568 research outputs found

    Ontology learning from folksonomies.

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    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

    Living Knowledge

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    Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following

    Social and Semantic Contexts in Tourist Mobile Applications

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    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

    Adding Context to Social Tagging Systems

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    Many of the features of Web 2.0 encourage users to actively interact with each other. Social tagging systems represent one of the good examples that reflect this trend on the Web. The primary purpose of social tagging systems is to facilitate shared access to resources. Our focus in this paper is on the attempts to overcome some of the limitations in social tagging systems such as the flat structure of folksonomies and the absence of semantics in terms of information retrieval. We propose and develop an integrated approach, social tagging systems with directory facility, which can overcome the limitations of both traditional taxonomies and folksonomies. Our preliminary experiments indicate that this approach is promising and that the context provided by the directory facility improves the precision of information retrieval. As well, our synonym detection algorithm is capable of finding synonyms in social tagging systems without any external inputs

    Community-driven & Work-integrated Creation, Use and Evolution of Ontological Knowledge Structures

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    An integrated approach to discover tag semantics

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    Tag-based systems have become very common for online classification thanks to their intrinsic advantages such as self-organization and rapid evolution. However, they are still affected by some issues that limit their utility, mainly due to the inherent ambiguity in the semantics of tags. Synonyms, homonyms, and polysemous words, while not harmful for the casual user, strongly affect the quality of search results and the performances of tag-based recommendation systems. In this paper we rely on the concept of tag relatedness in order to study small groups of similar tags and detect relationships between them. This approach is grounded on a model that builds upon an edge-colored multigraph of users, tags, and resources. To put our thoughts in practice, we present a modular and extensible framework of analysis for discovering synonyms, homonyms and hierarchical relationships amongst sets of tags. Some initial results of its application to the delicious database are presented, showing that such an approach could be useful to solve some of the well known problems of folksonomies

    prototypical implementations

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    In this technical report, we present prototypical implementations of innovative tools and methods developed according to the working plan outlined in Technical Report TR-B-09-05 [23]. We present an ontology modularization and integration framework and the SVoNt server, the server-side end of an SVN- based versioning system for ontologies in the Corporate Ontology Engineering pillar. For the Corporate Semantic Collaboration pillar, we present the prototypical implementation of a light-weight ontology editor for non-experts and an ontology based expert finder system. For the Corporate Semantic Search pillar, we present a prototype for algorithmic extraction of relations in folksonomies, a tool for trend detection using a semantic analyzer, a tool for automatic classification of web documents using Hidden Markov models, a personalized semantic recommender for multimedia content, and a semantic search assistant developed in co-operation with the Museumsportal Berlin. The prototypes complete the next milestone on the path to an integral Cor- porate Semantic Web architecture based on the three pillars Corporate Ontol- ogy Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search, as envisioned in [23]

    User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration

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    Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks. Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion. Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data

    Digital libraries: The challenge of integrating instagram with a taxonomy for content management

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    Interoperability and social implication are two current challenges in the digital library (DL) context. To resolve the problem of interoperability, our work aims to find a relationship between the main metadata schemas. In particular, we want to formalize knowledge through the creation of a metadata taxonomy built with the analysis and the integration of existing schemas associated with DLs. We developed a method to integrate and combine Instagram metadata and hashtags. The final result is a taxonomy, which provides innovative metadata with respect to the classification of resources, as images of Instagram and the user-generated content, that play a primary role in the context of modern DLs. The possibility of Instagram to localize the photos inserted by users allows us to interpret the most relevant and interesting informative content for a specific user type and in a specific location and to improve access, visibility and searching of library content

    Utilizing distributed web resources for enhanced knowledge representation

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