Abstract. Annotations of multimedia documents typically have been pursued in two different directions. Either previous approaches have focused on low level descriptors, such as dominant color, or they have focused on the content dimension and corresponding annotations, such as person or vehicle. In this paper, we present a software environment to bridge between the two directions. M-OntoMat-Annotizer allows for linking low level MPEG-7 visual descriptions to conventional Semantic Web ontologies and annotations. We use M-OntoMat-Annotizer in order to construct ontologies that include prototypical instances of high-level domain concepts together with a formal specification of corresponding visual descriptors. Thus, we formalize the interrelationship of high- and low-level multimedia concept descriptions allowing for new kinds of multimedia content analysis and reasoning. 1

The emergent Semantic Web community needs a common infrastructure for testing the scalability and quality of new techniques and software which use machine processable data. Since ontologies are a centerpiece of most approaches , we believe that for an accurate evaluation of tools for quality, scalability and performance, the research community needs a freely available ontology with a large description base. If the use of tools is to be for advanced semantic applications, such as those in business intelligence and national security, then instances in the knowledge base should be highly interconnected. Thus, we propose and describe a Semantic WEb Technology evaluation Ontology (SWETO) test-bed. In particular, we address the requirements of a test-bed to support research in semantic analytics , as well as the steps in its development, including, ontology creation, semi-automatic data extraction, and entity disambiguation.

Abstract. In this paper we discuss the use of knowledge for the automatic extraction of semantic metadata from multimedia content. For the representation of knowledge we extended and enriched current general-purpose ontologies to include low-level visual features. More specifically, we implemented a tool that links MPEG-7 visual descriptors to high-level, domain-specific concepts. For the exploitation of this knowledge infrastructure we developed an experimentation platform, that allows us to analyze multimedia content and automatically create the associated semantic metadata, as well as to test, validate and refine the ontologies built. We pursued a tight and functional integration of the knowledge base and the analysis modules putting them in a loop of constant interaction instead of being the one just a pre- or post-processing step of the other. 1

Abstract. The focus of contemporary Web information retrieval systems has been to provide efficient support for the querying and retrieval of relevant documents. More recently, information retrieval over semantic metadata extracted from the Web has received an increasing amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this metadata is an interesting and challenging research topic. Just as ranking of documents is a critical component of today’s search engines, the ranking of complex relationships will be an important component in tomorrow’s Semantic Web analytics engines. Building upon our recent work on specifying and discovering complex relationships in RDF data, called Semantic Associations, we present a flexible ranking approach which can be used to identify more interesting and relevant relationships in the Semantic Web. Additionally, we demonstrate our ranking scheme’s effectiveness through an empirical evaluation over a real-world dataset. Keywords. H.3.3.d Metadata, H.3.5.f XML/XSL/RDF 1

Abstract. Annotations of multimedia documents typically have been pursued in two different directions. Either previous approaches have focused on low level descriptors, such as dominant color, or they have focused on the content dimension and corresponding annotations, such as person or vehicle. In this paper, we present a software environment to bridge between the two directions. M-OntoMat-Annotizer allows for linking low level MPEG-7 visual descriptions to conventional Semantic Web ontologies and annotations. We use M-OntoMat-Annotizer in order to construct ontologies that include prototypical instances of high-level domain concepts together with a formal specification of corresponding visual descriptors. Thus, we formalize the interrelationship of high- and low-level multimedia concept descriptions allowing for new kinds of multimedia content analysis, reasoning and retrieval. 1

Abstract. Knowledge representation and annotation of multimedia documents typically have been pursued in two different directions. Previous approaches have focused either on low level descriptors, such as dominant color, or on the seman-tic content dimension and corresponding manual annotations, such as person or vehicle. In this paper, we present a knowledge infrastructure and a experimen-tation platform for semantic annotation to bridge the two directions. Ontologies are being extended and enriched to include low-level audiovisual features and de-scriptors. Additionally, we present a tool that allows for linking low-level MPEG-7 visual descriptions to ontologies and annotations. This way we construct on-tologies that include prototypical instances of high-level domain concepts to-gether with a formal specification of the corresponding visual descriptors. This infrastructure is exploited by a knowledge-assisted analysis framework that may handle problems like segmentation, tracking, feature extraction and matching in order to classify scenes, identify and label objects, thus automatically create the associated semantic metadata. 1

Abstract: ConTag is an approach to generate semantic tag recommendations for documents based on Semantic Web ontologies and Web 2.0 services. We designed and implemented a process to normalize documents to RDF format, extract document topics using Web 2.0 services and finally match extracted topics to a Semantic Web ontology. Due to ConTag we are able to show that the information provided by Web 2.0 services in combination with a Semantic Web ontology enables the generation of relevant semantic tag recommendations for documents. The main contribution of this work is a semantic tag recommendation process based on a choreography of Web 2.0 services.

Wikis are increasingly being used in world-wide, intranet and even in personal settings. Unfortunately, current wikis are data islands: people can read and edit them, but machines can only send around text strings without structure. Wiki migration, publishing from one wiki to another one and free choice of syntax hold back broader wiki usage.

Abstract. This paper presents a tool for supporting sharing and reuse of knowledge in document creation (writing) and use (reading). Semantic Web technologies are used to support the production of ontology based annotations while the document is written. Free text annotations (comments) can be added to integrate the knowledge in the document. In addition the tool uses external services (e.g. a Semantic Web harvester) to propose relevant content to writing user, enabling easy knowledge reuse. Similar facilities are provided for readers when their task does not coincide with the author’s one. The tool is specifically designed for Knowledge Management in organisations. In this paper we present and discuss how Semantic Web technologies are designed and integrated in the system. 1

The Semantic Web was invented by Tim Berners-Lee in 1998 as a web of data for machine consumption. Its applicability in supporting real world applications on the World Wide Web, however, remains unclear to this day because most existing works treat the Semantic Web as one universal RDF graph and ignore the Web aspect. In fact, the Semantic Web is distributed on the Web as a web of belief: each piece of Semantic Web data is independently published on the Web as a certain agent’s belief instead of the universal truth. Therefore, we enhance the current conceptual model of the Semantic Web to characterize both the content and the context of Semantic Web data. A significant sample dataset is harvested to demonstrate the non-trivial presence and the global properties of the Semantic Web on the Web. Based on the enhanced conceptual model, we introduce a novel search and navigation model for the unique behaviors in Web-scale Semantic Web data access, and develop an enabling tool – the Swoogle Semantic Web search engine. To evaluate the data quality of Semantic Web data, we also (i) develop an explainable ranking schema that orders the popularity of Semantic Web documents and terms, and (ii) introduce a new level of granularity of Semantic Web data – RDF molecule that supports lossless RDF graph decomposition and effective provenance tracking. This dissertation systematically investigates the Web aspect of the Semantic Web. Its primary contribu-tions are the enhanced conceptual model of the Semantic Web, the novel Semantic Web search and navigation model, and the Swoogle Semantic Web search engine.