ODEWiki: A Semantic Wiki That Interoperates with the ODESeW Semantic Portal

We present ODEWiki, a technology for the development of Semantic Wikis, which has a combined set of added-value features over other existing semantic wikis in the state of the art. Namely, ODEWiki interoperates with an existing semantic portal technology (ODESeW), it manages inconsistencies raised because of the distributed nature of knowledge base development and maintenance, it uses RDFa for the annotation of the resulting wiki pages, it follows a WYSIWYG approach, and it allows decoupling wiki pages and ontology instances, that is, a wiki page may contain one or several ontology instances. Although some of these features appear in some of the state-of-the-art semantic wikis, but they are not combined together in a single solution

The SemSearchXplorer - exploring semantic search results with semantic visualizations

SemSearchXplorer is a toolkit for the exploration of semantic data. The goal is to lower user barriers to access information in semantic data repositories. Therefore SemSearchXplorer supports the user in three respects: (1) it supports querying of the semantic data with a keyword based approach, so the users do not need to learn a semantic query language, (2) it helps users find relevant results both by using semantic enriched information about the results and semantic filter options to narrow down the set of results, and (3) it provides information exploration capabilities through semantic visualizations recommended by the system. Filtering of semantic search results helps to narrow down the result set to a more manageable amount of information. Besides searching for relevant information, facilities for the exploration of the results help users to gain insight in the context of results. With several semantic visualizations, we try to help users making sense of the raw data. Based on the assumption that there is no single visualization that fits all exploration needs, SemSearchXplorer recommends visualizations based on the selected information of users

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

PowerAqua: Open Question Answering on the Semantic Web

With the rapid growth of semantic information in the Web, the processes of searching and querying these very large amounts of heterogeneous content have become increasingly challenging. This research tackles the problem of supporting users in querying and exploring information across multiple and heterogeneous Semantic Web (SW) sources. A review of literature on ontology-based Question Answering reveals the limitations of existing technology. Our approach is based on providing a natural language Question Answering interface for the SW, PowerAqua. The realization of PowerAqua represents a considerable advance with respect to other systems, which restrict their scope to an ontology-specific or homogeneous fraction of the publicly available SW content. To our knowledge, PowerAqua is the only system that is able to take advantage of the semantic data available on the Web to interpret and answer user queries posed in natural language. In particular, PowerAqua is uniquely able to answer queries by combining and aggregating information, which can be distributed across heterogeneous semantic resources. Here, we provide a complete overview of our work on PowerAqua, including: the research challenges it addresses; its architecture; the techniques we have realised to map queries to semantic data, to integrate partial answers drawn from different semantic resources and to rank alternative answers; and the evaluation studies we have performed, to assess the performance of PowerAqua. We believe our experiences can be extrapolated to a variety of end-user applications that wish to open up to large scale and heterogeneous structured datasets, to be able to exploit effectively what possibly is the greatest wealth of data in the history of Artificial Intelligence

Semantic Selection of Internet Sources through SWRL Enabled OWL Ontologies

This research examines the problem of Information Overload (IO) and give an overview of various attempts to resolve it. Furthermore, argue that instead of fighting IO, it is advisable to start learning how to live with it. It is unlikely that in modern information age, where users are producer and consumer of information, the amount of data and information generated would decrease. Furthermore, when managing IO, users are confined to the algorithms and policies of commercial Search Engines and Recommender Systems (RSs), which create results that also add to IO. this research calls to initiate a change in thinking: this by giving greater power to users when addressing the relevance and accuracy of internet searches, which helps in IO. However powerful search engines are, they do not process enough semantics in the moment when search queries are formulated. This research proposes a semantic selection of internet sources, through SWRL enabled OWL ontologies. the research focuses on SWT and its Stack because they (a)secure the semantic interpretation of the environments where internet searches take place and (b) guarantee reasoning that results in the selection of suitable internet sources in a particular moment of internet searches. Therefore, it is important to model the behaviour of users through OWL concepts and reason upon them in order to address IO when searching the internet. Thus, user behaviour is itemized through user preferences, perceptions and expectations from internet searches. The proposed approach in this research is a Software Engineering (SE) solution which provides computations based on the semantics of the environment stored in the ontological model

Examining the Application of Modular and Contextualised Ontology in Query Expansions for Information Retrieval

This research considers the ongoing challenge of semantics-based search from the perspective of how to exploit Semantic Web languages for search in the current Web environment. The purpose of the PhD was to use ontology-based query expansion (OQE) to improve search effectiveness by increasing search precision, i.e. retrieving relevant documents in the topmost ranked positions in a returned document list. Query experiments have required a novel search tool that can combine Semantic Web technologies in an otherwise traditional IR process using a Web document collection

Semantic and Visual Analysis of Metadata to Search and Select Heterogeneous Information Resources

An increasing number of activities in several disciplinary and industrial fields such as the scientific research, the industrial design and the environmental management, rely on the production and employment of informative resources representing objects, information and knowledge. The vast availability of digitalized information resources (documents, images, maps, videos, 3D model) highlights the need for appropriate methods to effectively share and employ all these resources. In particular, tools to search and select information resources produced by third parties are required to successfully achieve our daily work activities. Headway in this direction is made adopting the metadata, a description of the most relevant features characterizing the information resources. However, a plenty of features have to be considered to fully describe the information resources in sophisticated fields as those mentioned. This brings to a complexity of metadata and to a growing need for tools which face with this complexity. The thesis aims at developing methods to analyze metadata easing the search and comparison of information resources. The goal is to select the resources which best fit the user\u27s needs in specific activities. In particular, the thesis faces with the problem of metadata complexity and supports in the discovery of selection criteria which are unknown to the user. The metadata analysis consists of two approaches: visual and semantic analysis. The visual analysis involves the user as much as possible to let him discover the most suitable selection criteria. The semantic analysis supports in the browsing and selection of information resources taking into account the user\u27s knowledge which is properly formalized

Forschungsbericht Universität Mannheim 2006 / 2007

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