Harvesting online ontologies for ontology evolution

Ontologies need to evolve to keep their domain representation adequate. However, the process of identifying new domain changes, and applying them to the ontology is tedious and time-consuming. Our hypothesis is that online ontologies can provide background knowledge to decrease user efforts during ontology evolution, by integrating new domain concepts through automated relation discovery and relevance assessment techniques, while resulting in ontologies of similar qualities to when the ontology engineers' knowledge is solely used. We propose, implement and evaluate solutions that exploit the conceptual connections and structure of online ontologies to first, automatically suggest new additions to the ontology in the form of concepts derived from domain data, and their corresponding connections to existing elements in the ontology; and second, to automatically evaluate the proposed changes in terms of relevance with respect to the ontology under evolution, by relying on a novel pattern-based technique for relevance assessment. We also present in this thesis various experiments to test the feasibility of each proposed approach separately, in addition to an overall evaluation that validates our hypothesis that user time during evolution is indeed decreased through the use of online ontologies, with comparable results to a fully manual ontology evolution

Linked Vocabulary Recommendation Tools for Internet of Things: A Survey

The Semantic Web emerged with the vision of eased integration of heterogeneous, distributed data on the Web. The approach fundamentally relies on the linkage between and reuse of previously published vocabularies to facilitate semantic interoperability. In recent years, the Semantic Web has been perceived as a potential enabling technology to overcome interoperability issues in the Internet of Things (IoT), especially for service discovery and composition. Despite the importance of making vocabulary terms discoverable and selecting most suitable ones in forthcoming IoT applications, no state-of-the-art survey of tools achieving such recommendation tasks exists to date. This survey covers this gap, by specifying an extensive evaluation framework and assessing linked vocabulary recommendation tools. Furthermore, we discuss challenges and opportunities of vocabulary recommendation and related tools in the context of emerging IoT ecosystems. Overall, 40 recommendation tools for linked vocabularies were evaluated, both, empirically and experimentally. Some of the key ndings include that (i) many tools neglect to thoroughly address both, the curation of a vocabulary collection and e ective selection mechanisms; (ii) modern information retrieval techniques are underrepresented; and (iii) the reviewed tools that emerged from Semantic Web use cases are not yet su ciently extended to t today’s IoT projects

Activities of daily living ontology for ubiquitous systems:Development and evaluation

Ubiquitous eHealth systems based on sensor technologies are seen as key enablers in the effort to reduce the financial impact of an ageing society. At the heart of such systems sit activity recognition algorithms, which need sensor data to reason over, and a ground truth of adequate quality used for training and validation purposes. The large set up costs of such research projects and their complexity limit rapid developments in this area. Therefore, information sharing and reuse, especially in the context of collected datasets, is key in overcoming these barriers. One approach which facilitates this process by reducing ambiguity is the use of ontologies. This article presents a hierarchical ontology for activities of daily living (ADL), together with two use cases of ground truth acquisition in which this ontology has been successfully utilised. Requirements placed on the ontology by ongoing work are discussed

A semantic framework for ontology usage analysis

The Semantic Web envisions a Web where information is accessible and processable by computers as well as humans. Ontologies are the cornerstones for realizing this vision of the Semantic Web by capturing domain knowledge by defining the terms and the relationship between these terms to provide a formal representation of the domain with machine-understandable semantics. Ontologies are used for semantic annotation, data interoperability and knowledge assimilation and dissemination.In the literature, different approaches have been proposed to build and evolve ontologies, but in addition to these, one more important concept needs to be considered in the ontology lifecycle, that is, its usage. Measuring the “usage” of ontologies will help us to effectively and efficiently make use of semantically annotated structured data published on the Web (formalized knowledge published on the Web), improve the state of ontology adoption and reusability, provide a usage-based feedback loop to the ontology maintenance process for a pragmatic conceptual model update, and source information accurately and automatically which can then be utilized in the other different areas of the ontology lifecycle. Ontology Usage Analysis is the area which evaluates, measures and analyses the use of ontologies on the Web. However, in spite of its importance, no formal approach is present in the literature which focuses on measuring the use of ontologies on the Web. This is in contrast to the approaches proposed in the literature on the other concepts of the ontology lifecycle, such as ontology development, ontology evaluation and ontology evolution. So, to address this gap, this thesis is an effort in such a direction to assess, analyse and represent the use of ontologies on the Web.In order to address the problem and realize the abovementioned benefits, an Ontology Usage Analysis Framework (OUSAF) is presented. The OUSAF Framework implements a methodological approach which is comprised of identification, investigation, representation and utilization phases. These phases provide a complete solution for usage analysis by allowing users to identify the key ontologies, and investigate, represent and utilize usage analysis results. Various computation components with several methods, techniques, and metrics for each phase are presented and evaluated using the Semantic Web data crawled from the Web. For the dissemination of ontology-usage-related information accessible to machines and humans, The U Ontology is presented to formalize the conceptual model of the ontology usage domain. The evaluation of the framework, solution components, methods, and a formalized conceptual model is presented, indicating the usefulness of the overall proposed solution

A smart environments architecture (Search)

We report on a system architecture, SEArch, and its associated methods and tools we have been developing, testing and extending for several years through a number of innovation processes in the field of Smart Environments. We have developed these infrastructure in a bottom up fashion directed by the needs of the different projects as opposed to an ideal one which projects have to conform to. In this sense is practical and although necessarily incomplete, it has significant versatility and reasonable efficiency. Projects developed using this architecture have been funded by different companies and funding bodies in Europe. The different components of the architecture are explained through the software supporting those aspects of the system and through the functionality they exhibit in different practical scenarios, extracted from some of the projects implemented with SEArch

Ontology Ranking: Finding the Right Ontologies on the Web

Ontology search, which is the process of finding ontologies or ontological terms for users’ defined queries from an ontology collection, is an important task to facilitate ontology reuse of ontology engineering. Ontology reuse is desired to avoid the tedious process of building an ontology from scratch and to limit the design of several competing ontologies that represent similar knowledge. Since many organisations in both the private and public sectors are publishing their data in RDF, they increasingly require to find or design ontologies for data annotation and/or integration. In general, there exist multiple ontologies representing a domain, therefore, finding the best matching ontologies or their terms is required to facilitate manual or dynamic ontology selection for both ontology design and data annotation. The ranking is a crucial component in the ontology retrieval process which aims at listing the ‘relevant0 ontologies or their terms as high as possible in the search results to reduce the human intervention. Most existing ontology ranking techniques inherit one or more information retrieval ranking parameter(s). They linearly combine the values of these parameters for each ontology to compute the relevance score against a user query and rank the results in descending order of the relevance score. A significant aspect of achieving an effective ontology ranking model is to develop novel metrics and dynamic techniques that can optimise the relevance score of the most relevant ontology for a user query. In this thesis, we present extensive research in ontology retrieval and ranking, where several research gaps in the existing literature are identified and addressed. First, we begin the thesis with a review of the literature and propose a taxonomy of Semantic Web data (i.e., ontologies and linked data) retrieval approaches. That allows us to identify potential research directions in the field. In the remainder of the thesis, we address several of the identified shortcomings in the ontology retrieval domain. We develop a framework for the empirical and comparative evaluation of different ontology ranking solutions, which has not been studied in the literature so far. Second, we propose an effective relationship-based concept retrieval framework and a concept ranking model through the use of learning to rank approach which addresses the limitation of the existing linear ranking models. Third, we propose RecOn, a framework that helps users in finding the best matching ontologies to a multi-keyword query. There the relevance score of an ontology to the query is computed by formulating and solving the ontology recommendation problem as a linear and an optimisation problem. Finally, the thesis also reports on an extensive comparative evaluation of our proposed solutions with several other state-of-the-art techniques using real-world ontologies. This thesis will be useful for researchers and practitioners interested in ontology search, for methods and performance benchmark on ranking approaches to ontology search

Case reuse in textual case-based reasoning.

Text reuse involves reasoning with textual solutions of previous problems to solve new similar problems. It is an integral part of textual case-based reasoning (TCBR), which applies the CBR problem-solving methodology to situations where experiences are predominantly captured in text form. Here, we explore two key research questions in the context of textual reuse: firstly what parts of a solution are reusable given a problem and secondly how might these relevant parts be reused to generate a textual solution. Reasoning with text is naturally challenging and this is particularly so with text reuse. However significant inroads towards addressing this challenge was made possible with knowledge of problem-solution alignment. This knowledge allows us to identify specific parts of a textual solution that are linked to particular problem attributes or attribute values. Accordingly, a text reuse strategy based on implicit alignment is presented to determine textual solution constructs (words or phrases) that needs adapted. This addresses the question of what to reuse in solution texts and thereby forms the first contribution of this thesis. A generic architecture, the Case Retrieval Reuse Net (CR2N), is used to formalise the reuse strategy. Functionally, this architecture annotates textual constructs in a solution as reusable with adaptation or without adaptation. Key to this annotation is the discovery of reuse evidence mined from neighbourhood characteristics. Experimental results show significant improvements over a retrieve-only system and a baseline reuse technique. We also extended CR2N so that retrieval of similar cases is informed by solutions that are easiest to adapt. This is done by retrieving the top k cases based on their problem similarity and then determining the reusability of their solutions with respect to the target problem. Results from experiments show that reuse-guided retrieval outperforms retrieval without this guidance. Although CR2N exploits implicit alignment to aid text reuse, performance can be greatly improved if there is explicit alignment. Our second contribution is a method to form explicit alignment of structured problem attributes and values to sentences in a textual solution. Thereafter, compositional and transformational approaches to text reuse are introduced to address the question of how to reuse textual solutions. The main idea in the compositional approach is to generate a textual solution by using prototypical sentences across similar authors. While the transformation approach adapts the retrieved solution text by replacing sentences aligned to mismatched problem attributes using sentences from the neighbourhood. Experiments confirm the usefulness of these approaches through strong similarity between generated text and human references. The third and final contribution of this research is the use of Machine Translation (MT) evaluation metrics for TCBR. These metrics have been shown to correlate highly with human expert evaluation. In MT research, multiple human references are typically used as opposed to a single reference or solution per test case. An introspective approach to create multiple references for evaluation is presented. This is particularly useful for CBR domains where single reference cases (or cases with a single solution per problem) typically form the casebase. For such domains we show how multiple references can be generated by exploiting the CBR similarity assumption. Results indicate that TCBR systems evaluated with these MT metrics are closer to human judgements