Raphaël Troncy

COMM: designing a well-founded multimedia ontology for the web

Semantic descriptions of non-textual media available on the web can be used to facilitate retrieval and presentation of media assets and documents containing them. While technologies for multimedia semantic descriptions already exist, there is as yet no formal description of a high quality multimedia ontology that is compatible with existing (semantic) web technologies. We explain the complexity of the problem using an annotation scenario. We then derive a number of requirements for specifying a formal multimedia ontology before we present the developed ontology, COMM, and evaluate it with respect to our requirements. We provide an API for generating multimedia annotations that conform to COMM.

Analysis of named entity recognition and linking for tweets

Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.

GERBIL: General Entity Annotator Benchmarking Framework

We present GERBIL, an evaluation framework for semantic entity annotation. The rationale behind our framework is to provide developers, end users and researchers with easy-to-use interfaces that allow for the agile, fine-grained and uniform evaluation of annotation tools on multiple datasets. By these means, we aim to ensure that both tool developers and end users can derive meaningful insights pertaining to the extension, integration and use of annotation applications. In particular, GERBIL provides comparable results to tool developers so as to allow them to easily discover the strengths and weaknesses of their implementations with respect to the state of the art. With the permanent experiment URIs provided by our framework, we ensure the reproducibility and archiving of evaluation results. Moreover, the framework generates data in machine-processable format, allowing for the efficient querying and post-processing of evaluation results. Finally, the tool diagnostics provided by GERBIL allows deriving insights pertaining to the areas in which tools should be further refined, thus allowing developers to create an informed agenda for extensions and end users to detect the right tools for their purposes. GERBIL aims to become a focal point for the state of the art, driving the research agenda of the community by presenting comparable objective evaluation results.

Semantic Multimedia

Multimedia constitutes an interesting field of application for Semantic Web and Semantic Web reasoning, as the access and management of multimedia content and context depends strongly on the semantic descriptions of both. At the same time, multimedia resources constitute complex objects, the descriptions of which are involved and require the foundation on sound modeling practice in order to represent findings of low- and high level multimedia analysis and to make them accessible via Semantic Web querying of resources. This tutorial aims to provide a red thread through these different issues and to give an outline of where Semantic Web modeling and reasoning needs to further contribute to the area of semantic multimedia for the fruitful interaction between these two fields of computer science.

Semantic Commitment for Designing Ontologies: A Proposal

The French institute ina is interested in ontologies in order to describe the content of audiovisual documents. Methodologies and tools for building such objects exist, but few propose complete guidelines to help the user to organize the key components of ontologies: subsumption hierarchies. This article proposes to use a methodology introducing a clear semantic commitment to normalize the meaning of the concepts. We have implemented this methodology in an editor, DOE, complementary to other existing tools, and used it to develop several ontologies.

Integrating structure and semantics into audio-visual documents

Describing audio-visual documents amounts to consider documentary aspects (the structure) as well as conceptual aspects (the content). In this paper, we propose an architecture which describes formally the content of the videos and which constrains the structure of their descriptions. This work is based on languages and technologies underlying the Semantic Web and in particular ontologies. Therefore, we propose to combine emerging Web standards, namely MPEG-7/XML Schema for the structural part and OWL/RDF for the knowledge part of the description. Finally, our work offers reasoning support on both aspects when querying a database of videos.

Hybrid event recommendation using linked data and user diversity

An ever increasing number of social services offer thousands of diverse events per day. Users tend to be overwhelmed by the massive amount of information available, especially with limited browsing options perceived in many event web services. To alleviate this information overload, a recommender system becomes a vital component for assisting users selecting relevant events. However, such system faces a number of challenges owed to the the inherent complex nature of an event. In this paper, we propose a novel hybrid approach built on top of Semantic Web. On the one hand, we use a content-based system enriched with Linked Data to overcome the data sparsity, a problem induced by the transiency of events. On the other hand, we incorporate a collaborative filtering to involve the social aspect, an influential feature in decision making. This hybrid system is enhanced by the integration of a user diversity model designed to detect user propensity towards specific topics. We show how the hybridization of CB+CF systems and the integration of interest diversity features are important to improve predictions. Experimental results demonstrate the effectiveness of our approach using precision and recall measures.

Linking events with media

We present a large dataset composed of events descriptions together with media descriptions associated with these events and interlinked with the larger Linked Open Data cloud. We are constructing a web-based environment that allows users to explore and select events, to inspect associated media, and to discover meaningful, surprising or entertaining connections between events, media and people participating in events. The dataset is obtained from three large public event directories (last.fm, eventful and upcoming) represented with the LODE ontology and from large media directories (flickr, youtube) represented with the Media Ontology. We describe how the data has been converted, interlinked and published following the best practices of the Semantic Web community.

Finding media illustrating events

We present a method combining semantic inferencing and visual analysis for finding automatically media (photos and videos) illustrating events. We report on experiments validating our heuristic for mining media sharing platforms and large event directories in order to mutually enrich the descriptions of the content they host. Our overall goal is to design a web-based environment that allows users to explore and select events, to inspect associated media, and to discover meaningful, surprising or entertaining connections between events, media and people participating in events. We present a large dataset composed of semantic descriptions of events, photos and videos interlinked with the larger Linked Open Data cloud and we show the benefits of using semantic web technologies for integrating multimedia metadata.

oMAP: combining classifiers for aligning automatically OWL ontologies

This paper introduces a method and a tool for automatically aligning OWL ontologies, a crucial step for achieving the interoperability of heterogeneous systems in the Semantic Web. Different components are combined for finding suitable mapping candidates (together with their weights), and the set of rules with maximum matching probability is selected. Machine learning-based classifiers and a new classifier using the structure and the semantics of the OWL ontologies are proposed. Our method has been implemented and evaluated on an independent test set provided by an international ontology alignment contest. We provide the results of this evaluation with respect to the other competitors.