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VISUAL 2014 : International Workshop on Visualizations and User Interfaces for Knowledge Engineering and Linked Data Analytics


When Nov 24, 2014 - Nov 25, 2014
Where Linköping, Sweden
Submission Deadline Sep 30, 2014
Notification Due Oct 17, 2014
Final Version Due Nov 7, 2014
Categories    visual analytics   knowledge engineering   visualization   semantic web

Call For Papers

Motivation and Objectives

With data continuously generated as a result of daily activities within organisations and new data sources (sensor streams, linked datasets, etc.) introduced within knowledge management, the growth of information is unprecedented. Providing knowledge engineers and data analysts with visualizations and well-designed user interfaces can significantly support understanding of the concepts, data instances and relationships of different domains.

The development of appropriate visualizations and user interfaces is a challenging task, given the size and complexity of the information that needs to be displayed and the varied backgrounds of the users. Further challenges emerge from technological developments and diverse application contexts. There is no “one size fits all” solution but the various use cases demand different visualization and interaction techniques. Ultimately, providing better visualizations and user interfaces will foster user engagement and likely lead to higher-quality results in different areas of knowledge engineering and linked data analytics.

This full-day workshop will be divided into two half-day tracks, one in the morning and the other in the afternoon, each focusing on one of the two workshop themes.

Track 1: Visualizations and User Interfaces for Knowledge Engineering

Visualizations and user interfaces are an integral part of knowledge engineering. They help to bridge the gap between domain experts and data management, and are essential to handle the increasing diversity of knowledge that is being modeled in ontologies, ensuring that it is easily accessible to a wide community. As knowledge-based systems and ontologies grow in size and complexity, the demand for comprehensive visualization and optimized interaction also rises.

A number of knowledge visualizations have become available in recent years, with some being already well-established, particularly in the field of ontology development. In other areas of knowledge engineering, such as ontology alignment and debugging, although several tools have recently been developed, few have a user interface, not to mention navigational aids or comprehensive visualization techniques. Other activities, such as data integration, rely on the relationships between the concepts of different ontologies, which not only multiplies the number of objects to be displayed but also compounds the problem with the portrayal of different kinds of relationships between concepts.

Topics of interest in this track include (but are not limited to):

- visualizations for (large and complex) ontologies
- user interfaces for ontology alignment and debugging
- visualizations and user interfaces for non-experts
- user interfaces and visualizations for linked data
- applications of novel interaction techniques (e.g. touch and gesture interaction)
- user interfaces for mobile knowledge engineering
- requirements analysis for visualizations in knowledge engineering
- user interfaces assisting people with disabilities
- knowledge visualizations for large displays and high resolutions
- user interfaces for collaborative knowledge engineering
- case studies of applying visualizations in knowledge engineering
- context-aware visualization and interaction techniques

Track 2: Visualizations and User Interfaces for Linked Data Analytics

New and traditional knowledge practices, digitisation of organisational processes, high performance computing and affordable datastores create an unprecedented amount of data as a part of daily organisational activities, at break-neck speed in a variety of formats. Conventional systems struggle to capture, store and analyze such dynamic and large scale data continuously generated. On its own, raw data has little value, but its value and significance is only unleashed when the data is extracted, processed and interpreted.

Visual Analytics attempts to address this challenge by harmoniously combining the strengths of human processing and electronic data processing. While semi-automated processes result in generating visualisations, humans can use visual processing and interactions to quickly identify trends, patterns and anomalies from large volumes of visual data. The growing challenges of analyzing big data, social media, linked data, and data streams have created an excellent opportunity for research in Visual Analytics.

Topics of interest in this track include (but are not limited to):

- interactive semantic systems
- design of interactive systems
- visual pattern discovery
- (semi-)automatic hypothesis generation
- augmented human reasoning
- novel visualizations of data and metadata
- visual approaches for semantic similarity measurement
- exploratory information visualization
- domain-specific visual analytics
- interactive systems in business intelligence
- cognition and sensemaking in visual contexts
- evaluation of interactive systems

Submission Guidelines

Paper submission and reviewing for this workshop will be electronic via EasyChair. The papers should be written in English, following Springer LNCS format, and be submitted in PDF.

The following types of contributions are welcome:

Full research papers (8-12 pages);
Experience papers (8-12 pages);
Position papers (6-8 pages);
Short research papers (4-6 pages);
System papers (4-6 pages).
Accepted papers will be published as a volume in the CEUR Workshop Proceedings series.


- Valentina Ivanova, Linköping University, Sweden
- Tomi Kauppinen, Aalto University, Finland and University of Bremen, Germany
- Steffen Lohmann, University of Stuttgart, Germany
- Suvodeep Mazumdar, The University of Sheffield, UK
- Catia Pesquita, University of Lisbon, Portugal
- Toomas Timpka, Linköping University, Sweden
- Kai Xu, Middlesex University, UK

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