Live Social Semantics

Social interactions are one of the key factors to the success of conferences and similar community gatherings. This paper describes a novel application that integrates data from the semantic web, online social networks, and a real-world contact sensing platform. This application was successfully deployed at ESWC09, and actively used by 139 people. Personal profiles of the participants were automatically generated using several Web~2.0 systems and semantic academic data sources, and integrated in real-time with face-to-face contact networks derived from wearable sensors. Integration of all these heterogeneous data layers made it possible to offer various services to conference attendees to enhance their social experience such as visualisation of contact data, and a site to explore and connect with other participants. This paper describes the architecture of the application, the services we provided, and the results we achieved in this deployment

Exploiting metadata for context creation and ranking on the desktop

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Organization and Usage of Learning Objects within Personal Computers

Research report of the ProLearn Network of Excellence (IST 507310), Deliverable 7.6To promote the integration of Desktop related Knowledge Management and Technology Enhanced Learning this deliverable aims at increasing the awareness of Desktop research within the Professional Learning community and at familiarizing the e-Learning researchers with the state-of-the-art in the relevant areas of Personal Information Management (PIM), as well as with the currently on-going activities and some of the regular PIM publication venues

Face Location - A Novel Approach to Post the User global Location

Today Web users are facing the problems of information overload on the World Wide Web which stores vast source of data. As a result, how to provide Web users with more exactly needed information is becoming a critical issue in web based information . People populate more information on Social Network Sites as their profile data and etc. Now a days most of the users have accounts in social network sites. In this paper we are discussing the popular document classifiers to find the location of a user  in the social networking sites,In this paper we make use of the profile data and other information to achieve the objective of RRR which is to give Right information to the Right set of People at Right Time In this paper, we present Supervised learning: a method to cascade the decision tree learning methods in classifying locations of the users posted in a social network site. Keywords Supervised Learning, Web mining,Social networks, Decision tree, facebook

Contextual Social Networking

The thesis centers around the multi-faceted research question of how contexts may be detected and derived that can be used for new context aware Social Networking services and for improving the usefulness of existing Social Networking services, giving rise to the notion of Contextual Social Networking. In a first foundational part, we characterize the closely related fields of Contextual-, Mobile-, and Decentralized Social Networking using different methods and focusing on different detailed aspects. A second part focuses on the question of how short-term and long-term social contexts as especially interesting forms of context for Social Networking may be derived. We focus on NLP based methods for the characterization of social relations as a typical form of long-term social contexts and on Mobile Social Signal Processing methods for deriving short-term social contexts on the basis of geometry of interaction and audio. We furthermore investigate, how personal social agents may combine such social context elements on various levels of abstraction. The third part discusses new and improved context aware Social Networking service concepts. We investigate special forms of awareness services, new forms of social information retrieval, social recommender systems, context aware privacy concepts and services and platforms supporting Open Innovation and creative processes. This version of the thesis does not contain the included publications because of copyrights of the journals etc. Contact in terms of the version with all included publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]

Semantics-based approach for generating partial views from linked life-cycle highway project data

The purpose of this dissertation is to develop methods that can assist data integration and extraction from heterogeneous sources generated throughout the life-cycle of a highway project. In the era of computerized technologies, project data is largely available in digital format. Due to the fragmented nature of the civil infrastructure sector, digital data are created and managed separately by different project actors in proprietary data warehouses. The differences in the data structure and semantics greatly hinder the exchange and fully reuse of digital project data. In order to address those issues, this dissertation carries out the following three individual studies. The first study aims to develop a framework for interconnecting heterogeneous life cycle project data into an unified and linked data space. This is an ontology-based framework that consists of two phases: (1) translating proprietary datasets into homogeneous RDF data graphs; and (2) connecting separate data networks to each other. Three domain ontologies for design, construction, and asset condition survey phases are developed to support data transformation. A merged ontology that integrates the domain ontologies is constructed to provide guidance on how to connect data nodes from domain graphs. The second study is to deal with the terminology inconsistency between data sources. An automated method is developed that employs Natural Language Processing (NLP) and machine learning techniques to support constructing a domain specific lexicon from design manuals. The method utilizes pattern rules to extract technical terms from texts and learns their representation vectors using a neural network based word embedding approach. The study also includes the development of an integrated method of minimal-supervised machine learning, clustering analysis, and word vectors, for computing the term semantics and classifying the relations between terms in the target lexicon. In the last study, a data retrieval technique for extracting subsets of an XML civil data schema is designed and tested. The algorithm takes a keyword input of the end user and returns a ranked list of the most relevant XML branches. This study utilizes a lexicon of the highway domain generated from the second study to analyze the semantics of the end user keywords. A context-based similarity measure is introduced to evaluate the relevance between a certain branch in the source schema and the user query. The methods and algorithms resulting from this research were tested using case studies and empirical experiments. The results indicate that the study successfully address the heterogeneity in the structure and terminology of data and enable a fast extraction of sub-models of data. The study is expected to enhance the efficiency in reusing digital data generated throughout the project life-cycle, and contribute to the success in transitioning from paper-based to digital project delivery for civil infrastructure projects

Multimedia Annotation Interoperability Framework

Multimedia systems typically contain digital documents of mixed media types, which are indexed on the basis of strongly divergent metadata standards. This severely hamplers the inter-operation of such systems. Therefore, machine understanding of metadata comming from different applications is a basic requirement for the inter-operation of distributed Multimedia systems. In this document, we present how interoperability among metadata, vocabularies/ontologies and services is enhanced using Semantic Web technologies. In addition, it provides guidelines for semantic interoperability, illustrated by use cases. Finally, it presents an overview of the most commonly used metadata standards and tools, and provides the general research direction for semantic interoperability using Semantic Web technologies

The genesis and emergence of Web 3.0: a study in the integration of artificial intelligence and the semantic web in knowledge creation

The web as we know it has evolved rapidly over the last decade. We have gone from a phase of rapid growth as seen with the dot.com boom where business was king to the current web 2.0 phase where social networking, Wiki’s, Blogs and other related tools flood the bandwidth of the world wide web. The empowerment of the web user with web 2.0 technologies has led to the exponential growth of data, information and knowledge on the web. With this rapid change, there is a need to logically categorise this information and knowledge so it can be fully utilised by all. It can be argued that the power of the knowledge held on the web is not fully exposed under its current structure and to improve this we need to explore the foundations of the web. This dissertation will explore the evolution of the web from its early days to the present day. It will examine the way web content is stored and discuss the new semantic technologies now available to represent this content. The research aims to demonstrate the possibilities of efficient knowledge extraction from a knowledge portal such as a Wiki or SharePoint portal using these semantic technologies. This generation of dynamic knowledge content within a limited domain will attempt to demonstrate the benefits of semantic web to the knowledge age