies. Domain experts are assisted in the ontology population task by recent machine learning techniques (i.e. conditional random fields). Comparisons are made between annotations from the ontology and from a trained CRF model, so as to detect candidate instances. An iterative process controlled by the experts results in knowledge discovery and constitution of an accurate ontology.

A step toward an adaptive composition of query suggestion approaches

The problems of comparing search support tool in interactive information retrieval (IIR) and of selecting the right one have always been difficult due to the inherent dependency to users. Using an adapted evaluation protocol, we study in this paper different suggestion approaches. The results show that the performance are changing for different users and also during the search sessions. As a consequence, they also show that the selection of a particular support tool has to use new grounding. In this way, we propose a system that allows to combine independent suggestion mechanisms based on an analysis of user behavior and considering the search session time as a key factor instead of using only static rules.

Events Extraction and Aggregation for Open Source Intelligence: From Text to Knowledge

Due to the considerable increase of freely available data, the discovery of relevant information from textual content is a critical challenge. The work presented here takes part in ongoing researches to develop a global knowledge gathering system. It aims at building knowledge sheets summarizing all the pieces of information we know about events extracted from text. For this sake, we define a global process bringing together different methods and components from multiple domains of research.

Relevant Sources of Information Are Not Necessarily Popular Ones

The constant growth of the Web in recent years has made more difficult the discovery of new sources of information on a given topic. This is a prominent problem for Experts in Intelligence Analysis (EIA) who are faced to the search of pages on specific and sensitive topics. Because of their lack of popularity or because they are poorly indexed due to their sensitive content, these pages are hard-to-find with traditional search engines. In this article, we describe a new Web source discovery system called DOWSER (Discovery Of Web Sources Evaluating Relevance). The goal of this system is to provide users with new sources of information related to their needs without considering the popularity of a page unlike classic Information Retrieval tools. The expected result is a balance between relevance and originality, in the sense that the wanted pages are not necessary popular. DOWSER is based on a user profile to focus its exploration of the Web in order to collect and index only related Web documents. As requests can be insufficient to express sensitive and specific needs, the users information needs are specified using users interests represented by DBPedia resources [1] and keywords, both extracted from Web pages provided by the user. A series of experiments provides an empirical evaluation of DOWSER.

A Bi-Dimensional User Profile to Discover Unpopular Web Sources

The discovery of new sources of information on a given topic is a prominent problem for Experts in Intelligence Analysis (EIA) who cope with the search of pages on specific and sensitive topics. Their information needs are difficult to express with queries and pages with sensitive content are difficult to find with traditional search engines as they are usually poorly indexed. We propose a double vector to model EIAs information needs, composed of DBpedia resources and keywords, both extracted from Web pages provided by the user. We also introduce a new similarity measure that is used in a Web source discovery system called DOWSER. DOWSER aims at providing users with new sources of information related to their needs without considering the popularity of a page. A series of experiments provides an empirical evaluation of the whole system.