Lifting user generated comments to SIOC

International audienceHTML boilerplate code is acting on webpages as presentation directives for a browser to display data to a human end user. For the machine, our community made tremenduous e orts to provide querying endpoints using consensual schemas, protocols, and principles since the avent of the Linked Data paradigm. These data lifting e orts have been the primary materials for bootstraping the Web of data. Data lifting usually involves an original data structure from which the semantic architect has to produce a mapper to RDF vocabularies. Less e orts are made in order to lift data produced by a Web mining process, due to the di culty to provide an e cient and scalable solution. Nonetheless, the Web of documents is mainly composed of natural language twisted in HTML boilerplate code, and few data schemas can be mapped into RDF. In this paper, we present CommentsLifter, a system that is able to lift SIOC data from user-generated comments in the Web 2.0

FoP: Never-Ending Learner for Multimedia Knowledge Extraction

International audience—In this paper we present our system Faces of Politics (henceforth FoP), that is able to continuously learn multimedia knowledge of Web multimedia resources about the presence of person(s) in a pictures and to leverage this knowledge to the Linked Open Data cloud (LOD-cloud). FoP promotes both scalability of the data lift process for this domain and a structured knowledge representation for complex queries. The system was bootstraped using Freebase data about politicians and their pictures, and we show that the model provides a good generalization with an error rate below 7%. Meantime, FoP not only relates a person to a multimedia resource, but it also detects and publishes metadata on the position of the person in the picture. Moreover, it supports the presence of several persons in the picture. At this step, FoP is also giving data in return to the LoD cloud that fed him in the first place: it leverages Linked Data on people recognized in these pictures, and on which rectangle area. This allows fine-grained queries like creating a curation of documents in which a person is depicted relatively to another for instance. On a technical point-of-view, we also provide a Website for browsing FoP knowledge base as Web users, and we also offer a public SPARQL endpoint for robots or other Web applications

Semantic Agent for Distributed Knowledge Management

At the beginning of the decade, the Agent Mediated Knowledge Management workshops series as well as Bonifacio's theoretical approach layed the foundations of a new eld of distributed knowledge management based upon the agent paradigm. The agent based approach enables key features for knowledge management. The local management of knowledge by agents allows to go beyond the limitations of centralized knowledge management. Thus, knowledge can be maintained in each agent at a coarse-grained level, with different representations. In the mean time the rise of the semantic web technologies enables a new range of possibilities for agents dedicated to knowledge management. In this chapter we investigate the integration of semantic web technologies into an agent architecture that allows agents to represent their knowledge and their behavior in a semantic manner. We present the semantic agent model, its implementation and we discuss the perpectives open by semantic agents

Learning from visualizing and Interacting with the Semantic Web Dog Food

International audienceSemantic Web conferences such as WWW and ISWC fos- tered a collaborative e ort for the leveraging of Linked Data about con- ferences people, papers and talks. This e ort gave birth to the Semantic Web Conference Corpus, a.k.a. the Semantic Web Dog Food Corpus. Many other conferences and journals contributed afterwards to this cor- pus, so that it is today a representative semantic data archive about our research community activities and progression. These metadata are con- sistent with Linked Data principles and therefore can be semantically processed by the machine. Although it is a matchless source of scienti c knowledge for our community, it is di cult for the researcher, as a hu- man, to browse this corpus that contains more than 180k unique triples. This paper presents our e ort to bring a user-friendly Web application based on the Semantic Web Dog Food corpus that show the topics trends in Semantic Web research. The application was made freely available to the researcher as an end user. In this work we identify speci c issues and barriers encountered when building the system, discuss how these were approached in this software, and how the lessons learnt can drive future implementations fostering the Web of Data

Conflict resolution when axioms are materialized in semantic-based smart environments.

International audienceIn SemanticWeb applications, reasoning engines that are data intensive commonly materialise inferences to speed up processing at query time. However, in evolving systems, such as smart environments, semantic-based context aware systems (SCAS) [6] or social software with user-generated data, knowledge does not grow monotonically: newer facts may contradict older ones, knowledge may be deprecated, discarded or updated such that knowledge must sometimes be retracted. We are describing a technique to retract explicit and inferred statements, when some information becomes obsolete, as well as retracting any statement that would lead to get back the removed explicit statements. This technique is based on OWL justifications and is triggered whenever a knowledge base becomes inconsistent, such that the system stays in a consistent state all the time, in spite of uncontrolled evolution.We prove termination and correctness of the algorithm, and describe the implementation and evaluation of the proposal

Extraction de commentaires utilisateurs sur le Web

National audienceDans cet article, nous présentons CommentsMiner, une solution d'ex-traction non supervisée pour l'extraction de commentaires utilisateurs. Notre approche se base sur une combinaison de techniques de fouille de sous-arbres fréquents, d'extraction de données et d'apprentissage de classement. Nos expéri-mentations montrent que CommentsMiner permet de résoudre le problème d'ex-traction de commentaires sur 84% d'un jeu de données représentatif et publique-ment accessible, loin devant les techniques existantes d'extraction

Plongement de métrique pour le calcul de similarité sémantique à l'échelle

National audiencePlongement de métrique pour le calcul de similarité sémantique à l'échelle Résumé. In this paper, we explore the embedding of the shortest-path metrics from a knowledge base (Wordnet) into the Hamming hypercube, in order to enhance the computation performance. We show that, although an isometric embedding is untractable, it is possible to achieve good non-isometric embeddings. We report a speedup of three orders of magnitude for the task of computing Leacock and Chodorow (LCH) similarities while keeping strong correlations

Mining user-generated comments

International audience—Social-media websites, such as newspapers, blogs, and forums, are the main places of generation and exchange of user-generated comments. These comments are viable sources for opinion mining, descriptive annotations and information extraction. User-generated comments are formatted using a HTML template, they are therefore entwined with the other information in the HTML document. Their unsupervised extraction is thus a taxing issue – even greater when considering the extraction of nested answers by different users. This paper presents a novel technique (CommentsMiner) for unsupervised users comments extraction. Our approach uses both the theoretical framework of frequent subtree mining and data extraction techniques. We demonstrate that the comment mining task can be modelled as a constrained closed induced subtree mining problem followed by a learning-to-rank problem. Our experimental evaluations show that CommentsMiner solves the plain comments and nested comments extraction problems for 84% of a representative and accessible dataset, while outperforming existing baselines techniques

Towards business model and technical platform for the service oriented context-aware mobile virtual communities

The focus of existing virtual communities is centered on a particular product or social interaction and the role of mobile devices is restricted to exchange a limited amount of contents. Herewith we envisage that the upcoming virtual communities will exploit the potential of social interaction and context information to offer personalized services to its members and mobile devices will play a significant role in this process. As a step towards this direction, in this paper we propose a business model for the mobile virtual communities in which the mobile device takes on the role of a content producer and content consumer. Though there are a number of research issues which need to be addressed to realize such virtual communities, in this paper we focus on the service requirements, architecture and open source software implementation of a technical platform for the content producer and consumer mobile devices

Slider : un Raisonneur Incrémental Évolutif

National audienceThe main drawbacks of current reasoning methods over ontologies are they struggle to provide scalability for large datasets. The batch processing reasoners who provide the best scalability so far are unable to infer knowledge from evolving data. We contribute to solving these problems by introducing Slider, an efficient incremental reasoner. Slider exhibits a performance improvement by more than a 70% compared to the OWLIM-SE reasoner. Slider is conceived to handle expanding data from streams with a growing background knowledge base. It natively supports ρdf and RDFS, and its architecture allows to extend it to more complex fragments with a minimal effort.Les solutions existantes pour le raisonnement incrémental souffrent principalement de leur incapacité à prendre en charge des ontologies complexes et ne sont pas conçues pour gérer de grandes quantités de connaissances. Dans cet article, nous présentons Slider (Chevalier et al. (2015)), un raisonneur incrémental évolutif par chaînage avant, permettant de raisonner sur des flux de données sémantiques