Abstract. The continued increase in Web usage, in particular participation in folksonomies, reveals a trend towards a more dynamic and interactive Web where individuals can organise and share resources. Tagging has emerged as the de-facto standard for the organisation of such resources, providing a versatile and reactive knowledge management mechanism that users find easy to use and understand. It is common nowadays for users to have multiple profiles in various folksonomies, thus distributing their tagging activities. In this paper, we present a method for the automatic consolidation of user profiles across two popular social networking sites, and subsequent semantic modelling of their interests utilising Wikipedia as a multi-domain model. We evaluate how much can be learned from such sites, and in which domains the knowledge acquired is focussed. Results show that far richer interest profiles can be generated for users when multiple tag-clouds are combined. 1

We introduce a formal model for capturing the notion of mashup in its globality. The basic component in our model is the mashlet. A mashlet may query data sources, import other mashlets, use external Web services, and specify complex interaction patterns between its components. A mashlet state is modeled by a set of relations and its logic specified by datalog-style active rules. We are primarily concerned with changes in a mashlet state relations and rules. The interactions with users and other applications, as well as the consequent effects on the mashlets composition and behavior, are captured by streams of changes. The model facilitates dynamic mashlets composition, interaction and reuse, and captures the fundamental behavioral aspects of mashups. Categories and Subject Descriptors

Abstract. This article presents the CHIP demonstrator 5 for providing personalized access to digital museum collections. It consists of three main components: Art Recommender, Tour Wizard, and Mobile Tour Guide. Based on the semantically-enriched Rijksmuseum Amsterdam 6 collection, we show how Semantic Web technologies can be deployed to (partially) solve three important challenges for recommender systems applied in an open Web context: (1) to deal with the complexity of various types of relationships for recommendation inferencing, where we take a content-based approach to recommend both artworks and art-history topics; (2) to cope with the typical user modeling problems, such as cold-start for first-time users, sparsity in terms of user ratings, and the efficiency of user feedback collection; and (3) to support the presentation of recommendations by combining different views like a historical timeline, museum map and faceted browser. Following a user-centered design cycle, we have performed two evaluations with users to test the effectiveness of the recommendation strategy and to compare the different ways for building an optimal user profile for efficient recommendations. The CHIP demonstrator received the Semantic Web Challenge Award (third prize) in 2007, Busan, Korea. Key words: CHIP, semantics-driven recommendations, content-based recommendations, enriched collections, cultural heritage vocabularies, interactive user modeling dialog, museum tours, mobile museum guide 1

Collaborative, social tagging and annotation systems have exploded on the Internet as part of the Web 2.0 phenomenon. Systems such as Flickr, Del.icio.us, Technorati, Connotea and LibraryThing, provide a community-driven approach to classifying information and resources on the Web, so that they can be browsed, discovered and re-used. Although social tagging sites provide simple, user-relevant tags, there are issues associated with the quality of the metadata and the scalability compared with conventional indexing systems. In this paper we propose a hybrid approach that enables authoritative metadata generated by traditional cataloguing methods to be merged with community annotations and tags. The HarvANA (Harvesting and Aggregating Networked Annotations) system uses a standardized but extensible RDF model for representing the annotations/tags and OAI-PMH to harvest the annotations/tags from distributed community servers. The harvested annotations are aggregated with the authoritative metadata in a centralized metadata store. This streamlined, interoperable, scalable approach enables libraries, archives and repositories to leverage community enthusiasm for tagging and annotation, augment their metadata and enhance their discovery services. This paper describes the HarvANA system and its evaluation through a collaborative testbed with the National Library of Australia using architectural images from PictureAustralia.

Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations: tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web. 1

Abstract. Tagging has become increasingly popular and useful across various social networks and applications. It allows users to classify and organize resources for improving the retrieval performance over those tagged resources. Within social networks, tags can also facilitate the interaction between members of the community, e.g. because similar tags may represent similar interests. Although obviously useful for straightforward retrieval tasks, the current meta-data model underlying typical tagging systems does not fully exploit the potential of the social process of finding, establishing, challenging, and promoting symbols, i.e. tags. For instance, the social process is not used for establishing an explicit hierarchy of tags or for the collective detection of equivalencies, synonyms, morphological variants, and other useful relationships across tags. This limitation is due to the constraints of the typical meta-model of tagging, in which the subject must be a Web resource, the relationship type is always hasTag, and the object must be a tag as a literal. In this paper, we propose a simple yet effective extension for the current meta-model of tagging systems in order to exploit the potential of collective tagging for the emergence of richer semantic structures, in particular for capturing semantic relationships between tags. Our approach expands the range of the object of tagging from Web resources only to the union of (1) Web resources and (2) pairs of tags, i.e., users can now use arbitrary tags for expressing typed relationships between a pair of tags. This allows the user community to establish similarity relations and other types of relationships between tags. We present a first prototype and the results from an evaluation in a small controlled setting.

The“wisdom of crowds”is accomplishing tasks that are cumbersome for individuals yet cannot be fully automated by means of specialized computer algorithms. One such task is the construction of thesauri and other types of concept hierarchies. Human expert feedback on the relatedness and relative generality of terms, however, can be aggregated to dynamically construct evolving concept hierarchies. The InPhO (Indiana Philosophy Ontology) project bootstraps feedback from volunteer users unskilled in ontology design into a precise representation of a specific domain. The approach combines statistical text processing methods with expert feedback and logic programming to create a dynamic semantic representation of the discipline of philosophy. In this paper, we show that results of comparable quality can be achieved by leveraging the workforce of crowdsourcing services such as the Amazon Mechanical Turk (AMT). In an extensive empirical study, we compare the feedback obtained from AMT’s workers with that from the InPhO volunteer users providing an insight into qualitative differences of the two groups. Furthermore, we present a set of strategies for assessing the quality of different users when gold standards are missing. We finally use these methods to construct a concept hierarchy based on the feedback acquired from AMT workers.

Information sharing can be effective with structured data. The Semantic Web is mainly aimed at structuring information by creating widely accepted ontologies. However, users have different preferences and evolving requirements. It is not practical to attempt perfect schema definitions with strict constraints. Creating structured formats should be a collaborative and evolutionary process. Social software motivates wide participation by providing easy interface. We propose a system called StYLiD for sharing a wide variety of structured information. Users freely define their own structured concepts. The system consolidates different versions defined by different users. The attributes of the different concept versions are aligned semi-automatically into a single unified view. Popular concepts gradually emerge from the concept cloud and stabilize. Concept definitions are flexible. An attribute value can take a literal or a resource URI and the suggestive range does not constrain the contributors. StYLiD generates unique dereferenceable URIs so that data items can form a linked data web. Structured data is embedded in machine readable form using RDFa. Search and browsing features are provided to utilize the structured data and consolidated concepts.

In this paper we introduce SemSor, a system developed especially for the analysis of emergency situations. It constantly collects information from sources of the Social Web, maps it to unique resources in the Semantic Web and uses the annotated information as basis for the situation analysis. If an emergency situation needs to get analyzed, four steps are required: First, all information that is already known about this situation must be entered in the SemSor-GUI. Second, the entered information needs to be mapped to resources in the Semantic Web. Third, using these resources as starting nodes, a spreading activation is applied along the relationships within the Semantic Web to find relevant Social Web information. And fourth, the newly identified information is visualized according to different dimensions and can be filtered and explored by the user. In an iterative process, new insights can be used to refine the query and thus improve the activated information until a comprehensive analysis of even complex situations is possible.

Abstract. In recent years social bookmarking systems (tagging systems) became one of the highly popular applications on the Internet. The main idea of social bookmarking is to organize content in a loose fashion by allowing users to completely freely annotate content. This work presents a way of combining the information retrieval (IR), Semantic Web and social web approaches of searching the Web by including general topic categories as a part of tagging systems. In this way semantic and social web are presented in a unified framework of search and indexing content. The work also shows a way of ontology learning by creating a hierarchical network of tag associations. This network is created using association rules discovery. In order to enhance these networks, IR search engine results are used to evaluate relevance of resources to a given topic. Networks of association, created by application of a modified Apriori algorithm, are evaluated with topic networks from the Open Directory Project (www.dmoz.org). 1