Pablo Rubén Fillottrani

Ontology-driven geographic information integration: A survey of current approaches

Integrating different information sources is a growing research area within different application domains. This is particularly true for the geographic information domain which is facing new challenges because newer and better technologies are capturing large amounts of information about the Earth. This trend can be combined with increases in the distribution of GIS (Geographic Information Systems) on the Web, which is leading to the proliferation of different geospatial information repositories and the subsequent need to integrate information across repositories to get consistent information. To overcome this situation, many proposals use ontologies in the integration process. In this paper we analyze and compare the most widely referred proposals of geographic information integration, focusing on those using ontologies as semantic tools to represent the sources, and to facilitate the integration process.

The ICOM 3.0 intelligent conceptual modelling tool and methodology

ICOM (version 3.0) is an advanced conceptual modelling tool, which allows the user to design multiple extended ontologies. Each project can be organised into several ontologies, with the possibility to include inter- and intra-ontology constraints. Complete logical reasoning is employed by the tool to verify the specification, infer implicit facts, devise stricter constraints, and manifest any inconsistency. ICOM is fully integrated with a very powerful description logic reasoning server which acts as a background inference engine. The intention behind ICOM is to provide a simple conceptual modelling tool that demonstrates the use of, and stimulates interest in, the novel and powerful knowledge representation based technologies for database and ontology design.

Toward an Ontology-Driven Unifying Metamodel for UML Class Diagrams, EER, and ORM2

Software compatibility and application integration can be achieved using their respective conceptual data models. However, each model may be represented in a different language. While such languages seem similar yet known to be distinct, no unifying framework exists that respects all of their language features. Aiming toward filling this gap, we designed a common, ontology-driven, metamodel of the static, structural, components of ER, EER, UML v2.4.1, ORM, and ORM2, such that each is a fragment of the encompassing consistent metamodel. This paper presents and overview and notable insights obtained on the real common core entities and constraints, roles and relationships, and attributes and value types that we refine with the notion of dimensional attribute.

Performance prediction of the hough transform

Based on three different implementation schemes, this paper strongly demonstrates that the performance of the Hough transform depends crucially on its implementation scheme when it is used for line detection. Moreover, the obtained results can be used as a theoretical basis to predict the performance of the Hough transform as well as to eliminate the noise in Hough space coming from image noise.

Conceptual Model Interoperability: A Metamodel-driven Approach

Linking, integrating, or converting conceptual data models represented in different modelling languages is a common aspect in the design and maintenance of complex information systems. While such languages seem similar, they are known to be distinct and no unifying framework exists that respects all of their language features in either model transformations or inter-model assertions to relate them. We aim to address this issue using an approach where the rules are enhanced with a logic-based metamodel. We present the main approach and some essential metamodel-driven rules for the static, structural, components of ER, EER, UML v2.4.1, ORM, and ORM2. The transformations for model elements and patterns are used with the metamodel to verify correctness of inter-model assertions across models in different languages.

An Analysis and Characterisation of Publicly Available Conceptual Models

Multiple conceptual data modelling languages exist, with newer version typically having more features to model the universe of discourse more precisely. The question arises, however, to what extent those features are actually used in extant models, and whether characteristic profiles can be discerned. We quantitatively evaluated this with a set of 105 UML Class Diagrams, ER and EER models, and ORM and ORM2 diagrams. When more features are available, they are used, but few times. Only 64 % of the entities are the kind of entities that appear in all three language families. Different profiles are identified that characterise how a typical UML, (E)ER and ORM diagram looks like.

Government chief information officer (GCIO) ontology: a tool to formalize the GCIO function

Information Technology (IT) leadership is essential for the successful utilization of Information and Communication Technology (ICT) in any organizational context. In particular, IT leadership is a critical success factor for every Electronic Government (e-Government) initiative. Most of the leading countries in e-Government development have adopted the Chief Information Officer (GCIO) function to lead and coordinate their technology-related projects. In addition, most influential international e-Government rankings -- like those conducted by United Nations and Waseda University; include the presence of the function in their assessment criteria. However, the adoption of the function entails broader actions than the merely establishment of the position itself. Several prerequisites, such as qualified human resources, coordination and collaboration capabilities, and governance mechanisms, among others, need to be available in government for the proper establishment and sustainability of the function. Despite its broader adoption in practice, there is scarce literature to assist governments in understanding the complexities of the GCIO function and making the prerequisites available. This paper presents the development of ontology to formally define the GCIO function. The main contribution of this work is to offer a tool for sharing and reusing the existing knowledge in the GCIO domain, filling the research-practice gap identified above.

Patterns for Heterogeneous TBox Mappings to Bridge Different Modelling Decisions

Correspondence patterns have been proposed as templates of commonly used alignments between heterogeneous elements in ontologies, although design tools are currently not equipped with handling these definition alignments nor pattern alignments. We aim to address this by, first, formalising the notion of design pattern; secondly, defining typical modelling choice patterns and their alignments; and finally, proposing algorithms for integrating automatic pattern detection into existing ontology design tools. This gave rise to six formalised pattern alignments and two efficient local search and pattern matching algorithms to propose possible pattern alignments to the modeller.

Evidence-Based Languages for Conceptual Data Modelling Profiles

To improve database system quality as well as runtime use of conceptual models, many logic-based reconstructions of conceptual data modelling languages have been proposed in a myriad of logics. They each cover their features to a greater or lesser extent and are typically motivated from a logic viewpoint. This raises questions such as what would be an evidence-based common core and what is the optimal language profile for a conceptual modelling language family. Based on a common metamodel of UML Class Diagrams (v2.4.1), ER/EER, and ORM/2’s static elements, a set of 101 conceptual models, and availing of computational complexity insights from Description Logics, we specify these profiles. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable \(\mathcal {ALNI}\)). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models.

Government information sharing: a model for classifying benefits, barriers and risks

Government Information Sharing (Gov-IS) allows information exchange between different government agencies, as well as between public and private institutions. Sharing information enables enhanced efficiency -- by avoiding duplication of processes updating the same data; better quality of processes and services -- by removing inconsistent data and reducing error; and improved transparency -- by facilitating access to information. The implementation of Gov-IS initiatives requires different types of initiatives -- technical, organizational, institutional and political. In this work, we present findings of our research work studying Gov-IS benefits, barriers and risks. Our research relies on secondary data and case studies of Gov-IS initiatives implemented in four countries -- Australia, Estonia, New Zealand and USA. The main contribution of this paper is the multi-view classification of Gov-IS benefits, barriers and risks, and a detailed list of instances of such concepts, providing a comprehensive and benchmarking mechanism to understand, analyze and justify the planning and development of Gov-IS initiatives.