Interacting with Semantics: A User-Centered Visualization Adaptation Based on Semantics Data

Nazemi, Kawa; Breyer, Matthias; Forster, Jeanette; Burkhardt, Dirk; Kuijper, Arjan

doi:10.1007/978-3-642-21793-7_28

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…Thus, the data bases commonly change in course of time, and a preprocessed analysis of the data base does not provide stable and sustainable information. Therefore, it is reasonable to use an iterative querying approach introduced in [18] for gathering quantitative statements of the data that serve as a basis for determining the data characteristics.…”

Section: Quantitative Analysis Of Datamentioning

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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Section: Quantitative Analysis Of Datamentioning

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

Self Cite

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“…On the same topic, [91] extracts, through semantics modeling, a data context used then to recommend a good visual representation paradigm. The works in [164,165] propose a cockpit for ontology visualization, in order to improve the knowledge space exploration, evaluated on Linked Open Data (LOD). Other approaches to ontology visualization and exploration are presented in [166][167][168].…”

Section: Data Interpretation and Reportingmentioning

confidence: 99%

Big Data Semantics

et al. 2018

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Big Data technology has discarded traditional data modelling approaches as no longer applicable to distributed data processing. It is, however, largely recognised that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g. data analytics and reporting. In this paper, the third of its kind co-authored by mem-

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“…The semantics visualizations are designed to show networks of linked information and also structures (see for instance [18]). As data sources the common LOD data-sources, e.g.…”

Section: Semantics Visualization Of Linked-open Datamentioning

confidence: 99%