A semantic handling of geological modeling workflows

Advances in Databases and Information Systems

Pierra

2010

Abstract. Nowadays, metamodeling techniques and ontologies play a central role in many computer science problems such as data exchange, integration of heterogeneous data and models or software reuse. Yet, if many metamodeling repositories and ontology repositories have been proposed, few attempts have been made to combine their advantages into a single repository with a dedicated language. In this paper, we present the capabilities of the OntoQL language for managing the various levels of information stored in a metamodeling systems where (1) both data, models and metamodels are available and (2) semantic descriptions are provided using ontologies. Some of the main characteristics of OntoQL are: management of the metamodel level using the same operators as the ones applied to data and to model levels, semantic queries through ontologies and SQL compatibility. We report several applications where this language has been extensively and successfully used.

Section: Resultsmentioning

confidence: 99%

Section: Case Study: Co2 Capture and Storagementioning

confidence: 99%

A Language for Ontology-Based Metamodeling Systems

Jean

Advances in Databases and Information Systems

Pierra

2010

“…The OntoQL language has been put into practice in various engineering projects (e.g, the Ewok-hub French project e ) [13,48,49]. In particular, its capability to manipulate the ontology model has been extensively used.…”

Section: Application Experiencesmentioning

confidence: 99%

OntoQL: An Alternative to Semantic Web Query Languages

Jean

Int. J. Semantic Computing

Pierra

2015

Ontologies are used in several application domains for representing knowledge. The defined approaches differ according to the type of addressed ontology (conceptual or linguistic) and to the used ontology model (e.g. OWL or PLIB). Several languages have been proposed to manipulate ontologies and their instances, especially in the Semantic Web domain. However these languages are often specific to a given ontology model, they focus on conceptual ontologies and they are not compatible with database exploitation languages. We address these three problems in this paper by proposing the OntoQL language. This language has three main original characteristics: (1) OntoQL is based on a core ontology model composed of the shared constructors of ontology models. This core ontology model can be extended by the language itself, (2) OntoQL queries can be expressed with different natural languages features using the linguistic layer of an ontology, and (3) OntoQL is fully compatible with SQL enabling a smooth integration between SQL queries of classical database applications and ontological queries. As a theoretical validation of this language, we present the algebra of operators that sets up its formal semantics. On the operational side, we describe the implementation of OntoQL on the OntoDB database and we illustrate the interest of this language by reporting several applications where this language has been extensively used and proved powerful.

“…To enable the evolution of ontology models, a third database architecture (called T ype 3 ) extending the second one by adding a new part, called the meta-schema part was proposed [9]. The presence of the meta-schema offers the following characteristics: (1) a generic access to the ontology part, (2) a support of the used ontology model's evolution by adding non functional properties such as preferences [13], web services [14], etc. and (3) a storage of different ontology models (OWL, PLIB, etc.).…”

Section: Fig 1: Redundancy Caused By Ontologiesmentioning

confidence: 99%

Be careful when designing semantic databases: Data and concepts redundancy

Chakroun

Bellatreche

IEEE 7th International Conference on Research Challenges in Information Science (RCIS)

et al. 2013

Self Cite

Recently, ontologies have been widely adopted by small, medium and large companies in various domains. These ontologies may contain redundant concepts (computed from primitive concepts). At the beginning of the development of ontologies, the relationship between them and the database was weakly coupled. With the explosion of semantic data, persistent solutions to ensure a high performance of applications were proposed. As a consequence, a new type of database, called semantic database (SDB) is born. Several types of SDB have been proposed and supported by different DBMS, where each one has its architecture and its storage layouts for ontologies and its instances. At this stage, relationship between databases and ontologies becomes strongly coupled. As a consequence, several research studies were proposed on the physical design phase of SDB. To guarantee the similar success that relational databases got, SDB has to be supported by complete design methodologies and tools including the different steps of the database life cycle. Such methodology should identify the redundancy embedded into ontology. In this paper, we propose a design methodology dedicated to SDB including the main phases of the lifecycle of the database development: conceptual, logical, deployment and physical. The conceptual design of SDB can be easily performed by exploiting the similarities between ontologies and conceptual models. The logical design phase is performed thanks to the incorporation of dependencies between concepts and properties in the ontologies. These dependencies are quite similar to the principle of functional dependencies defined in the traditional databases. Due to the diversity of the SDB architectures and the variety of the used storage layouts (horizontal, vertical, binary) to store and manage ontological data, we propose a SDB deploymentà la carte. Finally, a prototype implementing our design approach on Oracle 11g is outlined.