Representing Virtual Communities for Advanced Services

2011 ◽  
pp. 402-404
Author(s):  
Miguel-Angel Sicilia ◽  
Elena Sanchez-Alonso
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Social Relationships ◽  
Language Processing ◽  
Intelligent Agents ◽  
Virtual Communities ◽  
Virtual Community ◽  
Community Members ◽  
Semantic Web Technologies ◽  
Tower Of Babel

The Semantic Web vision described by Berners-Lee, Hendler and Lassila (2001) represents an approach to enhancing the current World Wide Web with machine-understandable semantics. The essential idea is that Web resources identified by Uniform Resource Identifiers (URIs) can be described by metadata with the purpose of enabling automated processing. The precision of logics-based description would allow for the creation of tools that do not rely on natural language processing as current search tools, thus ideally removing the problems associated with ambiguity and implicitness in natural language. Metadata requires shared semantics in the context of an open Web, so that the proliferation of descriptions and vocabularies do not end up in a Tower of Babel. Ontologies provide the support for the shared representation of such semantics, and ontology mapping procedures could resolve the issues of semantic interoperability in case of disparate representations over the same domain. Ontologies act as descriptions of semantic domains used to express metadata records according to previously agreed definitions. Based on the infrastructure provided by ontologies and semantic metadata, the Semantic Web vision rests in the provision of distributed functionality built on intelligent agents and Web services. Since a virtual community is no other thing than a phenomenon that takes place on the Web, its elements are thus subject to being described by Semantic Web metadata, so that community-specific Semantic Web functionality could enhance the communicational elements that are part of communities. A typical scenario for such functionality is that of a filtering service that delivers recommended resources for community members according to the interests shared by members with similar interests. Other scenarios may result in the adaptation of the shared space inhabited by the community members; for example, member lists may be reordered according to the strength of social relationships, or messages could be circulated according to the network of social relationships (Sicilia & García, 2004). The Semantic Web opens many perspectives to create richer environments for virtual communities, and some specific technologies have already been created to deal with some of the definitional aspects of communities. Here we sketch the main elements that should be taken into account in developing experimental applications of Semantic Web technologies to the specifics of virtual communities.

SEMCL

2010 ◽  
Vol 1 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Weisen Guo ◽  
Steven B. Kraines
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Global Knowledge ◽  
Semantic Web Technologies ◽  
Natural Languages ◽  
Web Technologies ◽  
Language Knowledge ◽  
Cross Language

To promote global knowledge sharing, one should solve the problem that knowledge representation in diverse natural languages restricts knowledge sharing effectively. Traditional knowledge sharing models are based on natural language processing (NLP) technologies. The ambiguity of natural language is a problem for NLP; however, semantic web technologies can circumvent the problem by enabling human authors to specify meaning in a computer-interpretable form. In this paper, the authors propose a cross-language semantic model (SEMCL) for knowledge sharing, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. Also, this model can match knowledge descriptions in diverse languages. First, the methods used to support searches at the semantic predicate level are given, and the authors present a cross-language approach. Finally, an implementation of the model for the general engineering domain is discussed, and a scenario describing how the model implementation handles semantic cross-language knowledge sharing is given.

scholarly journals A Framework for Automatic Web Service Discovery Based on Semantics and NLP Techniques

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Asma Adala ◽  
Nabil Tabbane ◽  
Sami Tabbane
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Natural Language ◽  
Web Service ◽  
Language Processing ◽  
Service Discovery ◽  
Semantic Web Service ◽  
Semantic Web Technologies ◽  
Web Service Discovery ◽  
Automatic Discovery

As a greater number of Web Services are made available today, automatic discovery is recognized as an important task. To promote the automation of service discovery, different semantic languages have been created that allow describing the functionality of services in a machine interpretable form using Semantic Web technologies. The problem is that users do not have intimate knowledge about semantic Web service languages and related toolkits. In this paper, we propose a discovery framework that enables semantic Web service discovery based on keywords written in natural language. We describe a novel approach for automatic discovery of semantic Web services which employs Natural Language Processing techniques to match a user request, expressed in natural language, with a semantic Web service description. Additionally, we present an efficient semantic matching technique to compute the semantic distance between ontological concepts.

SEMCL

2013 ◽  
pp. 168-187
Author(s):  
Weisen Guo ◽  
Steven B. Kraines
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Global Knowledge ◽  
Semantic Web Technologies ◽  
Natural Languages ◽  
Web Technologies ◽  
Language Knowledge ◽  
Cross Language

To promote global knowledge sharing, one should solve the problem that knowledge representation in diverse natural languages restricts knowledge sharing effectively. Traditional knowledge sharing models are based on natural language processing (NLP) technologies. The ambiguity of natural language is a problem for NLP; however, semantic web technologies can circumvent the problem by enabling human authors to specify meaning in a computer-interpretable form. In this paper, the authors propose a cross-language semantic model (SEMCL) for knowledge sharing, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. Also, this model can match knowledge descriptions in diverse languages. First, the methods used to support searches at the semantic predicate level are given, and the authors present a cross-language approach. Finally, an implementation of the model for the general engineering domain is discussed, and a scenario describing how the model implementation handles semantic cross-language knowledge sharing is given.

Virtual Hate Communities in the 21st Century

2011 ◽  
pp. 34-53
Author(s):  
Glenn T. Tsunokai ◽  
Allison R. McGrath
Keyword(s):  
Virtual Communities ◽  
Hate Speech ◽  
Computer Mediated Communication ◽  
Interactive Media ◽  
Virtual Community ◽  
Community Members ◽  
Mediated Communication ◽  
Computer Mediated ◽  
Using Data ◽  
And Function

Technological innovations in computer-mediated communication have helped hate groups to transform themselves into virtual communities. Likeminded individuals are now able to unite from all parts of the globe to promote hatred against visible minorities and other out-groups. Through their online interactions, a sense of place is often created. In this chapter, we explore the content and function of online hate communities. Since bigotry tends to be the cornerstone of virtual hate communities, we highlight the legal debate surrounding the regulation of Internet hate speech; in particular, we address the question: Does the First Amendment protect virtual community members who use the Internet to advocate hate? Next, using data collected from the largest hate website, Stormfront.org, we also investigate how Stormfront members utilize interactive media features to foster a sense of community. Finally, we direct our attention to the future of online hate communities by outlining the issues that need to be further investigated.

Online Communities and Community Building

2011 ◽  
pp. 2893-2898
Author(s):  
Martin C. Kindsmüller ◽  
Sandro Leuchter ◽  
Leon Urbas
Keyword(s):  
Online Community ◽  
Virtual Communities ◽  
Virtual Community ◽  
Community Members ◽  
Face To Face ◽  
Computer Mediated ◽  
Bulletin Boards ◽  
Real Community ◽  
Definition Of ◽  
Tacit Assumptions

“Online community” is one of today’s buzzwords. Even though superficially it is not hard to understand, the term has become somewhat vague while being extensively used within the e-commerce business. Within this article, we refer to online community as being a voluntary group of users who partake actively in a certain computer-mediated service. The term “online community” is preferred over the term “virtual community,” as it denotes the character of the community more accurately: community members are interacting online as opposed to face to face. Furthermore, the term “virtual community” seems too unspecific, because it includes other communities that only exist virtually, whereas an online community in our definition is always a real community in the sense that community members know that they are part of the community. Nevertheless, there are other reasonable definitions of online community. An early and most influencing characterization (which unfortunately utilizes the term “virtual community”) was coined by Howard Rheingold (1994), who wrote: “…virtual communities are cultural aggregations that emerge when enough people bump into each other often enough in cyberspace. A virtual community is a group of people […] who exchanges words and ideas through the mediation of computer bulletin boards and networks” (p. 57). A more elaborated and technical definition of online community was given by Jenny Preece (2000), which since then, has been a benchmark for developers. She stated that an online community consists of four basic constituents (Preece, 2000, p. 3): 1. Socially interacting people striving to satisfy their own needs. 2. A shared purpose, such as interest or need that provides a reason to cooperate. 3. Policies in the form of tacit assumptions, rituals, or rules that guide the community members’ behavior. 4. A technical system that works as a carrier that mediates social interaction. Not explicitly mentioned in this characterization but nevertheless crucial for our aforementioned definition (and not in opposition to Preece’s position) is voluntary engagement.

scholarly journals Ontology evolution: a process-centric survey

2013 ◽  
Vol 30 (1) ◽  
pp. 45-75 ◽  
Author(s):  
Fouad Zablith ◽  
Grigoris Antoniou ◽  
Mathieu d'Aquin ◽  
Giorgos Flouris ◽  
Haridimos Kondylakis ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Information Systems ◽  
Semantic Web ◽  
Natural Language ◽  
Language Processing ◽  
Process Model ◽  
Ontology Evolution ◽  
Depth Analysis ◽  
Future Challenges ◽  
Industrial Adoption

AbstractOntology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.

Effects of social capital on online knowledge sharing: positive and negative perspectives

2015 ◽  
Vol 39 (4) ◽  
pp. 466-484 ◽  
Author(s):  
Chiu-Ping Hsu
Keyword(s):  
Social Capital ◽  
Knowledge Sharing ◽  
Conceptual Model ◽  
Structural Equation ◽  
Virtual Communities ◽  
Virtual Community ◽  
Community Members ◽  
Content Type ◽  
Relationship Continuity ◽  
Complementary Resources

Purpose – The purpose of this paper is to present a conceptual model of how both the benefits and risks of social capital influence online knowledge sharing among virtual community members towards enhancing members’ relationship continuity and virtual community reputations. In addition, this study addresses the effects of complementary resources on knowledge sharing. Design/methodology/approach – The structural equation modelling approach was applied to estimate the conceptual model by using survey data from virtual community members. A total of 626 valid questionnaires were collected. Findings – The empirical results showed that most of the research hypotheses were supported. The benefits of social capital and complementary resources have a positive effect on knowledge sharing, thereby strengthening relationship continuity and community reputation. Furthermore, the risk of social capital has a negative effect on knowledge sharing, as expected. Originality/value – A review of the literature reveals a lack of studies focusing on both the positive and negative influences of social capital on online knowledge sharing. This study therefore contributes to a comprehensive perspective for understanding online knowledge sharing in virtual communities.

Understanding Knowledge-Sharing Motivation, Incentive Mechanisms, and Satisfaction in Virtual Communities

2012 ◽  
Vol 40 (4) ◽  
pp. 639-647 ◽  
Author(s):  
Chia-Shen Chen ◽  
Shih-Feng Chang ◽  
Chih-Hsing Liu
Keyword(s):  
Knowledge Sharing ◽  
Survey Data ◽  
Virtual Communities ◽  
Incentive Mechanism ◽  
Virtual Community ◽  
Community Members ◽  
Incentive Mechanisms ◽  
Knowledge Sharing Behavior ◽  
The Incentive Mechanism ◽  
Sharing Behavior

Virtual communities have gained popularity as a means for individuals to learn and share knowledge. However, knowledge-sharing motivation, incentive mechanisms, and satisfaction in these communities have not been examined in detail. Using survey data from 169 community members, we investigated the conditions under which motivation, incentive mechanisms, and satisfaction affect knowledge-sharing behavior. We found that the incentive mechanism is a significant predictor of a virtual community member's motivation to obtain knowledge, and that the incentive mechanism and motivation do not positively affect a member's satisfaction that causes him/her to engage willingly in knowledge sharing with others in virtual communities.

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