Onto Collab: Strategic review oriented collaborative knowledge modeling using ontologies

Modeling efficient knowledge bases for improving the semantic property of the World Wide Web is mandatory for promoting innovations and developments in World Wide Web. There is a need for efficient and organized modeling of the knowledge bases. In this paper, a strategy Onto Collab is proposed for construction of knowledge bases using ontology modeling. Ontologies are visualized as the basic building blocks of the knowledge in the web. The cognitive bridge between the human conceptual understanding of real world data and the processable data by computing systems is represented by Ontologies. A domain is visualized as a collection of similar ontologies. A review based strategy is proposed over a secure messaging system to author ontologies and a platform for retracing the domain ontologies as individuals and as a team is proposed. Evaluations for ontologies constructed pertaining to a domain for non-wiki knowledge bases is carried out

Improving Knowledge Acquisition in Collaborative Knowledge Construction Tool with Virtual Catalyst

Noctua is a web tool to assist in Knowledge Acquisition and Collaborative Knowledge Construction processes. Noctua has an innovation: a Virtual Catalyst designed to facilitate the task of eliciting and validating knowledge. The Virtual Catalyst queries participants, proposing new knowledge, seeking confirmation to the knowledge already elicited, and showing conflicting opinions. The Virtual Catalyst takes into account participants' profiles in order to automatically ask them questions related to each one's field of knowledge or interest. This paper presents Noctua and its Virtual Catalyst. The tool was submitted to experimentation and the analysis of the results showed that the primary goal of increasing the rate of knowledge construction was achieved (up to 144 % in the rate of knowledge creation), and also showed some unexpected beneficial outcomes

Social Data Visualization System for Understanding Diffusion Patterns on Twitter: A Case Study on Korean Enterprises

Online social media have been playing an important role of creating and diffusing information to many users. It means the users can get cognitive influence to the other users. Thus, it is important to understand how the information can be diffused by interactions among users through online social media. In this paper, we design a social media monitoring system (called "TweetPulse'') which can analyze and show meaningful diffusion patterns (DP) among the users. Particularly, TweetPulse focuses on visualizing information diffusion in Twitter, given a certain time duration. Also, this work has investigated the relationships 1) between DP and event detecting, 2) between DP and emotional words, and 3) between DP and the number of followers of the users. Thereby, to understand the continuous patterns of the information diffusion, we propose two different types of analytic methods, which are 1) macroscopic approach and 2) microscopic approach. For evaluating the proposed method, we have collected and preprocessed the dataset during about 4 months (14 March 2012 to 12 July 2012). As a conclusion, TweetPulse has helped users to easily understand DP from a large scale dataset streaming through Twitter

From the web of data to a world of action

This is the author’s version of a work that was accepted for publication in Web Semantics: Science, Services and Agents on the World Wide Web. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Web Semantics: Science, Services and Agents on the World Wide Web 8.4 (2010): 10.1016/j.websem.2010.04.007This paper takes as its premise that the web is a place of action, not just information, and that the purpose of global data is to serve human needs. The paper presents several component technologies, which together work towards a vision where many small micro-applications can be threaded together using automated assistance to enable a unified and rich interaction. These technologies include data detector technology to enable any text to become a start point of semantic interaction; annotations for web-based services so that they can link data to potential actions; spreading activation over personal ontologies, to allow modelling of context; algorithms for automatically inferring 'typing' of web-form input data based on previous user inputs; and early work on inferring task structures from action traces. Some of these have already been integrated within an experimental web-based (extended) bookmarking tool, Snip!t, and a prototype desktop application On Time, and the paper discusses how the components could be more fully, yet more openly, linked in terms of both architecture and interaction. As well as contributing to the goal of an action and activity-focused web, the work also exposes a number of broader issues, theoretical, practical, social and economic, for the Semantic Web.Parts of this work were supported by the Information Society Technologies (IST) Program of the European Commission as part of the DELOS Network of Excellence on Digital Libraries (Contract G038- 507618). Thanks also to Emanuele Tracanna, Marco Piva, and Raffaele Giuliano for their work on On Time

Cognition-based approaches for high-precision text mining

This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both the breadth and depth of knowledge extracted from text. This research has made contributions in the areas of a cognitive approach to automated concept recognition in. Cognitive approaches to search, also called concept-based search, have been shown to improve search precision. Given the tremendous amount of electronic text generated in our digital and connected world, cognitive approaches enable substantial opportunities in knowledge discovery. The generation and storage of electronic text is ubiquitous, hence opportunities for improved knowledge discovery span virtually all knowledge domains. While cognition-based search offers superior approaches, challenges exist due to the need to mimic, even in the most rudimentary way, the extraordinary powers of human cognition. This research addresses these challenges in the key area of a cognition-based approach to automated concept recognition. In addition it resulted in a semantic processing system framework for use in applications in any knowledge domain. Confabulation theory was applied to the problem of automated concept recognition. This is a relatively new theory of cognition using a non-Bayesian measure, called cogency, for predicting the results of human cognition. An innovative distance measure derived from cogent confabulation and called inverse cogency, to rank order candidate concepts during the recognition process. When used with a multilayer perceptron, it improved the precision of concept recognition by 5% over published benchmarks. Additional precision improvements are anticipated. These research steps build a foundation for cognition-based, high-precision text mining. Long-term it is anticipated that this foundation enables a cognitive-based approach to automated ontology learning. Such automated ontology learning will mimic human language cognition, and will, in turn, enable the practical use of cognitive-based approaches in virtually any knowledge domain --Abstract, page iii