Ontology-Based Data Access and Integration

An ontology-based data integration (OBDI) system is an information management system consisting of three components: an ontology, a set of data sources, and the mapping between the two. The ontology is a conceptual, formal description of the domain of interest to a given organization (or a community of users), expressed in terms of relevant concepts, attributes of concepts, relationships between concepts, and logical assertions characterizing the domain knowledge. The data sources are the repositories accessible by the organization where data concerning the domain are stored. In the general case, such repositories are numerous, heterogeneous, each one managed and maintained independently from the others. The mapping is a precise specification of the correspondence between the data contained in the data sources and the elements of the ontology. The main purpose of an OBDI system is to allow information consumers to query the data using the elements in the ontology as predicates. In the special case where the organization manages a single data source, the term ontology-based data access (ODBA) system is used

No users no dataspaces! Query-driven dataspace orchestration

Data analysis in rich spaces of heterogeneous data sources is an increasingly common activity. Examples include querying the web of linked data and personal information management. Such analytics on dataspaces is often iterative and dynamic, in an open-ended interaction between discovery and data orchestration. The current state of the art in integration and orchestration in dataspaces is primarily geared towards close-ended analysis, targeting the discovery of stable data mappings or one-time, pay-as-you-go ad hoc data mappings. The perspective here is dataspace-centric. In this paper, we propose a shift to a user-centric perspective on dataspace orchestration. We outline basic conceptual and technical challenges in supporting data analytics which is open-ended and always evolving, as users respond to new discoveries and connections

Fusing Automatically Extracted Annotations for the Semantic Web

This research focuses on the problem of semantic data fusion. Although various solutions have been developed in the research communities focusing on databases and formal logic, the choice of an appropriate algorithm is non-trivial because the performance of each algorithm and its optimal configuration parameters depend on the type of data, to which the algorithm is applied. In order to be reusable, the fusion system must be able to select appropriate techniques and use them in combination. Moreover, because of the varying reliability of data sources and algorithms performing fusion subtasks, uncertainty is an inherent feature of semantically annotated data and has to be taken into account by the fusion system. Finally, the issue of schema heterogeneity can have a negative impact on the fusion performance. To address these issues, we propose KnoFuss: an architecture for Semantic Web data integration based on the principles of problem-solving methods. Algorithms dealing with different fusion subtasks are represented as components of a modular architecture, and their capabilities are described formally. This allows the architecture to select appropriate methods and configure them depending on the processed data. In order to handle uncertainty, we propose a novel algorithm based on the Dempster-Shafer belief propagation. KnoFuss employs this algorithm to reason about uncertain data and method results in order to refine the fused knowledge base. Tests show that these solutions lead to improved fusion performance. Finally, we addressed the problem of data fusion in the presence of schema heterogeneity. We extended the KnoFuss framework to exploit results of automatic schema alignment tools and proposed our own schema matching algorithm aimed at facilitating data fusion in the Linked Data environment. We conducted experiments with this approach and obtained a substantial improvement in performance in comparison with public data repositories

Conceptual Metaphor, Human-Computer Interaction And Music: Applying Conceptual Metaphor To The Design And Analysis Of Music Interactions

Interaction design for domains that involve complex abstractions can present significant challenges. This problem is particularly acute in domains where users lack effective means to conceptualise and articulate relevant abstractions. In this thesis, we investigate the use of domain-specific conceptual metaphors to address the challenge of presenting complex abstractions, using tonal harmony as an extended case study. This thesis presents a methodology for applying domain-specific conceptual metaphors to interactions designs for music. This domain involves complex abstractions where users with any degree of domain knowledge may have difficulty in articulating concepts. The methodology comprises several parts. Firstly, the thesis explores methods for systematically guiding conversation between musicians to elicit speech that describes music using conceptual metaphors. Recommendations for the most suitable methods are made. Secondly, the thesis presents a methodology for identifying image schemas and conceptual metaphors from transcriptions of conversations between musicians. The methodology covers rules for identifying source image schemas and extrapolating conceptual metaphors. Thirdly, the thesis presents a methodology for evaluating existing music interaction designs using domain-specific conceptual metaphors. We demonstrate that this approach can be used to identify potential areas for improvement as well as tensions in the design between certain tasks or abstractions. Fourthly, the thesis presents a case study for the development of a conceptual metaphor-influenced design process. In the case study, a set of materials are developed to be used by participants in the design process to facilitate the mapping of conceptual metaphors to elements of an interaction design without requiring knowledge of Conceptual Metaphor Theory. Finally, a pilot study is presented integrating the results of the conceptual metaphor-influenced design process into a consistent and useful prototype system. Compromises and refinements to the design proposals made during the design process are discussed and the resulting system design is detailed

Knowledge Refinement via Rule Selection

In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting a subset of rules based on the expected operational behavior of the rules on available data. In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. We first establish computational hardness results for the decision problems underlying this minimization problem, as well as upper and lower bounds for its approximability. We then investigate a bi-objective optimization version of the rule selection problem in which both the total error and the size of the selected rules are taken into account. We show that testing for membership in the Pareto front of this bi-objective optimization problem is DP-complete. Finally, we show that a similar DP-completeness result holds for a bi-level optimization version of the rule selection problem, where one minimizes first the total error and then the size

Investigating the feasibility of a distributed, mapping-based, approach to solving subject interoperability problems in a multi-scheme, cross-service, retrieval environment

The HILT project is researching the problems of facilitating interoperability of subject descriptions in a distributed multi-scheme environment. HILT Phase I found a UK community consensus in favour of utilising an inter-scheme mapping service to improve interoperability. HILT Phase II investigated the approach by building a pilot server, and identified a range of issues that would have to be tackled if an operational service was to be successful. HILT Phase III will implement a centralised version of an M2M pilot, but will aim to design it so that the possibility of a move to a distributed service remains open. This aim will impact on likely future research concerns in Phase III and beyond. Wide adoption of a distributed approach to the problem could lead to the creation of a framework within which regional, national, and international efforts in the area can be harmonised and co-ordinated

A collective, probabilistic approach to schema mapping using diverse noisy evidence

We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise

Metamodel Instance Generation: A systematic literature review

Modelling and thus metamodelling have become increasingly important in Software Engineering through the use of Model Driven Engineering. In this paper we present a systematic literature review of instance generation techniques for metamodels, i.e. the process of automatically generating models from a given metamodel. We start by presenting a set of research questions that our review is intended to answer. We then identify the main topics that are related to metamodel instance generation techniques, and use these to initiate our literature search. This search resulted in the identification of 34 key papers in the area, and each of these is reviewed here and discussed in detail. The outcome is that we are able to identify a knowledge gap in this field, and we offer suggestions as to some potential directions for future research.Comment: 25 page