The Cognitive Atlas: Employing Interaction Design Processes to Facilitate Collaborative Ontology Creation

The Cognitive Atlas is a collaborative knowledge-building project that aims to develop an ontology that characterizes the current conceptual framework among researchers in cognitive science and neuroscience. The project objectives from the beginning focused on usability, simplicity, and utility for end users. Support for Semantic Web technologies was also a priority in order to support interoperability with other neuroscience projects and knowledge bases. Current off-the-shelf semantic web or semantic wiki technologies, however, do not often lend themselves to simple user interaction designs for non-technical researchers and practitioners; the abstract nature and complexity of these systems acts as point of friction for user interaction, inhibiting usability and utility. Instead, we take an alternate interaction design approach driven by user centered design processes rather than a base set of semantic technologies. This paper reviews the initial two rounds of design and development of the Cognitive Atlas system, including interactive design decisions and their implementation as guided by current industry practices for the development of complex interactive systems

Features for Killer Apps from a Semantic Web Perspective

There are certain features that that distinguish killer apps from other ordinary applications. This chapter examines those features in the context of the semantic web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing semantic web applications. Killer apps are highly tranformative technologies that create new e-commerce venues and widespread patterns of behaviour. Information technology, generally, and the Web, in particular, have benefited from killer apps to create new networks of users and increase its value. The semantic web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. The authors hope that this chapter will help to highlight some of the common ingredients of killer apps in e-commerce, and discuss how such applications might emerge in the semantic web

Living Knowledge

Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following

The Development of an Evaluation Framework for eGovernment Systems

This paper is a positioning paper which outlines a proposal for engaging in the evaluation of eGovernment systems. The primary purpose of our proposed research is to develop, apply, test, and disseminate an evaluation framework which can support continuous, adaptable, and reflective evaluation of eGovernment systems. The theoretical bases for the methodology will be the Information Systems (IS), Soft Systems Methodology, SSM (Checkland and Scholes, 1990) which provides the platform for the analyses of the ‘soft’ aspects (e.g. human, political, cultural and organisational factors) and the Hard Systems Methodology (HSM) which provides methods and tools for quantitative measures and analyses of the system. A further three interrelated bases are: Reflective Practice, Organisational Learning (OL), and Information and Knowledge Management (IKM). Some of the key underlying principles to a successful evaluation framework are good data collection and analyses methods, an evaluative reflective practice approach whichentails the complete process of identification and analysis of strengths and problems, followed by rigorous testing, implementation, and revision of solutions. Such a cycle encourages organisational learning and promotes continuous improvement to both the evaluation framework and system. Additionally, it aims to cultivate an organisational culture that supports evaluation through reflection, continuous learning, and knowledge management which facilitates knowledge creation, capture, sharing, application and dissemination

Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

In public vehicles, one of the major concerns is driver's level of expertise for its direct proportionality to safety of passengers. Hence, before a driver is subjected to certain type of vehicle, he should be thoroughly evaluated and categorized with respect to certain parameters instead of only one‐time metric of having driving license. These aspects may be driver's expertise, vigilance, aptitude, experience years, cognition, driving style, formal education, terrain, region, minor violations, major accidents, and age group. The purpose of this categorization is to ascertain suitability of a driver for certain vehicle type(s) to ensure passengers' safety. Currently, no driver categorization technique fully comprehends the implicit as well as explicit characteristics of drivers dynamically. In this paper, machine learning–based dynamic and adaptive technique named D‐CHAITs (driver categorization through hybrid of artificial intelligence techniques) is proposed for driver categorization with an objective focus on driver's attributes modeled in DriverOntology. A supervised mode of learning has been employed on a labeled dataset, having diverse profiles of drivers with attributes pertinent to drivers' perspectives of demographics, behaviors, expertise, and inclinations. A comparative analysis of D‐CHAIT with three other machine learning techniques (fuzzy logic, case‐based reasoning, and artificial neural networks) is also presented. The efficacy of all techniques was empirically measured while categorizing the drivers based on their profiles through metrics of accuracy, precision, recall, F‐measure performance, and associated costs. These empirical quantifications assert D‐CHAIT as a better technique than contemporary ones. The novelty of proposed technique is signified through preprocessing of feature attributes, quality of data, training of machine learning model on more relevant data, and adaptivity This is the peer reviewed version of the following article: Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization, which has been published in final form at https://doi.org/10.1002/ett.3729. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions

Self-tuning Personalized Information Retrieval in an Ontology-Based Framework

Reliability is a well-known concern in the field of personalization technologies. We propose the extension of an ontology-based retrieval system with semantic-based personalization techniques, upon which automatic mechanisms are devised that dynamically gauge the degree of personalization, so as to benefit from adaptivity but yet reduce the risk of obtrusiveness and loss of user control. On the basis of a common domain ontology KB, the personalization framework represents, captures and exploits user preferences to bias search results towards personal user interests. Upon this, the intensity of personalization is automatically increased or decreased according to an assessment of the imprecision contained in user requests and system responses before personalization is applied

The Internet of Musical Things Ontology

The Internet of Musical Things (IoMusT) is an emerging research area consisting of the extension of the Internet of Things paradigm to the music domain. Interoperability represents a central issue within this domain, where heterogeneous objects dedicated to the production and/or reception of musical content (Musical Things) are envisioned to communicate between each other. This paper proposes an ontology for the representation of the knowledge related to IoMusT ecosystems to facilitate interoperability between Musical Things. There was no previous comprehensive data model for the IoMusT domain, however the new ontology relates to existing ontologies, including the SOSA Ontology for the representation of sensors and actuators and the Music Ontology focusing on the production and consumption of music. This paper documents the design of the ontology and its evaluation with respect to specific requirements gathered from an extensive literature review, which was based on scenarios involving IoMusT stakeholders, such as performers and audience members. The IoMusT Ontology can be accessed at: https://w3id.org/iomust#