Visual exploration of semantic-web-based knowledge structures

Humans have a curious nature and seek a better understanding of the world. Data, in- formation, and knowledge became assets of our modern society through the information technology revolution in the form of the internet. However, with the growing size of accumulated data, new challenges emerge, such as searching and navigating in these large collections of data, information, and knowledge. The current developments in academic and industrial contexts target the corresponding challenges using Semantic Web techno- logies. The Semantic Web is an extension of the Web and provides machine-readable representations of knowledge for various domains. These machine-readable representations allow intelligent machine agents to understand the meaning of the data and information; and enable additional inference of new knowledge. Generally, the Semantic Web is designed for information exchange and its processing and does not focus on presenting such semantically enriched data to humans. Visualizations support exploration, navigation, and understanding of data by exploiting humans’ ability to comprehend complex data through visual representations. In the context of Semantic- Web-Based knowledge structures, various visualization methods and tools are available, and new ones are being developed every year. However, suitable visualizations are highly dependent on individual use cases and targeted user groups. In this thesis, we investigate visual exploration techniques for Semantic-Web-Based knowledge structures by addressing the following challenges: i) how to engage various user groups in modeling such semantic representations; ii) how to facilitate understanding using customizable visual representations; and iii) how to ease the creation of visualizations for various data sources and different use cases. The achieved results indicate that visual modeling techniques facilitate the engagement of various user groups in ontology modeling. Customizable visualizations enable users to adjust visualizations to the current needs and provide different views on the data. Additionally, customizable visualization pipelines enable rapid visualization generation for various use cases, data sources, and user group

A visual exploration workflow as enabler for the exploitation of Linked Open Data

Abstract. Semantically annotating and interlinking Open Data results in Linked Open Data which concisely and unambiguously describes a knowledge domain. However, the uptake of the Linked Data depends on its usefulness to non-Semantic Web experts. Failing to support data consumers to understand the added-value of Linked Data and possible exploitation opportunities could inhibit its diffusion. In this paper, we propose an interactive visual workflow for discovering and ex-ploring Linked Open Data. We implemented the workflow considering academic library metadata and carried out a qualitative evaluation. We assessed the work-flow’s potential impact on data consumers which bridges the offer: published Linked Open Data; and the demand as requests for: (i) higher quality data; and (ii) more applications that re-use data. More than 70 % of the 34 test users agreed that the workflow fulfills its goal: it facilitates non-Semantic Web experts to un-derstand the potential of Linked Open Data.

PIWeCS: enhancing human/machine agency in an interactive composition system

This paper focuses on the infrastructure and aesthetic approach used in PIWeCS: a Public Space Interactive Web-based Composition System. The concern was to increase the sense of dialogue between human and machine agency in an interactive work by adapting Paine's (2002) notion of a conversational model of interaction as a ‘complex system’. The machine implementation of PIWeCS is achieved through integrating intelligent agent programming with MAX/MSP. Human input is through a web infrastructure. The conversation is initiated and continued by participants through arrangements and composition based on short performed samples of traditional New Zealand Maori instruments. The system allows the extension of a composition through the electroacoustic manipulation of the source material

Semantic web technology to support learning about the semantic web

This paper describes ASPL, an Advanced Semantic Platform for Learning, designed using the Magpie framework with an aim to support students learning about the Semantic Web research area. We describe the evolution of ASPL and illustrate how we used the results from a formal evaluation of the initial system to re-design the user functionalities. The second version of ASPL semantically interprets the results provided by a non-semantic web mining tool and uses them to support various forms of semantics-assisted exploration, based on pedagogical strategies such as performing later reasoning steps and problem space filtering

Intelligent Association Exploration and Exploitation of Fuzzy Agents in Ambient Intelligent Environments

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant and important associations between embedded-agent based services that form Ambient Intelligent Environments (AIEs). The embedded agents are used in two ways; first they monitor the inhabitants of the AIE, learning their behaviours in an online, non-intrusive and life-long fashion with the aim of pre-emptively setting the environment to the users preferred state. Secondly, they evaluate the relevance and significance of the associations to various services with the aim of eliminating redundant associations in order to minimize the agent computational latency within the AIE. The embedded agents employ fuzzy-logic due to its robustness to the uncertainties, noise and imprecision encountered in AIEs. We describe unique real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture and methods