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[go-friends] CFP: Visualizations and User Interfaces for Knowledge Engineering and Linked Data Analytics

Catia Pesquita cpesquita at
Wed Sep 3 05:40:02 PDT 2014


Visualizations and User Interfaces for Knowledge Engineering and Linked
Data Analytics

International Workshop at EKAW 2014, 19th International Conference on
Knowledge Engineering and Knowledge Management
November 24 or 25, 2014, Linköping, Sweden

Motivation and Objectives

With data continuously generated as a result of daily activities within
organizations 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
- applications of novel interaction techniques (e.g. touch and gesture
- 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
- user interfaces and visualizations for linked data
- context-aware visualization and interaction techniques

Track 2: Visualizations and User Interfaces for Linked Data Analytics

New and traditional knowledge practices, digitization of organizational
processes, high performance computing and affordable datastores create an
unprecedented amount of data as a part of daily organizational 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 visualizations, 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

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.

Important Dates

- Submission: September 19, 2014
- Notification: October 17, 2014
- Camera-ready: November 7, 2014
- Workshop: November 24/25, 2014


- Valentina Ivanova, Linköping University, Sweden
- Tomi Kauppinen, Aalto University, Finland, and University of Bremen,
- 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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