Catálogo de publicaciones - libros

Access to multisource heterogeneous data is a fundamental research issue in a variety of contexts, including syndicated data retrieval, Web service selection and cooperative information systems. In these variable contexts, the brokering approach to multisource data access provides greater flexibility with respect to the more traditional data integration. The general brokering model assumes that the broker is submitted a query and has the responsibility to optimize the response along specified parameters such as time efficiency, completeness, and consistency. This paper takes a data quality perspective on data brokering and considers data accuracy. Furthermore, the data quality literature assumes that metadata are associated with data to describe their quality. Metadata support data selection without viewing and assessing data directly. On the contrary, previous brokering approaches view data. This paper compares previous results with those of a brokering approach based on metadata which assumes that actual data are transparent to the broker. Testing results comparing the delta between the data visibility and transparency approaches to data brokering are presented.

This paper outlines a prototypical work bench which offers semantically enhanced analytical capabilities to the business analyst. The business case for such an environment is outlined and user scenario development used to illustrate system requirements. Based upon ideas from meta-discourse and exploiting advances within the fields of ontology engineering, annotation, natural language processing and personal knowledge management, the Analyst Work Bench offers the automated identification of, and between business discourse items with possible propositional content. The semantically annotated results are visually presented allowing personalised report path traversal marked up against the original source.

The theoretical results achieved in the ontology engineering field in the last fifteen years are of incontestable value for the prospected large scale take-up of semantic technologies. Their range of application in real-world projects is, however, so far comparatively limited, despite the growing number of ontologies online available. This restricted impact was confirmed in a three month empirical study, in which we examined over 34 contemporary ontology development projects from a process- and costs-oriented perspective. In this paper we give an account of the results of this study. We conclude that ontology engineering research should strive for a unified, lightweight and component-based methodological framework, principally targeted at domain experts, in addition to consolidating the existing approaches.

Regardless of the knowledge representation schema chosen to implement a linguistic resource, conceptual design is an important step in its development. However, it is normally put aside by developing efforts as they focus on content, implementation and time-saving issues rather than on the software engineering aspects of the construction of linguistic resources. Based on an analysis of common problems found in linguistic resources, we present a reusable conceptual model which incorporates elements that give ontology developers the possibility to establish formal semantic descriptions for concepts and relations, and thus avoiding the aforementioned common problems. The model represents a step forward in our efforts to define a complete methodology for the design and implementation of ontology-based linguistic resources using relational databases and a sound software engineering approach for knowledge representation.

Interoperability of heterogeneous data sources has been extensively studied in data integration applications. However, the increasing number of tools that produce data with very different formats, such as bug tracking, version control, etc., produces many different kinds of semantic heterogeneities. These semantic heterogeneities can be expressed as mappings between the tools metadata which describe the data manipulated by the tools. However, the semantics of complex mappings (, and relationships) is hard to support. These mappings are usually directly coded in executable transformations using arithmetic expressions. And there is no mechanism to create and reuse complex mappings. In this paper we propose a novel approach to capture different kinds of complex mappings using correspondence models. The main advantage is to use high level specifications for the correspondence models that enable representing different kinds of mappings. The correspondence models may be used to automatically produce executable transformations. To validate our approach, we provide an experimentation with a real world scenario using bug tracking tools.

Defining and composing mappings are fundamental operations required in any data sharing architecture (e.g. data warehouse, data integration). Mapping composition is used to generate new mappings from existing ones and is useful when no direct mapping is available. The complexity of mapping composition depends on the amount of syntactic and semantic information in the mapping. The composition of mappings has proven to be inefficient to compute in many situations unless the mappings are simplified to binary relationships that represent “similarity” between concepts. Our contribution is an algorithm for composing metadata mappings that capture explicit semantics in terms of binary relationships. Our approach allows the hard cases of mapping composition to be detected and semi-automatically resolved, and thus reduces the manual effort required during composition. We demonstrate how the mapping composition algorithm is used to produce a direct mapping between schemas from independently produced schema-to-ontology mappings. An experimental evaluation shows that composing semantic mappings results in a more accurate composition result compared to composing mappings as morphisms.

This paper presents a new ontology mapping method. This method addresses the case in which a non-structured ontology is to be mapped with a structured one. Both ontologies are composed of triplets of the form (object, characteristic, value). Structured means that the values describing the objects according to a given characteristic are hierarchically organized using the relation. The proposed method uses fuzzy conceptual graphs [8] to represent and map objects from a source ontology to a target one. First, we establish a correspondence between characteristics of the source ontology and characteristics of the target ontology based on the comparison of their associated values. Then, we propose an original way of translating the description of an object of the source ontology using characteristics and values of the target ontology. The description thus translated is represented as a fuzzy conceptual graph. Finally, a new projection operation is used to find mappings between translated objects and actual objects of the target ontology. This method has been implemented and the results of an experimentation concerning the mapping of ontologies in the field of risk in food are presented.

Recently, the advantages of metamodeling for the graphical specification of ontologies have been recognized by the semantic web community. This has lead to a number of activities concerned with the development of graphical modeling approaches for the Web Ontology Language based on the Meta Object Facility (MOF) and the Unified Modeling Language (UML). An aspect that has not been addressed so far is the need to specify mappings between heterogenous ontologies. With an increasing number of ontologies being available, the problem of specifying mappings is becoming more important and the rationales for providing model based graphical modeling support for mappings is the same as for the ontologies themselves. In this paper, we therefore propose a MOF-based metamodel for mappings between OWL DL ontologies.

The paper presents the development of a virtual database for the genotypic selection of cereal cultivars starting from phenotypic traits.

The database is realized by integrating two existing web databases, Gramene and Graingenes, and a pre-existing data source developed by the Agrarian Faculty of the University of Modena and Reggio Emilia. The integration process gives rise to a virtual integrated view of the underlying sources. This integration is obtained using the MOMIS system (Mediator envirOnment for Multiple Information Sources), a framework developed by the Database Group of the University of Modena and Reggio Emilia (www.dbgroup.unimo.it). MOMIS performs information extraction and integration from both structured and semistructured data sources. Information integration is performed in a semi-automatic way, by exploiting the knowledge in a Common Thesaurus (defined by the framework) and the descriptions of source schemas with a combination of clustering and Description Logics techniques. Momis allows querying information in a transparent mode for the user regardless of the specific languages of the sources. The result obtained by applying MOMIS to Gramene and Graingenes web databases is a queriable virtual view that integrates the two sources and allow performing genotypic selection of cultivars of barley, wheat and rice based on phenotypic traits, regardless of the specific languages of the web databases. The project is conducted in collaboration with the Agrarian Faculty of the University of Modena and Reggio Emilia and funded by the Regional Government of Emilia Romagna.

In this paper, we introduce a mobile services environment, namely SMOP, in which semantic web based service capability matching and location-aware information gathering are both used to develop mobile applications. Domain independency and support on semantic matching in mobile service capabilities are the innovative features of the proposed environment. Built-in semantic matching engine of the environment provides the addition of new service domain ontologies which is critical in terms of system extensibility. Therefore the environment is generic in terms of developing various mobile applications and provides most relevant services for mobile users by applying semantic capability matching in service lookups. GPS (Global Positioning System) and map service utilization cause to find near services in addition to capability relevancy. The software architecture and system extensibility support of the environment are discussed in the paper. The real life implementation of the environment for the estate domain is also given as a case study in the evaluation section of the paper.