Enrichissement contrôlé de bases de connaissances à partir de documents semi-structurés annotés

International audienceGrâce au Linked Open Data, les sources RDF mises à disposition sur le Web sont de plus en plus nombreuses. Cependant, ces sources contiennent relativement peu d'information par comparaison au volume d'informations contenues dans les documents semi-structurés. De nombreux outils ont pour objectif d'annoter sémantiquement ces documents mais l'extraction de relations reste une tâche particulièrement difficile quand la structure et le vocabulaire des documents sont hétérogènes. Nous proposons une approche permettant d'enrichir et d'interroger une ou plusieurs bases de connaissances RDF/OWL en exploitant un ensemble de documents sémantiquement annotés. Ces bases sont enrichies par des instances de relations incertaines inférées à partir de la structure des documents, des ontologies et des faits présents dans les bases de connaissances. Une requête SPARQL formulée dans le vocabulaire du domaine est reformulée afin de combiner les faits issus des différentes bases et de trier les réponses en fonction de poids assignés. L'approche a été expérimentée sur des documents HTML et des bases de connaissances issues du Linked Open Data. Les résultats montrent que 63,3% des relations trouvées sont nouvelles avec une précision atteignant 62%

A Logic Programming approach for Access Control over RDF

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

A new fuzzy ontology development methodology (FODM) proposal

There is an upsurge in applying fuzzy ontologies to represent vague information in the knowledge representation field. Current research in the fuzzy ontologies paradigm mainly focuses on developing formalism languages to represent fuzzy ontologies, designing fuzzy ontology editors, and building fuzzy ontology applications in different domains. Less focus falls on establishing a formal methodological approach for building fuzzy ontologies. Existing fuzzy ontology development methodologies, such as the IKARUS-Onto methodology and Fuzzy Ontomethodology, provide formalized schedules for the conversion from crisp ontologies into fuzzy ones. However, a formal guidance on how to build fuzzy ontologies from scratch still lacks in current research. Therefore, this paper presents the first methodology, named FODM, for developing fuzzy ontologies from scratch. The proposed FODM can provide a very good guideline for formally constructing fuzzy ontologies in terms of completeness, comprehensiveness, generality, efficiency, and accuracy. To explain how the FODM works and demonstrate its usefulness, a fuzzy seabed characterization ontology is built based on the FODM and described step-by-step

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

From fuzzy to annotated semantic web languages

The aim of this chapter is to present a detailed, selfcontained and comprehensive account of the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions

Data quality issues in electronic health records for large-scale databases

Data Quality (DQ) in Electronic Health Records (EHRs) is one of the core functions that play a decisive role to improve the healthcare service quality. The DQ issues in EHRs are a noticeable trend to improve the introduction of an adaptive framework for interoperability and standards in Large-Scale Databases (LSDB) management systems. Therefore, large data communications are challenging in the traditional approaches to satisfy the needs of the consumers, as data is often not capture directly into the Database Management Systems (DBMS) in a seasonably enough fashion to enable their subsequent uses. In addition, large data plays a vital role in containing plenty of treasures for all the fields in the DBMS. EHRs technology provides portfolio management systems that allow HealthCare Organisations (HCOs) to deliver a higher quality of care to their patients than that which is possible with paper-based records. EHRs are in high demand for HCOs to run their daily services as increasing numbers of huge datasets occur every day. Efficient EHR systems reduce the data redundancy as well as the system application failure and increase the possibility to draw all necessary reports. However, one of the main challenges in developing efficient EHR systems is the inherent difficulty to coherently manage data from diverse heterogeneous sources. It is practically challenging to integrate diverse data into a global schema, which satisfies the need of users. The efficient management of EHR systems using an existing DBMS present challenges because of incompatibility and sometimes inconsistency of data structures. As a result, no common methodological approach is currently in existence to effectively solve every data integration problem. The challenges of the DQ issue raised the need to find an efficient way to integrate large EHRs from diverse heterogeneous sources. To handle and align a large dataset efficiently, the hybrid algorithm method with the logical combination of Fuzzy-Ontology along with a large-scale EHRs analysis platform has shown the results in term of improved accuracy. This study investigated and addressed the raised DQ issues to interventions to overcome these barriers and challenges, including the provision of EHRs as they pertain to DQ and has combined features to search, extract, filter, clean and integrate data to ensure that users can coherently create new consistent data sets. The study researched the design of a hybrid method based on Fuzzy-Ontology with performed mathematical simulations based on the Markov Chain Probability Model. The similarity measurement based on dynamic Hungarian algorithm was followed by the Design Science Research (DSR) methodology, which will increase the quality of service over HCOs in adaptive frameworks