Vers la construction de workflows pour le filtrage sémantique de nouvelles

National audienceInternet is becoming today a wonderful medium to broadcast informations. While sources are multiplying (RSS, Web Services, ...), the amount of informations is growing and it becomes necessary to filter them according to user interests. Many tools are currently developed that exploits ontologies or thesauri to annotate informations. They enable to query these annotations according to criteria to retrieve only the relevant informations. The composition of these tools constitute workflows that should be enriched by the emergence of new ontologies modeling different domains and text analysis tools. However the composition of these tools-chains is not accessible for everyone. In this paper we show how these workflows are built and present our approach for automatically building workflows based on user needs. This work is supported by the ANR Emergence \Y project dedicated to automate the broadcasting of informations on large screens, and for which the relevance of informations published is important.Le web se révèle aujourd'hui un merveilleux support de diffusion d'informations. Cependant, tandis que les sources se multiplient (flux rss, services web, ..), la quantité d'informations croît et il est nécessaire de les filtrer en fonction des centres d'intérêts des utilisateurs. Actuellement de nombreux outils qui exploitent les ontologies ou les thésaurus sont mis au point. Ils permettent d'annoter les informations, d'en déduire des critères et d'ensuite obtenir uniquement les informations pertinentes. La composition de ces outils constitue des workflows qui devraient encore s'enrichir grâce à l'apparition de nouvelles ontologies ciblées sur différents domaines et outils de lecture. Cependant la construction de telles chaînes logicielles n'est pas à la portée de tous. Dans cet article nous montrons comment de tels workflows ont été construits et présentons nos perspectives en matière de construction automatique de ces workflows en fonction des besoins utilisateur. Ce travail s'appuie sur le projet ANR Emergence \Y qui vise à automatiser la diffusion des informations sur de grands écrans, et pour lequel la pertinence des informations diffusées est donc particulièrement importante

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHCAA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely- Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data

Device-Oriented Automatic Semantic Annotation in IoT

Semantic technologies are the keys to address the problem of information interaction between assorted, heterogeneous, and distributed devices in the Internet of Things (IoT). Semantic annotation of IoT devices is the foundation of IoT semantics. However, the large amount of devices has led to the inadequacy of the manual semantic annotation and stressed the urgency into the research of automatic semantic annotation. To overcome these limitations, a device-oriented automatic semantic annotation method is proposed to annotate IoT devices’ information. The processes and corresponding algorithms of the automatic semantic annotation method are presented in detail, including the information extraction, text classification, property information division, semantic label selection, and information integration. Experiments show that our method is effective for the automatic semantic annotation to IoT devices’ information. In addition, compared to a typical rule-based method, the comparison experiment demonstrates that our approach outperforms this baseline method with respect to the precision and F-measure