Antoine Zimmermann

A general framework for representing, reasoning and querying with annotated Semantic Web data

We describe a generic framework for representing and reasoning with annotated Semantic Web data, a task becoming more important with the recent increased amount of inconsistent and non-reliable meta-data on the Web. We formalise the annotated language, the corresponding deductive system and address the query answering problem. Previous contributions on specific RDF annotation domains are encompassed by our unified reasoning formalism as we show by instantiating it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we provide a generic method for combining multiple annotation domains allowing to represent, e.g., temporally-annotated fuzzy RDF. Furthermore, we address the development of a query language - AnQL - that is inspired by SPARQL, including several features of SPARQL 1.1 (subqueries, aggregates, assignment, solution modifiers) along with the formal definitions of their semantics.

Scalable and distributed methods for entity matching, consolidation and disambiguation over linked data corpora

With respect to large-scale, static, Linked Data corpora, in this paper we discuss scalable and distributed methods for entity consolidation (aka. smushing, entity resolution, object consolidation, etc.) to locate and process names that signify the same entity. We investigate (i) a baseline approach, which uses explicit owl: sameAs relations to perform consolidation; (ii) extended entity consolidation which additionally uses a subset of OWL 2 RL/RDF rules to derive novel owl:sameAs relations through the semantics of inverse-functional properties, functional-properties and (max-)cardinality restrictions with value one; (iii) deriving weighted concurrence measures between entities in the corpus based on shared inlinks/outlinks and attribute values using statistical analyses; (iv) disambiguating (initially) consolidated entities based on inconsistency detection using OWL 2 RL/RDF rules. Our methods are based upon distributed sorts and scans of the corpus, where we deliberately avoid the requirement for indexing all data. Throughout, we offer evaluation over a diverse Linked Data corpus consisting of 1.118 billion quadruples derived from a domain-agnostic, open crawl of 3.985 million RDF/XML Web documents, demonstrating the feasibility of our methods at that scale, and giving insights into the quality of the results for real-world data.

AnQL: SPARQLing up annotated RDFS

Starting from the general framework for Annotated RDFS which we presented in previous work (extending Udrea et als Annotated RDF), we address the development of a query language - AnQL - that is inspired by SPARQL, including several features of SPARQL 1.1. As a side effect we propose formal definitions of the semantics of these features (subqueries, aggregates, assignment, solution modifiers) which could serve as a basis for the ongoing work in SPARQL 1.1. We demonstrate the value of such a framework by comparing our approach to previously proposed extensions of SPARQL and show that AnQL generalises and extends them.

Reasoning with a Network of Aligned Ontologies

In the context of the Semantic Web or semantic peer to peer systems, many ontologies may exist and be developed independently. Ontology alignments help integrating, mediating or reasoning with a system of networked ontologies. Though different formalisms have already been defined to reason with such systems, they do not consider ontology alignments as first class objects designed by third party ontology matching systems. Correspondences between ontologies are often asserted from an external point of view encompassing both ontologies. We study consistency checking in a network of aligned ontologies represented in Integrated Distributed Description Logics ( IDDL ). This formalism treats local knowledge (ontologies) and global knowledge (inter-ontology semantic relations, i.e. alignments) separately by distinguishing local interpretations and global interpretation so that local systems do not need to directly connect to each other. We consequently devise a correct and complete algorithm which, although being far from tractacle, has interesting properties: it is independent from the local logics expressing ontologies by encapsulating local reasoners. This shows that consistency of a IDDL system is decidable whenever consistency of the local logics is decidable. Moreover, the expressiveness of local logics does not need to be known as long as local reasoners can handle at least

Reconsidering the social web of things: position paper

\mathcal{ALC}

A methodological approach for ontologising and aligning health level seven (HL7) applications

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A Logic Programming approach for Access Control over RDF

The notion of a Social Web of Things (SWoT) appears in recent works at the convergence of the Social Web and the Web of Things. In our vision, a third dimension is needed: pro-activeness. We propose to extend and transform social networks by integrating autonomous and proactive things. In this paper, we discuss the evolution of the Web on several dimensions, leading to our vision for the SWoT. We discuss the challenges that need to be addressed, a possible approach for addressing them and we illustrate the applicability of the SWoT through a motivating scenario.

A SPARQL Extension for Generating RDF from Heterogeneous Formats

Healthcare applications are complex in the way data and schemas are organised in their internal systems. Widely deployed healthcare standards like Health Level Seven (HL7) V2 are designed using flexible schemas which allow several choices when constructing clinical messages. The recently emerged HL7 V3 has a centrally consistent information model that controls terminologies and concepts shared by V3 applications. V3 information models are arranged in several layers (abstract to concrete layers). V2 and V3 systems raise interoperability challenges: firstly, how to exchange clinical messages between V2 and V3 applications, and secondly, how to integrate globally defined clinical concepts with locally constructed concepts. The use of ontologies for interoperable healthcare applications has been advocated by domain and knowledge representation specialists. This paper addresses two main areas of an ontology-based integration framework: (1) an ontology building methodology for the HL7 standard where ontologies are developed in separated global and local layers; and (2) aligning V2 and V3 ontologies. We propose solutions that: (1) provide a semi-automatic mechanism to build HL7 ontologies; (2) provide a semi-automatic mechanism to align HL7 ontologies and transform underlying clinical messages. The proposed methodology has developed HL7 ontologies of 300 concepts in average for each version. These ontologies and their alignments are deployed and evaluated under a semantically-enabled healthcare integration framework.

Semantic Web Evaluation Challenges

The Resource Description Framework (RDF) is an interoperable data representation format suitable for interchange and integration of data, especially in Open Data contexts. However, RDF is also becoming increasingly attractive in scenarios involving sensitive data, where data protection is a major concern. At its core, RDF does not support any form of access control and current proposals for extending RDF with access control do not fit well with the RDF representation model. Considering an enterprise scenario, we present a modelling that caters for access control over the stored RDF data in an intuitive and transparent manner. For this paper we rely on Annotated RDF, which introduces concepts from Annotated Logic Programming into RDF. Based on this model of the access control annotation domain, we propose a mechanism to manage permissions via application-specific logic rules. Furthermore, we illustrate how our Annotated Query Language (AnQL) provides a secure way to query this access control annotated RDF data.

Logical Formalisms for Agreement Technologies

RDF aims at being the universal abstract data model for structured data on the Web. While there is effort to convert data in RDF, the vast majority of data available on the Web does not conform to RDF. Indeed, exposing data in RDF, either natively or through wrappers, can be very costly. Furthermore, in the emerging Web of Things, resource constraints of devices prevent from processing RDF graphs. Hence one cannot expect that all the data on the Web be available as RDF anytime soon. Several tools can generate RDF from non-RDF data, and transformation or mapping languages have been designed to offer more flexible solutions (GRDDL, XSPARQL, R2RML, RML, CSVW, etc.). In this paper, we introduce a new language, SPARQL-Generate, that generates RDF from: (i) a RDF Dataset, and (ii) a set of documents in arbitrary formats. As SPARQL-Generate is designed as an extension of SPARQL 1.1, it can provably: (i) be implemented on top on any existing SPARQL engine, and (ii) leverage the SPARQL extension mechanism to deal with an open set of formats. Furthermore, we show evidence that (iii) it can be easily learned by knowledge engineers that know SPARQL 1.1, and (iv) our first naive open source implementation performs better than the reference implementation of RML for big transformations.