Social Semantic Web Research Articles

Human Intelligence in the Process of Semantic Content Creation

Despite significant progress over the last years the large-scale adoption of semantic technologies is still to come. One of the reasons for this state of affairs is assumed to be the lack of useful semantic content, a prerequisite for almost every IT system or application using semantics. Through its very nature, this content can not be created fully automatically, but requires, to a certain degree, human contribution. The interest of Internet users in semantics, and in particular in creating semantic content, is, however, low. This is understandable if we think of several characteristics exposed by many of the most prominent semantic technologies, and the applications thereof. One of these characteristics is the high barrier of entry imposed. Interacting with semantic technologies today requires specific skills and expertise on subjects which are not part of the mainstream IT knowledge portfolio. A second characteristic are the incentives that are largely missing in the design of most semantic applications. The benefits of using machine-understandable content are in most applications fully decoupled from the effort of creating and maintaining this content. In other words, users do not have a motivation to contribute to the process. Initiatives in the areas of the Social Semantic Web acknowledged this problem, and identified mechanisms to motivate users to dedicate more of their time and resources to participate in the semantic content creation process. Still, even if incentives are theoretically in place, available human labor is limited and must only be used for those tasks that are heavily dependent on human intervention, and cannot be reliably automated. In this article, we concentrate on this step in between. As a first contribution, we analyze the process of semantic content creation in order to identify those tasks that are inherently human-driven. When building semantic applications involving these specific tasks, one has to install incentive schemes that are likely to encourage users to perform exactly these tasks that crucially rely on manual input. As a second contribution of the article, we propose incentives or incentive-driven tools that can be used to increase user interest in semantic content creation tasks. We hope that our findings will be adopted as recommendations for establishing a fundamentally new form of design of semantic applications by the semantic technologies community.

The Social Semantic Web in Intelligent Learning Environments: state of the art and future challenges

Today's technology-enhanced learning practices cater to students and teachers who use many different learning tools and environments and are used to a paradigm of interaction derived from open, ubiquitous, and socially oriented services. In this context, a crucial issue for education systems in general, and for Intelligent Learning Environments (ILEs) in particular, is related to the ability of leveraging these new paradigms for creating, maintaining and sharing the knowledge that these systems embed. This will enable ILEs to benefit from shared information from disparate systems, which is related to learning content and student activities, so that the overall complexity of system development and maintenance would be reduced while at the same time improving the capability of personalization, context-awareness, and interaction. In this article, we investigate how the Social Semantic Web can be leveraged for enabling and easing this process. We first analyze each module of a typical ILE, showing how it can benefit from the Social Semantic Web paradigm and then proceed to investigate how this new paradigm can be leveraged for increasing interactivity level of ILEs.

Reports of the AAAI 2009 Spring Symposia

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, was pleased to present the 2009 Spring Symposium Series, held Monday through Wednesday, March 23–25, 2009 at Stanford University. The titles of the nine symposia were Agents that Learn from Human Teachers, Benchmarking of Qualitative Spatial and Temporal Reasoning Systems, Experimental Design for Real-World Systems, Human Behavior Modeling, Intelligent Event Processing, Intelligent Narrative Technologies II, Learning by Reading and Learning to Read, Social Semantic Web: Where Web 2.0 Meets Web 3.0, and Technosocial Predictive Analytics. The goal of the Agents that Learn from Human Teachers was to investigate how we can enable software and robotics agents to learn from real-time interaction with an everyday human partner. The aim of the Benchmarking of Qualitative Spatial and Temporal Reasoning Systems symposium was to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning and identify a graded set of challenges for future midterm and long-term research. The Experimental Design symposium discussed the challenges of evaluating AI systems. The Human Behavior Modeling symposium explored reasoning methods for understanding various aspects of human behavior, especially in the context of designing intelligent systems that interact with humans. The Intelligent Event Processing symposium discussed the need for more AI-based approaches in event processing and defined a kind of research agenda for the field, coined as intelligent complex event processing (iCEP). The Intelligent Narrative Technologies II AAAI symposium discussed innovations, progress, and novel techniques in the research domain. The Learning by Reading and Learning to Read symposium explored two aspects of making natural language texts semantically accessible to, and processable by, machines. The Social Semantic Web symposium focused on the real-world grand challenges in this area. Finally, the Technosocial Predictive Analytics symposium explored new methods for anticipatory analytical thinking that provide decision advantage through the integration of human and physical models.

SSWP: A Social Semantic Web Portal for Effective Communication in Construction

In the construction industry, there is a pressing need for computer systems that will facilitate information exchange and knowledge sharing among all industry practitioners. The Social Semantic Web Portal (SSWP) proposed in this paper will accomplish three tasks: (1) the streamlining of information exchange about any individual project, (2) the encouragement of knowledge sharing in general, and (3) the virtual grouping of people with similar interests to form communities of practice. A domain ontology is developed in order to encapsulate knowledge about industrial actors and their roles in relation to sibling ontologies that conceptualize construction products and processes. This domain ontology is then tailored to be the cornerstone (the knowledge base) that will enable the semantics of Web services. The concept of the Social Web is employed to validate knowledge items and to connect users with similar interests. The information flow is realized through a content-based publish/subscribe system. The SSWP will semantically connect a user with knowledge items and socially link a user to his/her peers.

Semantics-Empowered Social Computing

In this article, we discuss some of the challenges in marking-up or annotating UGC, a first step toward the realization of the social semantic Web. Using examples from real-world UGC, we show how domain knowledge can effectively complement statistical natural language processing techniques for metadata creation.

Learning With Social Semantic Technologies - Exploiting Latest Tools

Even though it was only about three years ago that Social Software became a trend, it has become a common practice to utilize Social Software in learning institutions. It brought about a lot of advantages, but also challenges. Amounts of distributed and often unstructured user generated content make it difficult to meaningfully process and find relevant information. According to the estimate of the authors, the solution lies in underpinning Social Software with structure resulting in Social Semantic Software. In this contribution we introduce the central concepts Social Software, Semantic Web and Social Semantic Web and show how Social Semantic Technologies might be utilized in the higher education context.

Interlinking the Social Web with Semantics

One of the most visible trends on the Web is the emergence of Social Web sites, which help people create and gather knowledge by simplifying user contributions via blogs, tagging and folksonomies, wikis, podcasts, and online social networks. Current online-community sites are isolated from one another, like islands in a sea. Various discussions might contain complementary knowledge and discussions—parts of the answer a person is looking for—but people participating in one discussion can't readily access information about related discussions elsewhere. The potential synergies among many sites, communities, and services are expensive to exploit, and their data are difficult and cumbersome to link and reuse. The main reason for this lack of interoperation is that for the most part in the Social Web, common standards still don't exist for knowledge and information exchange and interoperation. However, the Semantic Web effort aims to provide the tools needed to define extensible, flexible standards for this purpose. The Semantic Web technology stack is well defined, enabling the creation of metadata and associated vocabularies. The Semantic Web effort is in an ideal position to make Social Web sites interoperable. Applying Semantic Web frameworks including SIOC (Semantically Interlinked Online Communities) and FOAF (Friend-of-a-Friend) to the Social Web can lead to a Social Semantic Web, creating a network of interlinked and semantically rich knowledge. This article is part of a special issue called Semantic Web Update.

Unevenness in network properties on the social semantic web

This paper studies unevenness in network properties on the social Semantic Web. First, we propose a two-step methodology for processing and analyzing social network data from the Semantic Web. Using the SPARQL query language, a derived RDF graph can be constructed that is tailored to a specific question. After a brief introduction to the notion of unevenness, this methodology is applied to examine unevenness in network properties of semantic data. Comparing Lorenz curves for different centrality measures, it is shown how examinations of unevenness can provide crucial hints regarding the topology of (social) Semantic Web data.

Hacia una web semántica social

The internet’s evolution toward a scenario of greater potential, with users increasingly involved in its development and management, demands a lower level of semantic ambiguity in the documents that are provided. The proposed semantic web, a concept that appeared almost a decade ago, has had only modest impact. On the other hand, web 2.0, an autonomous evolution of the web toward a collaborative environment, has met with enormous success. The solution devised by web 2.0 has exceeded the limitations of natural language processing tools and statistical approaches. For that reason, it seems logical to analyze the potential contributions that web 2.0 concepts could make to further the development of the semantic web. Some ways of making the leap from a social web to a social semantic web are discussed.