Enhancing information retrieval in folksonomies using ontology of place constructed from Gazetteer information

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesFolksonomy (from folk and taxonomy) is an approach to user metadata creation where users describe information objects with a free-form list of keywords (‘tags’). Folksonomy has have proved to be a useful information retrieval tool that support the emergence of “collective intelligence” or “bottom-up” light weight semantics. Since there are no guiding rules or restrictions on the users, folksonomy has some drawbacks and problems as lack of hierarchy, synonym control, and semantic precision. This research aims at enhancing information retrieval in folksonomy, particularly that of location information, by establishing explicit relationships between place name tags. To accomplish this, an automated approach is developed. The approach starts by retrieving tags from Flickr. The tags are then filtered to identify those that represent place names. Next, the gazetteer service that is a knowledge organization system for spatial information is used to query for the place names. The result of the search from the gazetteer and the feature types are used to construct an ontology of place. The ontology of place is formalized from place name concepts, where each place has a “Part-Of” relationship with its direct parent. The ontology is then formalized in OWL (Web Ontology Language). A search tool prototype is developed that extracts a place name and its parent name from the ontology and use them for searching in Flickr. The semantic richness added to Flickr search engine using our approach is tested and the results are evaluated

Social and Semantic Contexts in Tourist Mobile Applications

The ongoing growth of the World Wide Web along with the increase possibility of access information through a variety of devices in mobility, has defi nitely changed the way users acquire, create, and personalize information, pushing innovative strategies for annotating and organizing it. In this scenario, Social Annotation Systems have quickly gained a huge popularity, introducing millions of metadata on di fferent Web resources following a bottom-up approach, generating free and democratic mechanisms of classi cation, namely folksonomies. Moving away from hierarchical classi cation schemas, folksonomies represent also a meaningful mean for identifying similarities among users, resources and tags. At any rate, they suff er from several limitations, such as the lack of specialized tools devoted to manage, modify, customize and visualize them as well as the lack of an explicit semantic, making di fficult for users to bene fit from them eff ectively. Despite appealing promises of Semantic Web technologies, which were intended to explicitly formalize the knowledge within a particular domain in a top-down manner, in order to perform intelligent integration and reasoning on it, they are still far from reach their objectives, due to di fficulties in knowledge acquisition and annotation bottleneck. The main contribution of this dissertation consists in modeling a novel conceptual framework that exploits both social and semantic contextual dimensions, focusing on the domain of tourism and cultural heritage. The primary aim of our assessment is to evaluate the overall user satisfaction and the perceived quality in use thanks to two concrete case studies. Firstly, we concentrate our attention on contextual information and navigation, and on authoring tool; secondly, we provide a semantic mapping of tags of the system folksonomy, contrasted and compared to the expert users' classi cation, allowing a bridge between social and semantic knowledge according to its constantly mutual growth. The performed user evaluations analyses results are promising, reporting a high level of agreement on the perceived quality in use of both the applications and of the speci c analyzed features, demonstrating that a social-semantic contextual model improves the general users' satisfactio

Semantic technologies: from niche to the mainstream of Web 3? A comprehensive framework for web Information modelling and semantic annotation

Context: Web information technologies developed and applied in the last decade have considerably changed the way web applications operate and have revolutionised information management and knowledge discovery. Social technologies, user-generated classification schemes and formal semantics have a far-reaching sphere of influence. They promote collective intelligence, support interoperability, enhance sustainability and instigate innovation. Contribution: The research carried out and consequent publications follow the various paradigms of semantic technologies, assess each approach, evaluate its efficiency, identify the challenges involved and propose a comprehensive framework for web information modelling and semantic annotation, which is the thesis’ original contribution to knowledge. The proposed framework assists web information modelling, facilitates semantic annotation and information retrieval, enables system interoperability and enhances information quality. Implications: Semantic technologies coupled with social media and end-user involvement can instigate innovative influence with wide organisational implications that can benefit a considerable range of industries. The scalable and sustainable business models of social computing and the collective intelligence of organisational social media can be resourcefully paired with internal research and knowledge from interoperable information repositories, back-end databases and legacy systems. Semantified information assets can free human resources so that they can be used to better serve business development, support innovation and increase productivity

Knowledge Base Enrichment by Relation Learning from Social Tagging Data

There has been considerable interest in transforming unstructured social tagging data into structured knowledge for semantic-based retrieval and recommendation. Research in this line mostly exploits data co-occurrence and often overlooks the complex and ambiguous meanings of tags. Furthermore, there have been few comprehensive evaluation studies regarding the quality of the discovered knowledge. We propose a supervised learning method to discover subsumption relations from tags. The key to this method is quantifying the probabilistic association among tags to better characterise their relations. We further develop an algorithm to organise tags into hierarchies based on the learned relations. Experiments were conducted using a large, publicly available dataset, Bibsonomy, and three popular, human-engineered or data-driven knowledge bases: DBpedia, Microsoft Concept Graph, and ACM Computing Classification System. We performed a comprehensive evaluation using different strategies: relation-level, ontology-level, and knowledge base enrichment based evaluation. The results clearly show that the proposed method can extract knowledge of better quality than the existing methods against the gold standard knowledge bases. The proposed approach can also enrich knowledge bases with new subsumption relations, having the potential to significantly reduce time and human effort for knowledge base maintenance and ontology evolution

Reusing Ontologies to Enrich Semantically User Content in Web2.0: A Case Study on Folksonomies

Semantic Web and Web2.0 emerged during the past decade promising to achieve new frontiers for the Web. On the one hand, the Semantic Web is an interlinked web of data, supported by ontological semantics and allowing for intelligent applications such as semantic search and integration of heterogeneous content across systems and applications. On the other hand, Web2.0 represents the new technologies and paradigms that revolutionised the user engagement in content creation and introduced novel means towards social interaction. Bridging the gap between Web2.0 and the Semantic Web has been proposed as a means to better manage and interact with the large amounts of user contributed content, which is a new challenge for Web2.0. This thesis focuses on a popular paradigm of Web2.0, folksonomies. In particular, we investigate the semantic enrichment of folksonomy tagspaces by reusing ontologies available in the Semantic Web. We identify the need for methods that automatically apply semantic descriptions to user generated content without requiring user intervention or alteration of the current tagging paradigm. We use an iterative approach in order to identify the characteristics of folksonomies and the attributes of knowledge sources that influence the semantic enrichment of tagspaces. We build on the results of our experimental studies to implement a folksonomy enrichment algorithm, that given an input tagspace, automatically creates a semantic structure that describes the meaning and relations of tags. We introduce measures for the evaluation of enriched tagspaces and finally, we propose a search algorithm that exploits the semantic structures to improve folksonomy search