Julien Subercaze

Inferray: fast in-memory RDF inference

The advent of semantic data on the Web requires efficient reasoning systems to infer RDF and OWL data. The linked nature and the huge volume of data entail efficiency and scalability challenges when designing productive inference systems. This paper presents Inferray, an implementation of RDFS, ρdf, and RDFS-Plus inference with improved performance over existing solutions. The main features of Inferray are 1) a storage layout based on vertical partitioning that guarantees sequential access and efficient sort-merge join inference; 2) efficient sorting of pairs of 64-bit integers using ad-hoc optimizations on MSD radix and a custom counting sort; 3) a dedicated temporary storage to perform efficient graph closure computation. Our measurements on synthetic and real-world datasets show improvements over competitors on RDFS-Plus, and up to several orders of magnitude for transitivity closure.

Virtual Community Building and the Information Society: Current and Future Directions

This paper reports the results of an investigation into the life cycle model needed to develop information systems for groups of people with fluid requirements. For this purpose, we developed a modified spiral model and applied it to the analysis, design and implementation of a virtual community for a group of researchers and organizations that collaborated in a research project and had changing system requirements. The virtual knowledge community was dedicated to support mobilization and dissemination of evidence-based knowledge produced by the Disability Rights Promotion International Canada (DRPI-Canada) project.

Combining the semantic and the social web for intelligent learning systems

Over the last decade, two new facets of the Web emerged. On one hand, the Semantic Web provides data structures in order to break meanings barriers between distributed and heterogeneous data sources presents on the Web. On the other hand, the Social Web helps in clustering Web users into communities, and let them be prosumers of their Web experience. Both visions encompass many online applications, including Education. On the question of what is really the next stage of Web developments for Education, authors usually focus on a single of these two facets of the Web. As few studies have tried to combine them for an educational purpose this paper aims at giving an insight on the pioneers works and the opportunities raised by mixing the Social and the Semantic Web for education.

Towards Successful Virtual Communities

With the multiplication of communication medium, the increasing multi-partner global organizations,the remote working tendencies,dynamic teams, pervasive or ubiquitous computing Virtual Communities (VCs) are playing an increasing role in social organizations currently and will probably change profoundly the way people interact in the future. In this paper, we present our position on the key characteristics that are imperative to provide a successful VC as well as the future directions in terms of research, development and implementation. We identify three main aspects (business, techniques and social) and analyze for each of them the different components and their relationships.

Slider: An Efficient Incremental Reasoner

The Semantic Web has gained substantial momentum over the last decade. It contributes to the manifestation of knowledge from data, and leverages implicit knowledge through reasoning algorithms. The main drawbacks of current reasoning methods over ontologies are two-fold: first they struggle to provide scalability for large datasets, and second, 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 goes a significant step beyond existing system, including i) performance, by more than a 70% improvement in average compared to the fastest reasoner available to the best of our knowledge, and ii) inferences on streams of semantic data, by using intrinsic features that are themselves streams-oriented. Slider is fragment agnostic and conceived to handle expanding data with a growing background knowledge base. It natively supports pdf and RDFS, and its architecture allows to extend it to more complex fragments with a minimal effort. In this demo a web-based interface allows the users to visualize the internal behaviour of Slider during the inference, to better understand its design and principles.

On metric embedding for boosting semantic similarity computations

Computing pairwise word semantic similarity is widely used and serves as a building block in many tasks in NLP. 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) similarity while keeping strong correlations (r = .819, ρ = .826).

Video Stream Transmodality

In this paper we introduce the concept of video stream transmodality. Transmodality is the partitioning of an image into regions that are expected to present a better entropy using different coding schemes, depending on their structural density, at constant bandwidth. Our contribution is a transmoder i.e. an algorithm able to perform transmodality on a video stream. The transmoder includes different video coding adapted optimizations. We evaluate our proposal with different kinds of video (in content term), and we show that we are able to save up to 8% of bandwidth for the same PSNR in comparison with state of the art video encoding baselines.

The twitaholic next door.: scalable friend recommender system using a concept-sensitive hash function

In this paper we present a Friend Recommender System for micro-blogging. Traditional batch processing of massive amounts of data makes it difficult to provide a near-real time friend recommender system or even a system that can properly scale to millions of users. In order to overcome these issues, we have designed a solution that represents user-generated micro posts as a set of pseudo-cliques. These graphs are assigned a hash value using an original Concept-Sensitive Hash function, a new sub-kind of Locally-Sensitive Hash functions. Finally, since the user profiles are represented as a binary footprint, the pairwise comparison of footprints using the Hamming distance provides scalability to the recommender system. The paper goes with an online application relying on a large Twitter dataset, so that the reader can freely experiment the system.

Virtual knowledge communities for semantic agents

Virtual Knowledge Communities are a well suited paradigm for decentralized knowldege exchanges and they have been applied in several domains. In this paper we investigate the implementation of virtual knowledge communities with semantic agents. Using the SAM (Semantic Agent Modeling) approach, we show that agents can exchange community related concepts (in OWL) and behavior (in SWRL). Agents can then learn and adapt new community-related behavior, which is usefull when changing the role or entering into a new environment. For this purpose, we formalize Virtual Knowledge Communities in a set-theoretic way and we implement this formalization in an OWL ontology. Some examples of community representation using our formalization are presented in this paper.

A Merging Heuristic for the Rectangle Decomposition of Binary Matrices

In this paper we present a linear-time and linear-space algorithm for the decomposition of binary images into rectangles. Our contribution is a two-stage algorithm. In the first stage we compute a