Ontology-Mediated Queries for NOSQL Databases

This paper is an extended abstract of the paper with the same title presented at AAAI 2016.International audienceOntology-Based Data Access has been studied so far for relational structures and deployed on top of relational databases. This paradigm enables a uniform access to heterogeneous data sources, also coping with incomplete information. Whether OBDA is suitable also for non-relational structures, like those shared by increasingly popular NOSQL languages, is still an open question. In this paper, we study the problem of answering ontology-mediated queries on top of key-value stores. We formalize the data model and core queries of these systems, and introduce a rule language to express lightweight ontologies on top of data. We study the decidability and data complexity of query answering in this setting

Représenter l'information et Web sémantique

National audienceCe petit ouvrage s'adresse à tous les curieux de l'intelligence artificielle (IA). C'est une introduction au sujet, volontairement brève, aussi élémentaire que possible, afin d'être accessible au plus grand nombre. Elle est écrite par un groupe de spécialistes reconnus. Pour conclure Glossaire Quelques références Contributeurs De même que l'IA concerne tous les secteurs, cet ouvrage devrait intéresser tous vos lecteurs

Sanity checks and improvements for patch visualisation in prototype-based image classification

In this work, we perform an in-depth analysis of the visualisation methods implemented in two popular self-explaining models for visual classification based on prototypes - ProtoPNet and ProtoTree. Using two fine-grained datasets (CUB-200-2011 and Stanford Cars), we first show that such methods do not correctly identify the regions of interest inside of the images, and therefore do not reflect the model behaviour. Secondly, using a deletion metric, we demonstrate quantitatively that saliency methods such as Smoothgrads or PRP provide more faithful image patches. We also propose a new relevance metric based on the segmentation of the object provided in some datasets (e.g. CUB-200-2011) and show that the imprecise patch visualisations generated by ProtoPNet and ProtoTree can create a false sense of bias that can be mitigated by the use of more faithful methods. Finally, we discuss the implications of our findings for other prototype-based models sharing the same visualisation method

Mining XML Documents

XML documents are becoming ubiquitous because of their rich and flexible format that can be used for a variety of applications. Giving the increasing size of XML collections as information sources, mining techniques that traditionally exist for text collections or databases need to be adapted and new methods to be invented to exploit the particular structure of XML documents. Basically XML documents can be seen as trees, which are well known to be complex structures. This chapter describes various ways of using and simplifying this tree structure to model documents and support efficient mining algorithms. We focus on three mining tasks: classification and clustering which are standard for text collections; discovering of frequent tree structure which is especially important for heterogeneous collection. This chapter presents some recent approaches and algorithms to support these tasks together with experimental evaluation on a variety of large XML collections

Uncertainty-sensitive reasoning for inferring sameAs facts in linked data

albakri2016aInternational audienceDiscovering whether or not two URIs described in Linked Data -- in the same or different RDF datasets -- refer to the same real-world entity is crucial for building applications that exploit the cross-referencing of open data. A major challenge in data interlinking is to design tools that effectively deal with incomplete and noisy data, and exploit uncertain knowledge. In this paper, we model data interlinking as a reasoning problem with uncertainty. We introduce a probabilistic framework for modelling and reasoning over uncertain RDF facts and rules that is based on the semantics of probabilistic Datalog. We have designed an algorithm, ProbFR, based on this framework. Experiments on real-world datasets have shown the usefulness and effectiveness of our approach for data linkage and disambiguation

SomeRDFS in the Semantic Web

The Semantic Web envisions a world-wide distributed architecture where computational resources will easily inter-operate to coordinate complex tasks such as query answering. Semantic marking up of web resources using ontologies is expected to provide the necessary glue for making this vision work. Using ontology languages, (communities of) users will build their own ontologies in order to describe their own data. Adding semantic mappings between those ontologies, in order to semantically relate the data to share, gives rise to the Semantic Web: data on the web that are annotated by ontologies networked together by mappings. In this vision, the Semantic Web is a huge semantic peer data management system. In this paper, we describe the SomeRDFS peer data management systems that promote a "simple is beautiful" vision of the Semantic Web based on data annotated by RDFS ontologies

Exploiting ontologies and alignments for trust in semantic P2P networks

Les rapports de recherche du LIG - ISSN: 2105-0422In a semantic P2P network, peers use separate ontologies and rely on alignments between their ontologies for translating queries. However, alignments may be limited —unsound or incomplete— and generate flawed translations, and thereby produce unsatisfactory answers. In this paper we propose a trust mechanism that can assist peers to select those in the network that are better suited to answer their queries. The trust that a peer has towards another peer is subject to a specific query and approximates the probability that the latter peer will provide a satisfactory answer. In order to compute trust, we exploit the information provided by peers’ ontologies and alignments, along with the information that comes from peers’ experience. Trust values are refined over time as more queries are sent and answers received, and we prove that these approximations converge

Semantic Filtering of Scientific Articles guided by a Domain Ontology

International audienceThe problem that we address in this paper is how to improve the accuracy of retrieving specialized information within a textual scientific corpus. We present a new approach in which the keywords expressing the bibliographical needs of a researcher are related to a domain ontology. We illustrate how such a declarative ontolology-based approach can be used both for computing varied statistics, and also for helping experts to find useful fine-grained information within a textual corpus

A Probabilistic Trust Model for Semantic Peer to Peer Systems

International audienceSemantic peer to peer (P2P) systems are fully decentralized overlay networks of people or machines (called peers) sharing and searching varied resources (documents, videos, photos, data, services) based on their semantic annotations using ontologies. They provide a support for the emergence of open and decentralized electronic social networks, in which no central or external authority can control the reliability of the peers participating to the network. This lack of control may however cause some of the results provided by some peers to be unsatisfactory, because of inadequate or obso- lete annotations. In this paper, we propose a probabilistic model to handle trust in a P2P setting. It supports a local computation and a simple form of propagation of the trust of peers into classes of other peers. We claim that it is well appropriate to the dynamics of P2P networks and to the freedom of each peer within the network to have different viewpoints towards the peers with which it interacts

Reasoning with Inconsistencies in Propositional Peer-to-Peer Inference Systems

International audiencen a peer-to-peer inference system, there is no centralized control or hierarchical organization: each peer is equivalent in functionality and cooperates with other peers in order to solve a collective reasoning task. Since peer theories model possibly different viewpoints, even if each local theory is consistent, the global theory may be inconsistent. We exhibit a distributed algorithm detecting inconsistencies in a fully decentralized setting. We provide a fully distributed reasoning algorithm, which computes only well-founded consequences of a formula, i.e., with a consistent set of support