A Semantics-Based, End-User-Centered Information Visualization Process for Semantic Web Data

Voigt, Martin; Pietschmann, Stefan; Meißner, Klaus

doi:10.1007/978-1-4471-5301-6_5

Cited by 20 publications

(20 citation statements)

References 19 publications

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“…The weighted faceted browsing concept was realized as essential part of the VizBoard InfoVis workbench [21] which relies on the CRUISe mashup platform [14]. Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The WFB backend, implemented using Java and Jersey for the web service communication, handles a series of tasks. At first, it retrieves the Semantic Web data -narrowed in a forgoing pre-selection step [21] -from the data repository ( Fig. 3-1) to construct the indirect data facet ( Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Within our project VizBoard we strive for a semantics-based, end-user-centered InfoVis process [21]. As a first step, we have already developed and validated an algorithm that recommends appropriate visualization components for arbitrary Semantic Web data [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weighted faceted browsing for characteristics-based visualization selection through end users

Voigt

Werstler

Polowinski

et al. 2012

Proceedings of the 4th ACM SIGCHI Symposium on Engineering Interactive Computing Systems

Self Cite

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Faceted browsing is a widely spread, intuitive, and interactive search paradigm for information collections based on the metadata of its items. However, it has the problem that every selected criterion is mandatory so that less important ones may reduce the result set and interesting items may be removed unintentionally. On the other hand, choosing only very few facets yields to an unmanageable set of items wherein the best ones do not become obvious. In this paper, we propose weighted faceted browsing, which seamlessly extends the existing faceted browsing paradigm. Besides basic filtering capabilities, it provides a sophisticated relevance ranking of the result set based on the distinction between mandatory and weighted optional search criteria. Further, we show its practicability within an information visualization workbench to facilitate the end user's search for visualization components based on their characteristics.

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“…The weighted faceted browsing concept was realized as essential part of the VizBoard InfoVis workbench [21] which relies on the CRUISe mashup platform [14]. Fig.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Weighted faceted browsing for characteristics-based visualization selection through end users

Voigt

Werstler

Polowinski

et al. 2012

Proceedings of the 4th ACM SIGCHI Symposium on Engineering Interactive Computing Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Such tools are created mainly for domain experts or analysts [10], who are capable of formulating custom queries to information databases and select appropriate visualization methods. The aim of VizBoard [11] application is to ease the process as much as possible for a non-expert user via exploring the user context. Though such approach eases the task of data extraction and visualization, it cannot be used for illustrating dynamic event-driven context.…”

Section: Related Workmentioning

confidence: 99%

Context capturing in smart space applications

Vasilev

Paramonov²,

Balandin

et al. 2012

NPA

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This paper describes a study of the role and methods for context capturing in smart spaces. The collected context information is captured for further use by pro-active services provided in user-centric service spaces. Another aspect addressed by the paper is representation of the Network Protocols and Algorithms ISSN 1943-3581 2012 www.macrothink.org/npa 85 collected context data in human-readable format. For that purpose we use collaborative mind map editor HiveMind. The developed solution for context management is illustrated by the case study. We apply the proposed method for capturing context information to the smart space application Smart Conference System and use HiveMind as user-friendly context monitoring tool.

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“…The visualization of semantics focuses more on the data and semantic structure to enable a view on the entire or sub-set of the data [11,17,22]. Semantics visualizations for search are rarely presented [25,30], whereas the aspect of visual adaptation is hardly investigated at all. Although semantics enable a more efficient adaptation of visualizations based on both, data and user, the adaptation capabilities of semantics for visualizations were not investigated in depth in previous works.…”

Section: Introductionmentioning

confidence: 99%

Measuring context relevance for adaptive semantics visualizations

Nazemi

Kuijper

Hutter

et al. 2014

Proceedings of the 14th International Conference on Knowledge Technologies and Data-Driven Business

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Semantics visualizations enable the acquisition of information to amplify the acquisition of knowledge. The dramatic increase of semantics in form of Linked Data and Linked- Open Data yield search databases that allow to visualize the entire context of search results. The visualization of this semantic context enables one to gather more information at once, but the complex structures may as well confuse and frustrate users. To overcome the problems, adaptive visualizations already provide some useful methods to adapt the visualization on users' demands and skills. Although these methods are very promising, these systems do not investigate the relevance of semantic neighboring entities that commonly build most information value. We introduce two new measurements for the relevance of neighboring entities: The Inverse Instance Frequency allows weighting the relevance of semantic concepts based on the number of their instances. The Direct Relation Frequency inverse Relations Frequency measures the relevance of neighboring instances by the type of semantic relations. Both measurements pro- vide a weighting of neighboring entities of a selected semantic instance, and enable an adaptation of retinal variables for the visualized graph. The algorithms can easily be integrated into adaptive visualizations and enhance them with the relevance measurement of neighboring semantic entities. We give a detailed description of the algorithms to enable a replication for the adaptive and semantics visualization community. With our method, one can now easily derive the relevance of neighboring semantic entities of selected in-stances, and thus gain more information at once, without confusing and frustrating users

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A Semantics-Based, End-User-Centered Information Visualization Process for Semantic Web Data

Cited by 20 publications

References 19 publications

Weighted faceted browsing for characteristics-based visualization selection through end users

Weighted faceted browsing for characteristics-based visualization selection through end users

Context capturing in smart space applications

Measuring context relevance for adaptive semantics visualizations

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