A Large Scale Dataset for the Evaluation of Ontology Matching Systems

Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large scale matching tasks. In this paper we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two dozen of state of the art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it is error-free, incremental, discriminative, monotonic, and hard for the state of the art ontology matching systems. The paper has been accepted for publication in "The Knowledge Engineering Review", Cambridge Universty Press (ISSN: 0269-8889, EISSN: 1469-8005)

Encoding Classifications as Lightweight Ontologies

Classifications have been used for centuries with the goal of cataloguing and searching large sets of objects. In the early days it was mainly books; lately it has also become Web pages, pictures and any kind of electronic information items. Classifications describe their contents using natural language labels, which has proved very effective in manual classification. However natural language labels show their limitations when one tries to automate the process, as they make it very hard to reason about classifications and their contents. In this paper we introduce the novel notion of Formal Classification, as a graph structure where labels are written in a propositional concept language. Formal Classifications turn out to be some form of lightweight ontologies. This, in turn, allows us to reason about them, to associate to each node a normal form formula which univocally describes its contents, and to reduce document classification to reasoning about subsumption

Geospatial data harmonization from regional level to european level: a usa case in forest fire data

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Geospatial data harmonization is becoming more and more important to increase interoperability of heterogeneous data derived from various sources in spatial data infrastructures. To address this harmonization issue we present the current status of data availability among different communities, languages, and administrative scales from regional to national and European levels. With a use case in forest data models in Europe, interoperability of burned area data derived from Europe and Valencia Community in Spain were tested and analyzed on the syntactic, schematic and semantic level. We suggest approaches for achieving a higher chance of data interoperability to guide forest domain experts in forest fire analysis. For testing syntactic interoperability, a common platform in the context of formats and web services was examined. We found that establishing OGC standard web services in a combination with GIS software applications that support various formats and web services can increase the chance of achieving syntactic interoperability between multiple geospatial data derived from different sources. For testing schematic and semantic interoperability, the ontology-based schema mapping approach was taken to transform a regional data model to a European data model on the conceptual level. The Feature Manipulation Engine enabled various types of data transformation from source to target attributes to achieve schematic interoperability. Ontological modelling in Protégé helped identify a common concept between the source and target data models, especially in cases where matching attributes were not found at the schematic level. Establishment of the domain ontology was explored to reach common ground between application ontologies and achieve a higher level of semantic interoperability

Conceptualising the geographic world: the dimensions of negotiation in crowdsourced cartography

In crowdsourced cartographic projects, mappers coordinate their efforts through online tools to produce digital geospatial artefacts, such as maps and gazetteers, which were once the exclusive territory of professional surveyors and cartographers. In order to produce meaningful and coherent data, contributors need to negotiate a shared conceptualisation that defines the domain concepts, such as road, building, train station, forest, and lake, enabling the communi- cation of geographic knowledge. Considering the OpenStreetMap Wiki website as a case study, this article investigates the nature of this negotiation, driven by a small group of mappers in a context of high contribution inequality. De- spite the apparent consensus on the conceptualisation, the negotiation keeps unfolding in a tension between alternative representations, which are often in- commensurable, i.e., hard to integrate and reconcile. In this study, we identify six complementary dimensions of incommensurability that recur in the nego- tiation: (i) ontology, (ii) cartography, (iii) culture and language, (iv) lexical definitions, (v) granularity, and (vi) semantic overload and duplication

A FRAMEWORK FOR BIOPROFILE ANALYSIS OVER GRID

An important trend in modern medicine is towards individualisation of healthcare to tailor care to the needs of the individual. This makes it possible, for example, to personalise diagnosis and treatment to improve outcome. However, the benefits of this can only be fully realised if healthcare and ICT resources are exploited (e.g. to provide access to relevant data, analysis algorithms, knowledge and expertise). Potentially, grid can play an important role in this by allowing sharing of resources and expertise to improve the quality of care. The integration of grid and the new concept of bioprofile represents a new topic in the healthgrid for individualisation of healthcare. A bioprofile represents a personal dynamic "fingerprint" that fuses together a person's current and past bio-history, biopatterns and prognosis. It combines not just data, but also analysis and predictions of future or likely susceptibility to disease, such as brain diseases and cancer. The creation and use of bioprofile require the support of a number of healthcare and ICT technologies and techniques, such as medical imaging and electrophysiology and related facilities, analysis tools, data storage and computation clusters. The need to share clinical data, storage and computation resources between different bioprofile centres creates not only local problems, but also global problems. Existing ICT technologies are inappropriate for bioprofiling because of the difficulties in the use and management of heterogeneous IT resources at different bioprofile centres. Grid as an emerging resource sharing concept fulfils the needs of bioprofile in several aspects, including discovery, access, monitoring and allocation of distributed bioprofile databases, computation resoiuces, bioprofile knowledge bases, etc. However, the challenge of how to integrate the grid and bioprofile technologies together in order to offer an advanced distributed bioprofile environment to support individualized healthcare remains. The aim of this project is to develop a framework for one of the key meta-level bioprofile applications: bioprofile analysis over grid to support individualised healthcare. Bioprofile analysis is a critical part of bioprofiling (i.e. the creation, use and update of bioprofiles). Analysis makes it possible, for example, to extract markers from data for diagnosis and to assess individual's health status. The framework provides a basis for a "grid-based" solution to the challenge of "distributed bioprofile analysis" in bioprofiling. The main contributions of the thesis are fourfold: A. An architecture for bioprofile analysis over grid. The design of a suitable aichitecture is fundamental to the development of any ICT systems. The architecture creates a meaiis for categorisation, determination and organisation of core grid components to support the development and use of grid for bioprofile analysis; B. A service model for bioprofile analysis over grid. The service model proposes a service design principle, a service architecture for bioprofile analysis over grid, and a distributed EEG analysis service model. The service design principle addresses the main service design considerations behind the service model, in the aspects of usability, flexibility, extensibility, reusability, etc. The service architecture identifies the main categories of services and outlines an approach in organising services to realise certain functionalities required by distributed bioprofile analysis applications. The EEG analysis service model demonstrates the utilisation and development of services to enable bioprofile analysis over grid; C. Two grid test-beds and a practical implementation of EEG analysis over grid. The two grid test-beds: the BIOPATTERN grid and PlymGRID are built based on existing grid middleware tools. They provide essential experimental platforms for research in bioprofiling over grid. The work here demonstrates how resources, grid middleware and services can be utilised, organised and implemented to support distributed EEG analysis for early detection of dementia. The distributed Electroencephalography (EEG) analysis environment can be used to support a variety of research activities in EEG analysis; D. A scheme for organising multiple (heterogeneous) descriptions of individual grid entities for knowledge representation of grid. The scheme solves the compatibility and adaptability problems in managing heterogeneous descriptions (i.e. descriptions using different languages and schemas/ontologies) for collaborated representation of a grid environment in different scales. It underpins the concept of bioprofile analysis over grid in the aspect of knowledge-based global coordination between components of bioprofile analysis over grid

Enabling quantitative data analysis through e-infrastructures

This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can benefit from e-infrastructure support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences

Infraestructura tecnológica de servicios semánticos para la Web Semántica

This project aims at creating a network of distributed interoperable semantic services for building more complex ones. These services will be available in semantic Web service libraries, so that they can be invoked by other systems (e.g., semantic portals, software agents, etc.). Thus, to accomplish this objective, the project proposes: a) To create specific technology for developing and composing Semantic Web Services. b) To migrate the WebODE ontology development workbench to this new distributed interoperable semantic service architecture. c) To develop new semantic services (ontology learning, ontology mappings, incremental ontology evaluation, and ontology evolution). d) To develop technological support that eases semantic portal interoperability, using Web services and Semantic Web Services. The project results will be open source, so as to improve their technological transfer. The quality of these results is ensured by a benchmarking process. Keywords: Ontologies and Semantic We