Evaluating the semantic web: a task-based approach

The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape

What can be done with the Semantic Web? An overview of Watson-based applications

Thanks to the huge efforts deployed in the community for creating, building and generating semantic information for the Semantic Web, large amounts of machine processable knowledge are now openly available. Watson is an infrastructure component for the Semantic Web, a gateway that provides the necessary functions to support applications in using the Semantic Web. In this paper, we describe a number of applications relying on Watson, with the purpose of demonstrating what can be achieved with the Semantic Web nowadays and what sort of new, smart and useful features can be derived from the exploitation of this large, distributed and heterogeneous base of semantic information

Learning Structured Knowledge from Social Tagging Data A critical review of methods and techniques

For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and the Semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data

Semantic Interaction in Web-based Retrieval Systems : Adopting Semantic Web Technologies and Social Networking Paradigms for Interacting with Semi-structured Web Data

Existing web retrieval models for exploration and interaction with web data do not take into account semantic information, nor do they allow for new forms of interaction by employing meaningful interaction and navigation metaphors in 2D/3D. This thesis researches means for introducing a semantic dimension into the search and exploration process of web content to enable a significantly positive user experience. Therefore, an inherently dynamic view beyond single concepts and models from semantic information processing, information extraction and human-machine interaction is adopted. Essential tasks for semantic interaction such as semantic annotation, semantic mediation and semantic human-computer interaction were identified and elaborated for two general application scenarios in web retrieval: Web-based Question Answering in a knowledge-based dialogue system and semantic exploration of information spaces in 2D/3D

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

Learning Relations from Social Tagging Data

An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds between them. Experiments were conducted using two large, real-world datasets, the Bibsonomy dataset which is used to extract tags and their features, and the DBpedia dataset which is used as the ground truth. Three sets of features were designed and extracted based on topic distributions, similarity and probabilistic associations. Evaluation results with respect to the ground truth demonstrate that our method outperforms existing ones based on various features and heuristics. Future studies are suggested to study the Knowledge Base Enrichment from folksonomies and deep neural network approaches to process tagging data

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

Ontology learning from folksonomies.

Chen, Wenhao.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (p. 63-70).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Ontologies and Folksonomies --- p.1Chapter 1.2 --- Motivation --- p.3Chapter 1.2.1 --- Semantics in Folksonomies --- p.3Chapter 1.2.2 --- Ontologies with basic level concepts --- p.5Chapter 1.2.3 --- Context and Context Effect --- p.6Chapter 1.3 --- Contributions --- p.6Chapter 1.4 --- Structure of the Thesis --- p.8Chapter 2 --- Background Study --- p.10Chapter 2.1 --- Semantic Web --- p.10Chapter 2.2 --- Ontology --- p.12Chapter 2.3 --- Folksonomy --- p.14Chapter 2.4 --- Cognitive Psychology --- p.17Chapter 2.4.1 --- Category (Concept) --- p.17Chapter 2.4.2 --- Basic Level Categories (Concepts) --- p.17Chapter 2.4.3 --- Context and Context Effect --- p.20Chapter 2.5 --- F1 Evaluation Metric --- p.21Chapter 2.6 --- State of the Art --- p.23Chapter 2.6.1 --- Ontology Learning --- p.23Chapter 2.6.2 --- Semantics in Folksonomy --- p.26Chapter 3 --- Ontology Learning from Folksonomies --- p.28Chapter 3.1 --- Generating Ontologies with Basic Level Concepts from Folksonomies --- p.29Chapter 3.1.1 --- Modeling Instances and Concepts in Folksonomies --- p.29Chapter 3.1.2 --- The Metric of Basic Level Categories (Concepts) --- p.30Chapter 3.1.3 --- Basic Level Concepts Detection Algorithm --- p.31Chapter 3.1.4 --- Ontology Generation Algorithm --- p.34Chapter 3.2 --- Evaluation --- p.35Chapter 3.2.1 --- Data Set and Experiment Setup --- p.35Chapter 3.2.2 --- Quantitative Analysis --- p.36Chapter 3.2.3 --- Qualitative Analysis --- p.39Chapter 4 --- Context Effect on Ontology Learning from Folksonomies --- p.43Chapter 4.1 --- Context-aware Basic Level Concepts Detection --- p.44Chapter 4.1.1 --- Modeling Context in Folksonomies --- p.44Chapter 4.1.2 --- Context Effect on Category Utility --- p.45Chapter 4.1.3 --- Context-aware Basic Level Concepts Detection Algorithm --- p.46Chapter 4.2 --- Evaluation --- p.47Chapter 4.2.1 --- Data Set and Experiment Setup --- p.47Chapter 4.2.2 --- Result Analysis --- p.49Chapter 5 --- Potential Applications --- p.54Chapter 5.1 --- Categorization of Web Resources --- p.54Chapter 5.2 --- Applications of Ontologies --- p.55Chapter 6 --- Conclusion and Future Work --- p.57Chapter 6.1 --- Conclusion --- p.57Chapter 6.2 --- Future Work --- p.59Bibliography --- p.6