On relational learning and discovery in social networks: a survey

The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements

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

Linking Folksonomies and Ontologies for Supporting Knowledge Sharing: a State of the Art

Deliverable of ISICIL ANR-funded projectSocial tagging systems have recently become 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 report compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web

Architecture of participation : the realization of the Semantic Web, and Internet OS

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, February 2008.Includes bibliographical references (p. 65-68).The Internet and World Wide Web (WWW) is becoming an integral part of our daily life and touching every part of the society around the world including both well-developed and developing countries. The simple technology and genuine intention of the original WWW, which is to help researchers share and exchange information and data across incompatible platforms and systems, have evolved into something larger and beyond what one could conceive. While WWW has reached the critical mass, many limitations are uncovered. To address the limitations, the development of its extension, the Semantic Web, has been underway for more than five years by the inventor of WWW, Tim Berners-Lee, and the technical community. Yet, no significant impact has been made. Its awareness by the public is surprisingly and unfortunately low. This thesis will review the development effort of the Semantic Web, examine its progress which appears lagging compared to WWW, and propose a promising business model to accelerate its adoption path.by Shelley Lau.S.M

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

COMMUNITY DETECTION IN GRAPHS

Thesis (Ph.D.) - Indiana University, Luddy School of Informatics, Computing, and Engineering/University Graduate School, 2020Community detection has always been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach, community detection aims to explore node high-order closeness by leveraging graph topological structure. By grouping similar nodes or edges into the same community while separating dissimilar ones apart into different communities, graph structure can be revealed in a coarser resolution. It can be beneficial for numerous applications such as user shopping recommendation and advertisement in e-commerce, protein-protein interaction prediction in the bioinformatics, and literature recommendation or scholar collaboration in citation analysis. However, identifying communities is an ill-defined problem. Due to the No Free Lunch theorem [1], there is neither gold standard to represent perfect community partition nor universal methods that are able to detect satisfied communities for all tasks under various types of graphs. To have a global view of this research topic, I summarize state-of-art community detection methods by categorizing them based on graph types, research tasks and methodology frameworks. As academic exploration on community detection grows rapidly in recent years, I hereby particularly focus on the state-of-art works published in the latest decade, which may leave out some classic models published decades ago. Meanwhile, three subtle community detection tasks are proposed and assessed in this dissertation as well. First, apart from general models which consider only graph structures, personalized community detection considers user need as auxiliary information to guide community detection. In the end, there will be fine-grained communities for nodes better matching user needs while coarser-resolution communities for the rest of less relevant nodes. Second, graphs always suffer from the sparse connectivity issue. Leveraging conventional models directly on such graphs may hugely distort the quality of generate communities. To tackle such a problem, cross-graph techniques are involved to propagate external graph information as a support for target graph community detection. Third, graph community structure supports a natural language processing (NLP) task to depict node intrinsic characteristics by generating node summarizations via a text generative model. The contribution of this dissertation is threefold. First, a decent amount of researches are reviewed and summarized under a well-defined taxonomy. Existing works about methods, evaluation and applications are all addressed in the literature review. Second, three novel community detection tasks are demonstrated and associated models are proposed and evaluated by comparing with state-of-art baselines under various datasets. Third, the limitations of current works are pointed out and future research tracks with potentials are discussed as well

Social search in collaborative tagging networks : the role of ties

[no abstract