Networked Knowledge - Networked Media

The book title Networked Knowledge - Networked Media reflects on the convergence of Social Media and the Semantic Web. When these developments became popular a few years ago it was a simple co-existence between the two, but in the meantime they have increasingly melted making it impossible to think of knowledge technologies without thinking of the Semantic Web.

This paper discusses RDF related work in the context of OpenLink Virtuoso, a general purpose relational / federated database and applications platform. The use cases are dual 1. large RDF repositories 2. making arbitrary relational data queriable with SPARQL and RDF by mapping on demand. We discuss adapting a relational engine for native RDF support with dedicated data types, bitmap indexing and SQL optimizer techniques. We discuss adaptations of the query engine for running on shared nothing clusters, providing virtually unbounded scalability for RDF or relational warehouses. We further discuss mapping existing relational data into RDF for SPARQL access without converting the data into physical triples. We present conclusions and metrics as well as a number of use cases, from DBpedia to bio informatics and collaborative web applications.

Despite the growing importance of knowledge work in todays organizations, its support by means of ICT tools is still rather limited. Recent trends in semantic technologies provide novel approaches for an effective solution to these challenges in terms of semantic-based task management. However, task management involves the complex interplay of information and work activities. Thus a semantic task management framework is needed which supports an adaptable semantic foundation, to meet the challenges of knowledge work, via a set of task services on the desktop. To this end, we propose the Nepomuk Semantic Task Management Framework (STMF) as platform for a task-oriented ecosystem for desktop applications.

Effective information integration is still one of today’s emerging research goals. The explosive growth of heterogeneous information sources makes the task harder and more challenging. Although ontologies promise an effective solution towards information management and coordination, it would be a surprise if two independent parties have constructed the same ontology to manage information for the same domain. Hence, to integrate information effectively, ontology mapping methods are invaluable. This paper presents the AUTOMS-F framework, which aims to facilitate the development of synthesized methods for the efficient and effective automatic mapping of ontologies. AUTOMS-F is highly extendable and customizable, providing facilities for supporting the rapid prototyping of synthesized mapping methods, adapting some well established programming design patterns. The paper presents the AUTOMS mapping method as an evaluated case of AUTOMS-F’s potential.

In this paper, we introduce the OntoWiki Application Framework for developing Semantic Web applications with a strong emphasis on collaboration. After presenting OntoWiki as our main show case for the framework, we give both an architectural overview and a detailed view on the included components.We conclude this paper with a presentation of different use cases where the framework was strongly involved.

Effective learning support in organizations requires a flexible and personalized toolset that brings together the individual and the organizational perspective on learning. Such toolsets need a service-oriented infrastructure of reusable knowledge and learning services as an enabler. This contribution focuses on conceptual foundations for such an infrastructure as it is being developed within the MATURE IP and builds on the knowledge maturing process model on the one hand, and the seeding-evolutionary growth-reseeding model on the other hand. These theories are used to derive maturing services, for which initial examples are presented.

While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem we investigate how to improve retrieval performance in a setting where resources are sparsely annotated with semantic information. We suggest employing techniques from associative information retrieval to find relevant material, which was not originally annotated with the concepts used in a query. We present an associative retrieval service for the Semantic Desktop and evaluate if the use of associative retrieval techniques increases retrieval performance.

Evaluation of new retrieval paradigms, as retrieval in the Semantic Web or on the Semantic Desktop, presents an additional challenge as no off-the-shelf test corpora for evaluation exist. Hence we give a detailed description of the approach taken to the evaluation of the information retrieval service we have built for the Semantic Desktop.

Existing information, knowledge and content infrastructures are currently facing challenging problems in terms of scalability, management and integration of various content and services. The latest technology trends, including SemanticWeb Services, Grid computing and Intelligent Content Objects provide the technological means to address parts of the previously mentioned problems. A combination of the three technologies could provide a sound technological foundation to build scalable infrastructures that provide highly automated support in fulfilling user’s goals.

This paper introduces GRISINO, an integrated infrastructure for Semantic Web Services, Intelligent Content Objects and Grid computing, which may serve as a foundation for next generation distributed applications.

Semantic Wikis have been introduced for collaborative authoring of ontologies as well as for annotating textual and multimedia wiki content with semantic metadata. In this paper, we introduce a different approach for a Semantic Wiki based on an ontology metamodel that has been especially customized for the deployment within a wiki. For optimal usability client-side technologies for graphical user interface have been combined with a simple and intuitive semantic query language. Single fragments of a wiki page can be annotated in an interactive and rather intuitive way to minimize the additional effort that is necessary for adding semantic annotation. Thus, the productivity and efficiency of a Semantic Wiki system will open up for non expert users as well, which is important for fostering the popularity of Semantic Wiki systems.

Wikis as well as collaborative tagging have been subject of very intense research and an active discussion topic in the so-called blogsphere. In this paper, we propose Collaborative Wiki Tagging based on the idea to exploit inherent semantics of the concept of link in a wiki. The low-level integration of wiki and collaborative tagging of web resources is expected to be effective in enterprise environments particularly in the personal and group knowledge management application area. We first introduce a conceptualization of Collaborative Wiki Tagging. Then, we propose a simple scheme for using one of the existing native wiki syntax to represent tagging data. Collaborative Wiki Tagging Portal Prototype, developed as a proof of concept, is used to give illustrative practical examples of the proposed approach and illustration of the user interface.

The Palette project dedicated to learning in Communities of Practice (CoPs) aims to offer several services for CoPs, in particular Knowledge Management (KM) services based on an ontology dedicated to CoPs, the so-called O’CoP. Built from information sources about the Palette CoPs, O’CoP aims both at modelling the members of the CoP and at annotating the CoP’s knowledge resources. The paper describes the structure of O’CoP, its main concepts and relations, and it reports some lessons learnt from the cooperative building of this ontology.

Several data quality management (DQM) tasks like duplicate detection or consistency checking depend on domain specific knowledge. Many DQM approaches have potential for bringing together domain knowledge and DQM metadata. We provide an approach which uses this knowledge modeled in ontologies instead of aquiring that knowledge by cost-intensive interviews with domain-experts. These ontologies can directly be annotated with DQM specific metadata. With our approach a synergy effect can be achieved when modeling a domain ontology, e.g. for defining a shared vocabulary for improved interoperability, and performing DQM. We present five DQM applications which directly use knowledge provided by domain ontologies. These applications use the ontology structure itself to provide correction suggestions for invalid data, identify duplicates, and to store data quality annotations at schema and instance level.

In the economic and financial analysis domain, quick access to the right information plays a major role. Using current systems, the search for and presentation of data is very cumbersome. The data, mostly in form of time-series, is stored in various databases. In order to retrieve the searched data, the analysts need to know where to search and sometimes even the structure of the database and its coding. Then it is required to export the data, process the data and create a chart to view the data. This might take time from tens of minutes to hours. In our work we present a first prototype of an integrated search engine that takes as input a natural language query and offers graphic and text output depending on the user task. The system automatically identifies the resulting time-series and types of graphical data presentation, and shows the results in a web browser or in Excel. The knowledge based expert system uses domain ontologies for extraction of economic terms in the search queries and specially built data type taxonomies with user task and chart type ontologies for the identification of graphical output.

Analysts are often interested in how sentiment towards an organization, a product or a particular technology changes over time. Popular methods that process unstructured textual material to automatically detect sentiment based on tagged dictionaries are not capable of fulfilling this task, even when coupled with part-of speech tagging, a standard component of most text processing toolkits that distinguishes grammatical categories such as article, noun, verb, and adverb. Small corpus size, ambiguity and subtle incremental change of tonal expressions between different versions of a document complicate sentiment detection. Parsing grammatical structures, by contrast, outperforms dictionary-based approaches in terms of reliability, but usually suffers from poor scalability due to its computational complexity. This work provides an over view of different dictionary- and machine-learning-based sentiment detection methods and evaluates them on several Web corpora.After identifying the shortcomings of these methods, the paper proposes an approach based on automatically building Tagged Linguistic Unit (TLU) databases to overcome the restrictions of dictionaries with a limited set of tagged tokens.

Corporate Semantic Web describes the application of semantic technologies within enterprises for better knowledge management or enhanced IT service management. But, well-known cost- and process-oriented problems of ontology engineering hinder the employment of ontologies as a flexible, scalable, and cost effective means for integrating data in small and mid-sized enterprises.We propose an innovative ontology lifecycle, examine existing tools towards the functional requirements of the lifecycle phases, and propose the vision of an architecture supporting them integratively.

Policy rules are often written in organizations by a team of people in different roles and technical backgrounds. While user-generated content and community-driven ontologies become common practices in the semantic environments, machine-processable user-generated policies have been underexplored, and tool support for such policy acquisition is practically non-existent. We defined the concept and developed a tool for policy acquisition from the end users, grounded on Semantic Web technologies.We describe a policy management environment (PME) for the Semantic Web and show its added value compared to existing policy-related developments. In particular, we detail a part of the PME, the policy acquisition tool that enables non-expert users to create and modify semantic policy rules. An empirical study has been conducted with 10 users, who were new to the semantic policy acquisition concept and the developed tool. The main task for the users was to model policies of two different scenarios using previously unknown to them. Overall, the users successfully modeled policies employing the tool, with minor deviations between their performance and feedback. Observation-based, quantitative and qualitative feedback on the concept and the implementation of the end-user policy acquisition tool is presented.

A key factor for success of companies operating in a globalized market environment is a modern SOA-based infrastructure. An essential component of a SOA infrastructure is the central service registry. Current standards for organizing service registries and their implementations are driven by the technical aspects of the infrastructure. When using such technically organized service registries, business users often fail to find the needed information. With the concepts of Web 2.0 in mind, we present a new approach to the organization and implementation of the business registries that are driven by the needs of business users. The paper discusses the problems of the current technically driven approaches, presents an architecture for a business user-driven service registry and introduces an implementation of the architecture using UDDI and Semantic MediaWiki.

Realising the vision of pervasive health care will generate new challenges for knowledge management and data integration. Such challenges are fundamentally different from issues and problems that we face in centralised approaches as well as non-clinical scenarios. In this paper, we reflect upon our experiences in the Miakt project wherein a prototype system was developed to support data integration and decision making in the breast cancer domain. While the decision making needs to rely on different clinical expertise, the Miakt system leveraged a system ontology to glue together distributed services. Situating the Miakt system in a highly pervasive environment reveals the inefficiency of global vocabularies via domain ontologies and the inappropriateness of “static” system ontologies with assigned system configuration instances. We examine the capability of a process calculus based language, Lightweight Coordination Calculus (LCC), in meeting knowledge management challenges in pervasive health care. The key difference in approach lies in making the representational abstraction reflect the relative autonomy of the various clinical specialisms (eg., mammography or histopathology) involved in contributing to patient management. The bringing together of diverse forms of information necessary for the collective medical assessment is managed by tracking the message passing protocols undertaken by medical personnel. The scope within LCC of accommodating boolean-valued constraints allows for flexible integration of heterogeneous sources in multiple formats, which are characteristic features of a pervasive healthcare environment.

Interactive digital TV is becoming a reality throughout the globe. The most essential aspect of TV broadcasting is enhancing the interaction experience for the viewer. To this end, we explore the potential of introducing semantics in the distribution, processing and usage of the media content. We propose a smart iTV receiver framework capable of collecting, extending and processing semantic metadata related to the broadcast multimedia content. A system architecture is presented along with examples of services to illustrate the combination of semantic metadata content, user preferences and external data sources.

The game industry has long been neglected as a market and research area for knowledge management and semantic technologies. However, as the budgets for game projects are growing and game development is subject to an increasing professionalization and specialization coupled with outsourcing and offshoring, new needs and potentials for continuous knowledge management and the use of semantic technologies emerge. This chapter starts with a description of the current situation and examines typical game development activities and involved parties that could benefit from a continuous knowledge management support. Subsequently, it provides a general framework architecture and implementation examples that show how knowledge management and semantic technologies can be employed to support game development.