Semantic technologies: from niche to the mainstream of Web 3? A comprehensive framework for web Information modelling and semantic annotation

Context: Web information technologies developed and applied in the last decade have considerably changed the way web applications operate and have revolutionised information management and knowledge discovery. Social technologies, user-generated classification schemes and formal semantics have a far-reaching sphere of influence. They promote collective intelligence, support interoperability, enhance sustainability and instigate innovation. Contribution: The research carried out and consequent publications follow the various paradigms of semantic technologies, assess each approach, evaluate its efficiency, identify the challenges involved and propose a comprehensive framework for web information modelling and semantic annotation, which is the thesis’ original contribution to knowledge. The proposed framework assists web information modelling, facilitates semantic annotation and information retrieval, enables system interoperability and enhances information quality. Implications: Semantic technologies coupled with social media and end-user involvement can instigate innovative influence with wide organisational implications that can benefit a considerable range of industries. The scalable and sustainable business models of social computing and the collective intelligence of organisational social media can be resourcefully paired with internal research and knowledge from interoperable information repositories, back-end databases and legacy systems. Semantified information assets can free human resources so that they can be used to better serve business development, support innovation and increase productivity

ECSCW 2013 Adjunct Proceedings The 13th European Conference on Computer Supported Cooperative Work 21 - 25. September 2013, Paphos, Cyprus

This volume presents the adjunct proceedings of ECSCW 2013.While the proceedings published by Springer Verlag contains the core of the technical program, namely the full papers, the adjunct proceedings includes contributions on work in progress, workshops and master classes, demos and videos, the doctoral colloquium, and keynotes, thus indicating what our field may become in the future

User-Generated Tagging and Segmentation of Video Records of Practice: A Tool for Meaning-Marking.

The field of teacher education is consciously shifting its focus to be more “practice-oriented” and increasingly using video as a way of examining teaching practice. However, questions remain about how educators make sense of video and what types of tools and supports are needed. This exploratory study examines the potential of user-generated segmenting and tagging of videos of teaching practice as a tool for marking what educators find salient about teaching and the language they use to describe those phenomena. Data was collected in a teacher education program where video was used extensively for the purposes of learning about and improving teaching practice. There were two participant groups: pre-service teachers (n=6) and teacher educators/educational researchers (n=8). Each participant watched the same 8-minute video of practice and applied segments and tags to the video. The data included segments and tags created by each participant, interviews, and questionnaires; themes in the data were uncovered using content analysis. Interview data was used to interpret participants’ meaning in order to accurately categorize the tags. Using tag gardening strategies, hierarchal and networked tagging language was visualized. Findings indicate that user-generated segment and tag data of video records of practice can provide insight into what participants pay attention to and the language they use to describe that meaning making. This study uncovered three tensions that influenced participants’ segmenting and tagging behavior: findability versus nuance, concerns with being critical, and the need for a social context and community of practice. Educators’ specific and unique needs, purposes, and culture directly affected what participants marked as salient and what tagging language they used, resulting in some misleading segment and tag data. This work provides insights into the design of segmenting and tagging video tools and online communities of practice that support educators’ use of video. This research is particularly relevant to teacher education professionals and designers of tools that support educators’ use of video records of practice, laying the groundwork for further research on using and designing video annotation tools that support the work of teaching and aggregate data about how educators are making sense of videos of teaching.PHDEducational StudiesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116765/1/jrsteine_1.pd

Connected Information Management

Society is currently inundated with more information than ever, making efficient management a necessity. Alas, most of current information management suffers from several levels of disconnectedness: Applications partition data into segregated islands, small notes don’t fit into traditional application categories, navigating the data is different for each kind of data; data is either available at a certain computer or only online, but rarely both. Connected information management (CoIM) is an approach to information management that avoids these ways of disconnectedness. The core idea of CoIM is to keep all information in a central repository, with generic means for organization such as tagging. The heterogeneity of data is taken into account by offering specialized editors. The central repository eliminates the islands of application-specific data and is formally grounded by a CoIM model. The foundation for structured data is an RDF repository. The RDF editing meta-model (REMM) enables form-based editing of this data, similar to database applications such as MS access. Further kinds of data are supported by extending RDF, as follows. Wiki text is stored as RDF and can both contain structured text and be combined with structured data. Files are also supported by the CoIM model and are kept externally. Notes can be quickly captured and annotated with meta-data. Generic means for organization and navigation apply to all kinds of data. Ubiquitous availability of data is ensured via two CoIM implementations, the web application HYENA/Web and the desktop application HYENA/Eclipse. All data can be synchronized between these applications. The applications were used to validate the CoIM ideas

A Study on the Use of Ontologies to Represent Collective Knowledge

The development of ontologies has become an area of considerable research interest over the past number of years. Domain ontologies are often developed to represent a shared understanding that in turn indicates cooperative effort by a user community. However, the structure and form that an ontology takes is predicated both on the approach of the developer and the cooperation of the user community. A shift has taken place in recent years from the use of highly specialised and expressive ontologies to simpler knowledge models, progressively developed by community contribution. It is within this context that this thesis investigates the use of ontologies as a means to representing collective knowledge. It investigates the impact of the community on the approach to and outcome of knowledge representation and compares the use of simple terminological ontologies with highly structured expressive ontologies in community-based narrative environments

Using Data Mining for Facilitating User Contributions in the Social Semantic Web

This thesis utilizes recommender systems to aid the user in contributing to the Social Semantic Web. In this work, we propose a framework that maps domain properties to recommendation technologies. Next, we develop novel recommendation algorithms for improving personalized tag recommendation and for recommendation of semantic relations. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on those systems

Finding meaning in the masses: issues of taste, identity and sociability in digitality

This thesis focuses on the development of sociability within digitality, through an examination of three primary relationships: people and music, people and the Web 2.0 and people and each other. Mobile digital devices, such as the iPod, represent the convergence of musical taste and the internet. Both are inherently social, and, while critics have accused mobile digital devices as being socially isolating, the youth in this study have demonstrated an environment in which this technology is used as a means of communication. For these digital youth, such technologies are seen as a gateway to communication and the sharing of experiences. Having grown up fully immersed in digitality, these youth are negotiating new relationships with technology and each other, through the perceived invisibility of the technology. An important aspect of this research is the formation of identity and taste in digitality. Music is an integral facet of identity, a means to relate to others and form judgments on those we meet – but how is this affected by digitality? The internet encourages a loss of genre distinction, and a culture of eclecticism, whereby people can listen to a multitude of genres, often without knowing what exactly they are listening to, and without aligning their identities with specific genres or subgenres. Based on empirical data, it is demonstrated that this fragmentation of taste matches an intensified fragmentation of identity through social networking sites

Capturing perceived everyday lived landscapes through gamification and active crowdsourcing

Summary Landscapes are distinguishable areas of the earth with distinct characters comprised of tangible and intangible dimensions and entities. Interactions between humans and landscapes influence social, physical and mental well-being as well as guide behaviour. Understanding how landscapes are perceived has thus gained traction in sustainable and inclusive policy and decision making processes and public participation is called for. The recognised importance of understanding landscapes from an experiential and perceptual perspective and incorporating public participation in data generation efforts is reflected in overarching conventions, policy guidelines and frameworks including the European Landscape Convention (ELC), the Millennium Ecosystem Assessment (MEA), Natures Contributions to People (NCP) and the Landscape Character Assessment (LCA) framework. Major challenges for these conventions and frameworks are 1) how to collect data on landscape experiences and perceptions from a diverse group of individuals, 2) how to integrate and link physical entities, sensory experiences and intangible dimensions of landscapes and 3) how to identify other potential sources of landscape relevant information. The abundance of storage space and the accessibility of broadband internet have led to a burgeoning of user generated natural language content. In parallel, various paradigms of exploiting ubiquitous internet access for research purposes have emerged, including crowdsourcing, citizen science, volunteered geographic information and public participation geographic information systems. These low cost approaches have shown great potential in generating large amounts of data, however, they struggle with motivating and retaining participants. Gamification - broadly defined as adding entertaining or playful elements to applications or processes - has been found to increase user motivation and has explicitly been called for in landscape perception and preference research to diversify participant demographics. Meanwhile, natural language has been found to be deeply intertwined with thought and emotion and has been identified as a rich source of semantic data on how landscapes are perceived and experienced. Written texts and the ways in which these can be analysed have gained particular interest. Therefore, the overall goal of this thesis is to develop and implement a gamified crowdsourcing application to collect natural language landscape descriptions and to analyse and explore the contributions in terms of how landscapes are perceived through sensory experiences and how additional landscape relevant natural language can be identified. To approach this goal, I first elicit key data and feature requirements to collect landscape relevant information from a heterogeneous audience. Guided by the identified requirements, I develop and implement Window Expeditions, a gamified active crowdsourcing platform geared towards collecting natural language descriptions of everyday lived landscapes. The generated corpus of natural language is explored using computational methods and I present and discuss the results in light of who the contributors are, the locations from which participants contribute and salient terms found in English and German. In a further step I annotate a subset of English contributions according to the contained biophysical elements, sensory experiences and cultural ecosystem (dis)services and explore these in terms of how they are linked. Finally, I present a novel approach of using a curated high quality landscape specific dataset to computationally identify similar documents in other corpora using sentence-transformers. Using the Mechanics, Dynamics and Aesthetics (MDA) framework, the aesthetics of discovery, expression and fellowship were identified as most fitting for an active crowdsourcing platform. In addition, four groups of main dynamics were found, namely general dynamics of user interactions, contribution dynamics, exploration dynamics and moderation dynamics. The application was gamified by introducing points and leader boards and the platform was implemented in German and English (with French being added at a later point) to collect landscape descriptions in multiple languages. Demographic information was collected about the users including their year of birth, their gender, if they were at home whilst contributing and what languages users believed to be fluent in. Using the Mechanics, Dynamics and Aesthetics (MDA) framework, the aesthetics of discovery, expression and fellowship were identified as most fitting for an active crowdsourcing platform. In addition, four groups of main dynamics were found, namely general dynamics of user interactions, contribution dynamics, exploration dynamics and moderation dynamics. The application was gamified by introducing points and leader boards and the platform was implemented in German and English (with French being added at a later point) to collect landscape descriptions in multiple languages. Demographic information was collected about the users including their year of birth, their gender, if they were at home whilst contributing and what languages users believed to be fluent in reporting not being at home (n = 172) who were more likely to contribute from areas of herbaceous vegetation. Terms describing salient elements of everyday lived environments such as "tree", "house", "garden" and "street", as well as weather related phenomena and colours were found frequently in both English and German contributions in the generated corpus. Further, terms related to space, time and people were found significantly more frequently in the generated corpus compared to general natural language and representative landscape image descriptions highlighting the importance of spatial features as well as people and the times at which these were observed. Notably, descriptions referring to trees and birds were frequently found in the contributed texts, underlining their saliency in everyday lived landscapes. The results show biophyiscal terms related to vegetation (n = 556) and the built environment (n = 468) as well as weather related terms (n = 452) to be most prominent. Further, contributions referencing visual (n = 186) and auditory (n = 96) sensory experiences were found most often with positive sensory experiences being most common (n = 168) followed by neutral (n = 86) and negative (n = 68). In regards to the intangible dimensions captured in the contributed landscape descriptions, recreation (n = 68) was found most often followed by heritage (n = 36), identity (n = 26) and tranquillity (n = 23). Through linking biophysical elements, sensory experiences and cultural ecosystem (dis)services, the results show that the biophysical category of animals appears often with the sensory experience of smell/taste and the biophysical category of moving objects appears more than expected with the sensory experience of sound. Further, the results show the cultural ecosystem service of inspiration to often appear with the biophysical category of natural features and tranquillity with weather. Using a curated subcorpus of English natural language landscape descriptions (n = 428) collected with Window Expeditions, similar documents in other collections were identified. Through translating documents to vectors by means of sentence-transformers and calculating cosine similarity scores, a total of 6075 to 8172 documents were identified to be similar to contributions to Window Expeditions, depending on if the initial dataset was prefiltered for biophysical noun lemmas (a list of biophysical landscape elements derived from the Window Expeditions corpus) and Craik’s list adjectives (a list of common adjectives used to describe landscapes). Latent Dirichlet allocation topic modelling, a clustering approach which is commonly used to identify overarching topics or themes in collections of natural language, shows four distinct clusters in both Window Expeditions as well as in the corpus of identified similar documents, namely urban and residential, rural and natural, autumn and colours and snow and weather. Overall, the results presented in this thesis provide further evidence to work that natural language is a rich source of landscape specific information, capturing underlying semantics of a multitude of referenced landscape dimensions. In particular, this thesis demonstrates that computationally aided approaches to analysing and exploring landscape relevant textual data can give detailed insights into salient features of landscapes and how individuals perceive and experience these. Especially when complemented by human annotation, natural language landscape descriptions are a welcome source of data about a landscape’s biophysical elements, individual sensory experiences in landscapes and the perceived cultural ecosystem (dis)services. The findings of this thesis are accompanied by various limitations, chief amongst which are the possibilities of users to falsify their locations, the rather small amount of data that was collected through Window Expeditions and the Eurocentric definitions and approaches common in landscape perception research. The former two limitations can be addressed through implementational reiterations and promotional efforts, whereas the latter limitation calls for further consideration of the socio-culturally induced construction of landscape perception research and a rethinking of holistic approaches, especially in multicultural participatory contexts. The work presented in this thesis shows great potential in complementing landscape perception research with gamified methods of data generation. Active crowdsourcing can be a cost efficient and scalable approach of generating much needed data from a diverse audience. Exploring landscape relevant natural language with both quantitative and qualitative methods from various disciplines including geographic information science, linguistics and machine learning can lead to new insights into landscape perception, sensory landscape experiences and how these are expressed