Ontology based Systems

The Web of Data is an open environment consisting of a great number of large inter-linked RDF datasets from various do-mains. In this environment, organizations and companies adopt the Linked Data practices utilizing Semantic Web (SW) tech-nologies, in order to publish their data and offer SPARQL endpoints (i.e., SPARQL-based search services). On the other hand, the dominant standard for information exchange in the Web today is XML. Additionally, many international standards (e.g., Dublin Core, MPEG-7, METS, TEI, IEEE LOM) in several domains (e.g., Digital Libraries, GIS, Multimedia, e-Learning) have been expressed in XML Schema. The aforementioned have led to an increasing emphasis on XML data, accessed using the XQuery query language. The SW and XML worlds and their developed infrastructures are based on different data models, semantics and query languages. Thus, it is crucial to develop interoperability mechanisms that allow the Web of Data users to access XML datasets, using SPARQL, from their own working environments. It is unrealistic to expect that all the existing legacy data (e.g., Relational, XML, etc.) will be transformed into SW data. Therefore, publishing legacy data as Linked Data and providing SPARQL endpoints over them has become a major research challenge. In this direction, we introduce the SPARQL2XQuery Framework which creates an interoperable environment, where SPARQL queries are automatically translated to XQuery queries, in order to access XML data across the Web. The SPARQL2XQuery Framework provides a mapping model for the expression of OWL–RDF/S to XML Schema mappings as well as a method for SPARQL to XQuery translation. To this end, our Framework supports both manual and automatic mapping specification between ontologies and XML Schemas. In the automatic mapping specification scenario, the SPARQL2XQuery exploits the XS2OWL component which transforms XML Sche-mas into OWL ontologies. Finally, extensive experiments have been conducted in order to evaluate the schema transfor-mation, mapping generation, query translation and query evaluation efficiency, using both real and synthetic datasets.

In this paper, an ontology for the description and indexing of the contents of audiovisual resources will
be presented. The concerned domain of reference (or: knowledge domain) is the cultural heritage of
minorities and indigenous people, hence the name or title of this ontology, PCI ontology (standing for the
French “Patrimoine Culturel de Minorités et Peuples Indigènes”). The whole audiovisual corpus can be
accessed and explored on the PCI audiovisual web portal (figure 1): http://semioweb.mshparis.fr/corpus/pci/fr/

Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and
heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems
operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing
with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the
availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints
for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly
scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for
information abstraction and summarization. Further, they must take into account different user-defined exploration scenarios
and user preferences. In this work, we present a generic model for personalized multilevel exploration and analysis over large
dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently
constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale
model. We define two versions of this structure, that adopt different data organization approaches, well-suited to exploration and
analysis context. In the proposed structure, statistical computations can be efficiently performed on-the-fly. Considering different
exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction
and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough
theoretical analysis is presented, illustrating the efficiency of the proposed model. The proposed model is realized in a web-based
prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets. Finally, we
provide a performance evaluation and a empirical user study employing real datasets.

Fault dependency (D)-matrix is a systematic analytic model to catch the different system-level fault diagnostic information consisting of conditions between recognizable symptoms and failure modes connected with a system. Constructing a D-matrix from earlier principles and redesigning it utilizing the space learning is a difficult task and time intensive assignment. Further, in-time augmentation of D-matrix through the revelation of new symptoms and failure modes watched for the first run through is a testing assignment. An ontology based D-matrix[1] depict an ontology based text mining strategy for consequently constructing and redesigning a D-matrix by mining countless repair verbatim (normally written in unstructured text) gathered during the diagnosis scenes. The system constructs the fault diagnosis ontology consisting of concepts and relationships commonly saw in the fault diagnosis space. Next, utilize the content mining calculations that make utilization of this ontology to distinguish the fundamental ancient rarities, for example, parts, symptoms, failure modes, and their conditions from the unstructured repair verbatim content. In our methodology, we first construct the D-Matrices from [1] for different datasets. Next, we generate graph model for each generated d-matrix and used only common patterns from generated graph and develop new graph. And, after this procedure, we create D-matrix depending upon data given by the similarity graph. Keyword: Fault diagnosis ontology, D-matrix, Text Mining, Unstructured text, Graph comparison algorithm. I. INTRODUCTION Typically, created system lives up to expectations in its pre recognized working conditions and done with its given assignment as expressed. There is nothing to stress over the system until it is working accurately and gives worthy results. In the event that the consequence of the system is not as every the expectation we can say this as introduction of fault in system. Identification of faults and its correction is a subfield of control designing which relate itself with managing a system, recognizing when a fault has happened, what are the reasons behind it and discover the sort of fault and its location [2]. It is important to discover the underlying driver of a fault on the grounds that there may be possibility that other interconnected subsystems might likewise give fault indications that may conceivably shroud the underlying driver. A complex system interfaces with its enveloping to execute a set of endeavors by keeping up its execution inside a satisfactory extent of resilience. Any deviation of a system from its satisfactory execution is managed as a deficiency. The fault detection and diagnosis (FDD) is performed to find the fault and diagnose the hidden drivers to minimize the downtime of a system. On account of relentlessly creating mechanical advancement that is embedded in the vehicle systems, for illustration propelled programming embedded systems, demonstrative sensors, web, along these lines forward the procedure of FDD becomes a testing development in the occasion of part or system glitch. Systems like On-board diagnostics (OBD) alluding to a vehicle's demonstrative toward oneself and reporting ability. These systems give mechanics or manager of the vehicle access to the status of the different vehicle subsystems [3]. In the wake of determining the different issues it is essential to note down its causes, impact of the reason on the system in organized way so we can utilize this information recent while creating the system to make it perfect. But when information which we have assembled is in colossal sum there is need to store this unstructured information pertinently with the goal that we can discover faults by gathering its partner symptoms. The reason for Text Mining is to process unstructured data, get out compelling files from the literary data, and make the information available in the data receptive to the different data mining calculations. We are utilizing D-matrix in light of the fact that it is one of the standard indicative models tagged in IEEE Standard 1232[4]. Be that as it may, the development of a D-matrix by utilizing text mining is a testing assignment part of the way because of the commotions saw in the repair verbatim content information. Regularly the procedure of FD begins by extracting the mistake codes from a target framework and focused around the watched lapse codes the professionals take after particular finding technique alongside their experience to diagnose the shortcomings. At the time of fault determination, a few information sorts are gathered, for example, error codes, examined benefits of working parameters connected with broken part/framework, repair verbatim, and many more. The gathered information is then exchanged to the OEM database and especially the repair verbatim information gathered over a time of time can be mined to create the D-matrix analytic models. Such models can be utilized by the field professionals and other stakeholders to perform precise FDD.

"The representation of characters’ intentions in a
story is of great importance for media scholars and analysts,
and it is susceptible of applicative scenarios within the media
industry. In this paper, we introduce an interactive system for
the visualization of a story analysis based on a plan-based
representation of the characters’ intentions. The system relies on
an ontology of drama and builds upon the unrestricted annotation
provided by narrative enthusiasts and media students. The system
is able to build the mapping between a library of plans and the
users’ annotation, and to visualize the contributions of the several
characters’ intentions to the whole plot. The system was tested
on the analysis of a short movie."

Présentation des projets mis en oeuvre au département des Manuscrits de la BnF pour permettre la mise en interopérabilité des bibliothèques numériques de manuscrits. Le projet principal présenté dans l'article est Shared Canvas, piloté par l'Université Stanford avec le soutien de la fondation Mellon qui vise à utiliser les formats RDF et OpenAnnotation pour permettre l'échange de métadonnées et l'interfaçage entre bibliothèques numériques et outils d'aide à la recherche.

Les sujets développés dans cette conférence :
1) Les « digital humanities » et la question des archives numériques.
2) Evolution des archives numériques vers des plateformes de travail.
3) Les deux étapes centrales lorsqu'on travail avec des archives numériques: a) L’étape de l’archivage numérique à proprement parler ; b) L’étape du « repurposing » des données archivées.
4) Le Studio ASA – une plateforme de travail pour archives audiovisuelles.
5) Le cœur du « Studio ASA » - un métalangage de description des données audiovisuelles.

Call For Papers:
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We envisage a strong undercurrent of intelligent software
agents, mobile agents, and MAS running through this workshop;
side by side with the use of semantic technologies, there are
several foci of interest:

• Software Agents, Mobile Agents, and MAS,
• Agent, MAS and Semantic Web Technologies,
• Ontologies for Agents and MAS,
• Platforms for semantic agent and MAS implementation,
• Semantic Intelligence and Semantic Robotics,
• Other subjects of relevance in semantic technologies, semantic software agents, mobile agents, agent-based and multi-agent systems.