Publications

2014
15.	Kawa Nazemi Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Reprint by Eugraphics Association (EG)). Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Computer Based Learning, Data Analytics, E-Learning, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Ontology visualization, personalization, Policy modeling, reference model, Semantic data modeling, Semantic visualization, Semantic web, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics @phdthesis{Nazemi2014f, title = {Adaptive Semantics Visualization}, author = {Kawa Nazemi}, url = {https://diglib.eg.org/handle/10.2312/12076, EG Lib https://diglib.eg.org/bitstream/handle/10.2312/12076/nazemi.pdf, full text}, doi = {10.2312/12076}, year = {2014}, date = {2014-11-27}, school = {Technische Universität Darmstadt}, abstract = {Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users' behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field. In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation. To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "visualization cockpit". This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system. This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers.}, note = {Reprint by Eugraphics Association (EG)}, keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Computer Based Learning, Data Analytics, E-Learning, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Ontology visualization, personalization, Policy modeling, reference model, Semantic data modeling, Semantic visualization, Semantic web, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics}, pubstate = {published}, tppubtype = {phdthesis} } Close Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users' behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field. In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation. To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "visualization cockpit". This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system. This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers. Close EG Lib full text doi:10.2312/12076 Close
14.	Kawa Nazemi Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Department of Computer Science. Supervised by Dieter W. Fellner.). Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Computer Based Learning, Data Analytics, eGovernance, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction Design, Ontology visualization, personalization, Policy modeling, Semantic data modeling, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics @phdthesis{Nazemi2014g, title = {Adaptive Semantics Visualization}, author = {Kawa Nazemi}, url = {http://tuprints.ulb.tu-darmstadt.de/id/eprint/4319, TU Darmstadt Prints http://tuprints.ulb.tu-darmstadt.de/4319/1/Nazemi_Diss.pdf, full text}, year = {2014}, date = {2014-11-23}, address = {Darmstadt, Germany}, school = {Technische Universität Darmstadt}, abstract = {Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users’ behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field. In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation. To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "‘visualization cockpit"’. This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system. This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers.}, note = {Department of Computer Science. Supervised by Dieter W. Fellner.}, keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Computer Based Learning, Data Analytics, eGovernance, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction Design, Ontology visualization, personalization, Policy modeling, Semantic data modeling, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics}, pubstate = {published}, tppubtype = {phdthesis} } Close Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users’ behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field. In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation. To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "‘visualization cockpit"’. This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system. This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers. Close TU Darmstadt Prints full text Close
13.	Kawa Nazemi; Dirk Burkhardt; Reimond Retz; Arjan Kuijper; Jörn Kohlhammer Adaptive Visualization of Linked-Data Inproceedings In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Ryan McMahan; Jason Jerald; Hui Zhang; Steven M Drucker; Chandra Kambhamettu; Maha El Choubassi; Zhigang Deng; Mark Carlson (Ed.): Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., pp. 872–883, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14364-4. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, reference model, Semantic visualization, Semantic web, User behavior, User modeling, User-centered design, Visual analytics @inproceedings{Nazemi2014b, title = {Adaptive Visualization of Linked-Data}, author = {Kawa Nazemi and Dirk Burkhardt and Reimond Retz and Arjan Kuijper and Jörn Kohlhammer}, editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson}, url = {https://link.springer.com/chapter/10.1007/978-3-319-14364-4_84, Springer link}, doi = {10.1007/978-3-319-14364-4_84}, isbn = {978-3-319-14364-4}, year = {2014}, date = {2014-03-01}, booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.}, pages = {872--883}, publisher = {Springer International Publishing}, address = {Cham}, series = {LNCS 8888}, abstract = {Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.}, keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, reference model, Semantic visualization, Semantic web, User behavior, User modeling, User-centered design, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling. Close Springer link doi:10.1007/978-3-319-14364-4_84 Close
12.	Kawa Nazemi; Dirk Burkhardt; Wilhelm Retz; Jörn Kohlhammer Adaptive Visualization of Social Media Data for Policy Modeling Inproceedings In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Ryan McMahan; Jason Jerald; Hui Zhang; Steven M Drucker; Chandra Kambhamettu; Maha El Choubassi; Zhigang Deng; Mark Carlson (Ed.): Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., pp. 333–344, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14249-4. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics @inproceedings{Nazemi2014g, title = {Adaptive Visualization of Social Media Data for Policy Modeling}, author = {Kawa Nazemi and Dirk Burkhardt and Wilhelm Retz and Jörn Kohlhammer}, editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson}, url = {https://link.springer.com/chapter/10.1007/978-3-319-14249-4_32, Springer link}, doi = {10.1007/978-3-319-14249-4_32}, isbn = {978-3-319-14249-4}, year = {2014}, date = {2014-01-01}, booktitle = {Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.}, pages = {333--344}, publisher = {Springer International Publishing}, address = {Cham}, series = {LNCS 8887}, abstract = {The visual analysis of social media data emerged a huge number of interactive visual representations that use different characteristics of the data to enable the process of information acquisition. The social data are used in the domain of policy modeling to gather information about citizens' demands, opinions, and requirements and help to decide about political policies. Although existing systems already provide a huge number of visual analysis tools, the search and exploration paradigm is not really clear. Furthermore, the systems commonly do not provide any kind of human centered adaptation for the different stakeholders involved in the policy making process. In this paper, we introduce a novel approach that investigates the exploration and search paradigm from two different perspectives and enables a visual adaptation to support the exploration and analysis process.}, keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close The visual analysis of social media data emerged a huge number of interactive visual representations that use different characteristics of the data to enable the process of information acquisition. The social data are used in the domain of policy modeling to gather information about citizens' demands, opinions, and requirements and help to decide about political policies. Although existing systems already provide a huge number of visual analysis tools, the search and exploration paradigm is not really clear. Furthermore, the systems commonly do not provide any kind of human centered adaptation for the different stakeholders involved in the policy making process. In this paper, we introduce a novel approach that investigates the exploration and search paradigm from two different perspectives and enables a visual adaptation to support the exploration and analysis process. Close Springer link doi:10.1007/978-3-319-14249-4_32 Close
11.	Kawa Nazemi; Arjan Kuijper; Marco Hutter; Jörn Kohlhammer; Dieter W. Fellner Measuring Context Relevance for Adaptive Semantics Visualizations Inproceedings In: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business, pp. 14:1–14:8, ACM, Graz, Austria, 2014, ISBN: 978-1-4503-2769-5, (Honourable Mention). Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, data weighting, Information retrieval, semantic processing, Semantic web, Semantics visualization, User Interface, User modeling, Visual analytics @inproceedings{Nazemi:2014:MCR:2637748.2638416, title = {Measuring Context Relevance for Adaptive Semantics Visualizations}, author = {Kawa Nazemi and Arjan Kuijper and Marco Hutter and Jörn Kohlhammer and Dieter W. Fellner}, url = {http://doi.acm.org/10.1145/2637748.2638416, ACM DL}, doi = {10.1145/2637748.2638416}, isbn = {978-1-4503-2769-5}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business}, pages = {14:1--14:8}, publisher = {ACM}, address = {Graz, Austria}, series = {i-KNOW '14}, abstract = {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 provide 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 instances, and thus gain more information at once, without confusing and frustrating users.}, note = {Honourable Mention}, keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, data weighting, Information retrieval, semantic processing, Semantic web, Semantics visualization, User Interface, User modeling, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close 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 provide 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 instances, and thus gain more information at once, without confusing and frustrating users. Close ACM DL doi:10.1145/2637748.2638416 Close
2013
10.	Christian Stab; Dirk Burkhardt; Matthias Breyer; Kawa Nazemi Visualizing Search Results of Linked Open Data Book Chapter In: Tim Hussein; Heiko Paulheim; Stephan Lukosch; Jürgen Ziegler; Ga "e (Ed.): Semantic Models for Adaptive Interactive Systems, pp. 133–149, Springer London, London, 2013, ISBN: 978-1-4471-5301-6. Abstract \| Links \| BibTeX \| Tags: Human-centered user interfaces, Human-computer interaction (HCI), Linked Data, LOD, Semantic visualization, Semantic web, User behavior, User Interactions, User-centered design, Visual analytics @inbook{Stab2013, title = {Visualizing Search Results of Linked Open Data}, author = {Christian Stab and Dirk Burkhardt and Matthias Breyer and Kawa Nazemi}, editor = {Tim Hussein and Heiko Paulheim and Stephan Lukosch and Jürgen Ziegler and Ga "e}, url = {https://link.springer.com/chapter/10.1007/978-1-4471-5301-6_7, Springer link}, doi = {10.1007/978-1-4471-5301-6_7}, isbn = {978-1-4471-5301-6}, year = {2013}, date = {2013-10-01}, booktitle = {Semantic Models for Adaptive Interactive Systems}, pages = {133--149}, publisher = {Springer London}, address = {London}, series = {Human–Computer Interaction Series}, abstract = {Finding accurate information of high quality is still a challenging task particularly with regards to the increasing amount of resources in current information systems. This is especially true if policy decisions that impact humans, economy or environment are based on the demanded information. For improving search result generation and analyzing user queries more and more information retrieval systems utilize Linked Open Data and other semantic knowledge bases. Nevertheless, the semantic information that is used during search result generation mostly remains hidden from the users although it significantly supports users in understanding and assessing search results. The presented approach combines information visualizations with semantic information for offering visual feedback about the reasons the results were retrieved. It visually represents the semantic interpretation and the relation between query terms and search results to offer more transparency in search result generation and allows users to unambiguously assess the relevance of the retrieved resources for their individual search. The approach also supports the common search strategies by providing visual feedback for query refinement and enhancement. Besides the detailed description of the search system, an evaluation of the approach shows that the use of semantic information considerably supports users in assessment and decision-making tasks.}, keywords = {Human-centered user interfaces, Human-computer interaction (HCI), Linked Data, LOD, Semantic visualization, Semantic web, User behavior, User Interactions, User-centered design, Visual analytics}, pubstate = {published}, tppubtype = {inbook} } Close Finding accurate information of high quality is still a challenging task particularly with regards to the increasing amount of resources in current information systems. This is especially true if policy decisions that impact humans, economy or environment are based on the demanded information. For improving search result generation and analyzing user queries more and more information retrieval systems utilize Linked Open Data and other semantic knowledge bases. Nevertheless, the semantic information that is used during search result generation mostly remains hidden from the users although it significantly supports users in understanding and assessing search results. The presented approach combines information visualizations with semantic information for offering visual feedback about the reasons the results were retrieved. It visually represents the semantic interpretation and the relation between query terms and search results to offer more transparency in search result generation and allows users to unambiguously assess the relevance of the retrieved resources for their individual search. The approach also supports the common search strategies by providing visual feedback for query refinement and enhancement. Besides the detailed description of the search system, an evaluation of the approach shows that the use of semantic information considerably supports users in assessment and decision-making tasks. Close Springer link doi:10.1007/978-1-4471-5301-6_7 Close
9.	Dirk Burkhardt; Kawa Nazemi; Peter Sonntagbauer; Susanne Sonntagbauer; Jörn Kohlhammer Interactive Visualizations in the Process of Policy Modelling. Inproceedings In: Maria Wimmer; Marjin Janssen; Ann Macintosh; Hans J Scholl; Efthimios Tambouris (Ed.): Electronic Government and Electronic Participation Joint Proceedings of Ongoing Research of IFIP EGOV and IFIP ePart 2013, pp. 104–115, Gesellschaft für Informatik e.V. (GI), 2013, ISBN: 978-3-88579-615-2. Links \| BibTeX \| Tags: eGovernance, Interaction Design, Policy modeling, Semantic visualization, Semantic web, User Interactions, User-centered design @inproceedings{burkhardt2013interactive, title = {Interactive Visualizations in the Process of Policy Modelling.}, author = {Dirk Burkhardt and Kawa Nazemi and Peter Sonntagbauer and Susanne Sonntagbauer and Jörn Kohlhammer}, editor = {Maria Wimmer and Marjin Janssen and Ann Macintosh and Hans J Scholl and Efthimios Tambouris}, url = {https://subs.emis.de/LNI/Proceedings/Proceedings221/104.pdf, LNI GI- full text https://pdfs.semanticscholar.org/78a3/e0732eabaeb7c84b50a28a70bcddde40f562.pdf, Semantic scholars - full text}, isbn = {978-3-88579-615-2}, year = {2013}, date = {2013-01-01}, booktitle = {Electronic Government and Electronic Participation Joint Proceedings of Ongoing Research of IFIP EGOV and IFIP ePart 2013}, pages = {104--115}, publisher = {Gesellschaft für Informatik e.V. (GI)}, keywords = {eGovernance, Interaction Design, Policy modeling, Semantic visualization, Semantic web, User Interactions, User-centered design}, pubstate = {published}, tppubtype = {inproceedings} } Close LNI GI- full text Semantic scholars - full text Close
8.	Kawa Nazemi; Reimond Retz; Jürgen Bernard; Jörn Kohlhammer; Dieter W. Fellner Adaptive Semantic Visualization for Bibliographic Entries Inproceedings In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Baoxin Li; Fatih Porikli; Victor Zordan; James Klosowski; Sabine Coquillart; Xun Luo; Min Chen; David Gotz (Ed.): Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing., pp. 13–24, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, ISBN: 978-3-642-41939-3. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, Visual analytics @inproceedings{Nazemi2013b, title = {Adaptive Semantic Visualization for Bibliographic Entries}, author = {Kawa Nazemi and Reimond Retz and Jürgen Bernard and Jörn Kohlhammer and Dieter W. Fellner}, editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Baoxin Li and Fatih Porikli and Victor Zordan and James Klosowski and Sabine Coquillart and Xun Luo and Min Chen and David Gotz}, url = {https://link.springer.com/chapter/10.1007/978-3-642-41939-3_2, Springer link}, doi = {10.1007/978-3-642-41939-3_2}, isbn = {978-3-642-41939-3}, year = {2013}, date = {2013-01-01}, booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing.}, pages = {13--24}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, series = {LNCS 8034}, abstract = {Adaptive visualizations aim to reduce the complexity of visual representations and convey information using interactive visualizations. Although the research on adaptive visualizations grew in the last years, the existing approaches do not make use of the variety of adaptable visual variables. Further the existing approaches often premises experts, who has to model the initial visualization design. In addition, current approaches either incorporate user behavior or data types. A combination of both is not proposed to our knowledge. This paper introduces the instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on visual layout and visual presentation in a multiple visualization environment. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonic requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.}, keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Adaptive visualizations aim to reduce the complexity of visual representations and convey information using interactive visualizations. Although the research on adaptive visualizations grew in the last years, the existing approaches do not make use of the variety of adaptable visual variables. Further the existing approaches often premises experts, who has to model the initial visualization design. In addition, current approaches either incorporate user behavior or data types. A combination of both is not proposed to our knowledge. This paper introduces the instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on visual layout and visual presentation in a multiple visualization environment. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonic requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling. Close Springer link doi:10.1007/978-3-642-41939-3_2 Close
2012
7.	Dirk Burkhardt; Christian Stab; Martin Steiger; Matthias Breyer; Kawa Nazemi Interactive Exploration System: A User-Centered Interaction Approach in Semantics Visualizations Inproceedings In: Arjan Kuijper; Alexei Sourin (Ed.): Proceedings of the 2012 International Conference on Cyberworlds (Cyberworlds 2012), pp. 261–267, IEEE, Piscataway, NJ, USA, 2012, ISBN: 978-0-7695-4814-2. Abstract \| Links \| BibTeX \| Tags: Multimodal Interaction, Semantic web, Semantics visualization, User-Centered Systems @inproceedings{Burkhardt2012c, title = {Interactive Exploration System: A User-Centered Interaction Approach in Semantics Visualizations}, author = {Dirk Burkhardt and Christian Stab and Martin Steiger and Matthias Breyer and Kawa Nazemi}, editor = {Arjan Kuijper and Alexei Sourin}, doi = {10.1109/CW.2012.45}, isbn = {978-0-7695-4814-2}, year = {2012}, date = {2012-09-25}, booktitle = {Proceedings of the 2012 International Conference on Cyberworlds (Cyberworlds 2012)}, pages = {261--267}, publisher = {IEEE}, address = {Piscataway, NJ, USA}, abstract = {Nowadays a wide range of input devices are available to users of technical systems. Especially modern alternative interaction devices, which are known from game consoles etc., provide a more natural way of interaction. In parallel to that the research on visualization of large amount of data advances very quickly. This research was also influenced by the semantic web and the idea of storing data in a structured and linked form. The semantically annotated data gains more and more importance in information acquisition processes. Especially the Linked Open Data (LOD) format already experienced a huge growth. However, the user-interfaces of web-applications mostly do not reflect the added value of semantics data. This paper describes the conceptual design and implementation of an Interactive Exploration System that offers a user-centered graphical environment of web-based knowledge repositories, to support and optimize explorative learning, and the integration of a taxonomy-based approach to enable the use of more natural interaction metaphors, as they are possible with modern devices like Wii Mote or Microsoft Kinect. Therefore we introduce a different classification for interaction devices, and current approaches for supporting the added values in semantics visualizations. Furthermore, we describe the concept of our IES, including a strategy to organize and structure today's existing input devices, and a semantics exploration system driven by user-experience. We conclude the paper with a description of the implementation of the IES and an application scenario.}, keywords = {Multimodal Interaction, Semantic web, Semantics visualization, User-Centered Systems}, pubstate = {published}, tppubtype = {inproceedings} } Close Nowadays a wide range of input devices are available to users of technical systems. Especially modern alternative interaction devices, which are known from game consoles etc., provide a more natural way of interaction. In parallel to that the research on visualization of large amount of data advances very quickly. This research was also influenced by the semantic web and the idea of storing data in a structured and linked form. The semantically annotated data gains more and more importance in information acquisition processes. Especially the Linked Open Data (LOD) format already experienced a huge growth. However, the user-interfaces of web-applications mostly do not reflect the added value of semantics data. This paper describes the conceptual design and implementation of an Interactive Exploration System that offers a user-centered graphical environment of web-based knowledge repositories, to support and optimize explorative learning, and the integration of a taxonomy-based approach to enable the use of more natural interaction metaphors, as they are possible with modern devices like Wii Mote or Microsoft Kinect. Therefore we introduce a different classification for interaction devices, and current approaches for supporting the added values in semantics visualizations. Furthermore, we describe the concept of our IES, including a strategy to organize and structure today's existing input devices, and a semantics exploration system driven by user-experience. We conclude the paper with a description of the implementation of the IES and an application scenario. Close doi:10.1109/CW.2012.45 Close
2011
6.	Dirk Burkhardt; Matthias Breyer; Kawa Nazemi; Arjan Kuijper Search Intention Analysis for User-Centered Adaptive Visualizations Conference Universal Access in Human-Computer Interaction. Design for All and eInclusion. UAHCI 2011., LNCS 6765 Springer, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21671-8. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Search result visualization, Semantic visualization, Semantic web, User behavior, User centered modeling @conference{C35-P-22190, title = {Search Intention Analysis for User-Centered Adaptive Visualizations}, author = {Dirk Burkhardt and Matthias Breyer and Kawa Nazemi and Arjan Kuijper}, editor = {C Stephanidis}, url = {https://link.springer.com/chapter/10.1007/978-3-642-21672-5_35, Springer link}, doi = {10.1007/978-3-642-21672-5_35}, isbn = {978-3-642-21671-8}, year = {2011}, date = {2011-01-01}, booktitle = {Universal Access in Human-Computer Interaction. Design for All and eInclusion. UAHCI 2011.}, pages = {317-326}, publisher = {Springer, Berlin, Heidelberg}, series = {LNCS 6765}, abstract = {Searching information on web turned to a matter of course in the last years. The visualization and filtering of the results of such search queries plays a key-role in different disciplines and is still today under research. In this paper a new approach for classifying the search intention of users' is presented. The approach uses existing and easy parameters for a differentiation between explorative and targeted search. The results of the classification are used for a differentiated presentation based on graphical visualization techniques.}, keywords = {Adaptive Information Visualization, Search result visualization, Semantic visualization, Semantic web, User behavior, User centered modeling}, pubstate = {published}, tppubtype = {conference} } Close Searching information on web turned to a matter of course in the last years. The visualization and filtering of the results of such search queries plays a key-role in different disciplines and is still today under research. In this paper a new approach for classifying the search intention of users' is presented. The approach uses existing and easy parameters for a differentiation between explorative and targeted search. The results of the classification are used for a differentiated presentation based on graphical visualization techniques. Close Springer link doi:10.1007/978-3-642-21672-5_35 Close
5.	Kawa Nazemi; Dirk Burkhardt; Alexander Praetorius; Matthias Breyer; Arjan Kuijper Adapting User Interfaces by Analyzing Data Characteristics for Determining Adequate Visualizations Inproceedings In: Masaaki Kurosu (Ed.): Human Centered Design, pp. 566–575, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21753-1. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, personalization, reference model, Semantic visualization, Semantic web, User behavior @inproceedings{Nazemi2011c, title = {Adapting User Interfaces by Analyzing Data Characteristics for Determining Adequate Visualizations}, author = {Kawa Nazemi and Dirk Burkhardt and Alexander Praetorius and Matthias Breyer and Arjan Kuijper}, editor = {Masaaki Kurosu}, url = {https://doi.org/10.1007/978-3-642-21753-1_63, DOI https://link.springer.com/chapter/10.1007/978-3-642-21753-1_63, Springer page}, doi = {10.1007/978-3-642-21753-1_63}, isbn = {978-3-642-21753-1}, year = {2011}, date = {2011-01-01}, booktitle = {Human Centered Design}, pages = {566--575}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, abstract = {Today the information visualization takes in an important position, because it is required in nearly every context where large databases have to be visualized. For this challenge new approaches are needed to allow the user an adequate access to these data. Static visualizations are only able to show the data without any support to the users, which is the reason for the accomplished researches to adaptive user-interfaces, in particular for adaptive visualizations. By these approaches the visualizations were adapted to the users' behavior, so that graphical primitives were change to support a user e.g. by highlighting user-specific entities, which seems relevant for a user. This approach is commonly used, but it is limited on changes for just a single visualization. Modern heterogeneous data providing different kinds of aspects, which modern visualizations try to regard, but therefore a user often needs more than a single visualization for making an information retrieval. In this paper we describe a concept for adapting the user-interface by selecting visualizations in dependence to automatically generated data characteristics. So visualizations will be chosen, which are fitting well to the generated characteristics. Finally the user gets an aquatically arranged set of visualizations as initial point of his interaction through the data.}, keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, personalization, reference model, Semantic visualization, Semantic web, User behavior}, pubstate = {published}, tppubtype = {inproceedings} } Close Today the information visualization takes in an important position, because it is required in nearly every context where large databases have to be visualized. For this challenge new approaches are needed to allow the user an adequate access to these data. Static visualizations are only able to show the data without any support to the users, which is the reason for the accomplished researches to adaptive user-interfaces, in particular for adaptive visualizations. By these approaches the visualizations were adapted to the users' behavior, so that graphical primitives were change to support a user e.g. by highlighting user-specific entities, which seems relevant for a user. This approach is commonly used, but it is limited on changes for just a single visualization. Modern heterogeneous data providing different kinds of aspects, which modern visualizations try to regard, but therefore a user often needs more than a single visualization for making an information retrieval. In this paper we describe a concept for adapting the user-interface by selecting visualizations in dependence to automatically generated data characteristics. So visualizations will be chosen, which are fitting well to the generated characteristics. Finally the user gets an aquatically arranged set of visualizations as initial point of his interaction through the data. Close DOI Springer page doi:10.1007/978-3-642-21753-1_63 Close
4.	Christian Stab; Kawa Nazemi; Matthias Breyer; Dirk Burkhardt; Arjan Kuijper Interacting with Semantics and Time Inproceedings In: Julie A Jacko (Ed.): Human-Computer Interaction. Users and Applications. Proceedings of HCI International 2011, pp. 520–529, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21619-0. Abstract \| Links \| BibTeX \| Tags: Data Analytics, Human Factors, Human-centered user interfaces, Information visualization, Ontology visualization, Semantic visualization, Semantic web, Temporal Visualization, User behavior @inproceedings{Stab2011, title = {Interacting with Semantics and Time}, author = {Christian Stab and Kawa Nazemi and Matthias Breyer and Dirk Burkhardt and Arjan Kuijper}, editor = {Julie A Jacko}, url = {https://link.springer.com/chapter/10.1007/978-3-642-21619-0_64, Springer link}, doi = {10.1007/978-3-642-21619-0_64}, isbn = {978-3-642-21619-0}, year = {2011}, date = {2011-01-01}, booktitle = {Human-Computer Interaction. Users and Applications. Proceedings of HCI International 2011}, volume = {4}, pages = {520--529}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, series = {LNCS 6764}, abstract = {Time appears in many different semantic information systems like historical databases, multimedia systems or social communities as a common attribute. Beside the temporal information, the resources in these domains are categorized in a domain-specific schema and interconnected by semantic relations. Nevertheless, the high potential of these systems is not yet exhausted completely. Even today most of these knowledge systems present time-dependent semantic knowledge in textual form, what makes it difficult for the average user to understand temporal structures and dependencies. For bridging this gap between human and computer and for simplifying the exploration of time-dependent semantic knowledge, we developed a new interactive timeline visualization called SemaTime. The new designed temporal navigation concept offers an intuitive way for exploring and filtering time-depended resources. Additionally SemaTime offers navigation and visual filtering methods on the conceptual layer of the domain and is able to depict semantic relations. In this paper we describe the conceptual design of SemaTime and illustrate its application potentials in semantic search environments.}, keywords = {Data Analytics, Human Factors, Human-centered user interfaces, Information visualization, Ontology visualization, Semantic visualization, Semantic web, Temporal Visualization, User behavior}, pubstate = {published}, tppubtype = {inproceedings} } Close Time appears in many different semantic information systems like historical databases, multimedia systems or social communities as a common attribute. Beside the temporal information, the resources in these domains are categorized in a domain-specific schema and interconnected by semantic relations. Nevertheless, the high potential of these systems is not yet exhausted completely. Even today most of these knowledge systems present time-dependent semantic knowledge in textual form, what makes it difficult for the average user to understand temporal structures and dependencies. For bridging this gap between human and computer and for simplifying the exploration of time-dependent semantic knowledge, we developed a new interactive timeline visualization called SemaTime. The new designed temporal navigation concept offers an intuitive way for exploring and filtering time-depended resources. Additionally SemaTime offers navigation and visual filtering methods on the conceptual layer of the domain and is able to depict semantic relations. In this paper we describe the conceptual design of SemaTime and illustrate its application potentials in semantic search environments. Close Springer link doi:10.1007/978-3-642-21619-0_64 Close
3.	Kawa Nazemi; Matthias Breyer; Jeanette Forster; Dirk Burkhardt; Arjan Kuijper Interacting with Semantics: A User-Centered Visualization Adaptation Based on Semantics Data Inproceedings In: Michael J Smith; Gavriel Salvendy (Ed.): Human Interface and the Management of Information. Interacting with Information. Symposium on Human Interface 2011., pp. 239–248, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21793-7. Abstract \| Links \| BibTeX \| Tags: Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Semantic visualization, Semantic web, User behavior, User Interactions @inproceedings{Nazemi2011cb, title = {Interacting with Semantics: A User-Centered Visualization Adaptation Based on Semantics Data}, author = {Kawa Nazemi and Matthias Breyer and Jeanette Forster and Dirk Burkhardt and Arjan Kuijper}, editor = {Michael J Smith and Gavriel Salvendy}, url = {https://link.springer.com/chapter/10.1007/978-3-642-21793-7_28, Springer link}, doi = {10.1007/978-3-642-21793-7_28}, isbn = {978-3-642-21793-7}, year = {2011}, date = {2011-01-01}, booktitle = {Human Interface and the Management of Information. Interacting with Information. Symposium on Human Interface 2011.}, pages = {239--248}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, series = {LNCS 6771}, abstract = {Semantically annotated data gain more and more importance in future information acquiring processes. Especially the Linked Open Data (LOD) format has already experienced a great growth. However, the user-interfaces of web-applications mostly do not reflect the added value of semantics data. The following paper describes a new approach of user-centered data-adaptive semantics visualization, which makes use of the advantages of semantics data combined with an adaptive composition of information visualization techniques. It starts with a related work section, where existing LOD systems and information visualization techniques are described. After that, the new approach will bridge the gap between semantically annotated data (LOD) and information visualization and introduces a visualization system that adapts the composition of visualizations based on the underlying data structure. A case study of an example case will conclude this paper.}, keywords = {Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Semantic visualization, Semantic web, User behavior, User Interactions}, pubstate = {published}, tppubtype = {inproceedings} } Close Semantically annotated data gain more and more importance in future information acquiring processes. Especially the Linked Open Data (LOD) format has already experienced a great growth. However, the user-interfaces of web-applications mostly do not reflect the added value of semantics data. The following paper describes a new approach of user-centered data-adaptive semantics visualization, which makes use of the advantages of semantics data combined with an adaptive composition of information visualization techniques. It starts with a related work section, where existing LOD systems and information visualization techniques are described. After that, the new approach will bridge the gap between semantically annotated data (LOD) and information visualization and introduces a visualization system that adapts the composition of visualizations based on the underlying data structure. A case study of an example case will conclude this paper. Close Springer link doi:10.1007/978-3-642-21793-7_28 Close
2010
2.	Kawa Nazemi; Dirk Burkhardt; Matthias Breyer; Christian Stab; Dieter W. Fellner Semantic Visualization Cockpit: Adaptable Composition of Semantics-Visualization Techniques for Knowledge-Exploration Conference International Conference on Interactive Collaborative Learning (ICL), Kassel University Press, 2010, 2010. Abstract \| Links \| BibTeX \| Tags: Exploratory learning, Intelligent user interfaces, Semantic visualization, Semantic web @conference{C35-P-21605, title = {Semantic Visualization Cockpit: Adaptable Composition of Semantics-Visualization Techniques for Knowledge-Exploration}, author = {Kawa Nazemi and Dirk Burkhardt and Matthias Breyer and Christian Stab and Dieter W. Fellner}, url = {https://pdfs.semanticscholar.org/46d4/1ca0256a1024fd32f5f37280a359f60ae2f1.pdf, Full text https://www.semanticscholar.org/paper/Semantic-Visualization-Cockpit%3A-Adaptable-Composit-Nazemi-Burkhardt/46d41ca0256a1024fd32f5f37280a359f60ae2f1, Semantic Scholars}, year = {2010}, date = {2010-01-01}, booktitle = {International Conference on Interactive Collaborative Learning (ICL)}, pages = {163-173}, publisher = {Kassel University Press, 2010}, abstract = {Semantic-Web and ontology-based information processing systems are established technologies and techniques, in more than only research areas and institutions. Different worldwide projects and enterprise companies identified already the added value of semantic technologies and work on different sub-topics for gathering and conveying knowledge. As the process of gathering and structuring semantic information plays a key role in the most developed applications, the process of transferring and adopting knowledge to and by humans is neglected, although the complex structure of knowledge-design opens many research-questions. The following paper describes a new approach for visualizing semantic information as a composition of different adaptable ontology-visualization techniques. We start with a categorized description of existing ontology visualization techniques and show potential gaps. After that the new approach will be described and its added value to existing systems. A case study within the greatest German program for semantic information processing will show the usage of the system in real scenarios.}, keywords = {Exploratory learning, Intelligent user interfaces, Semantic visualization, Semantic web}, pubstate = {published}, tppubtype = {conference} } Close Semantic-Web and ontology-based information processing systems are established technologies and techniques, in more than only research areas and institutions. Different worldwide projects and enterprise companies identified already the added value of semantic technologies and work on different sub-topics for gathering and conveying knowledge. As the process of gathering and structuring semantic information plays a key role in the most developed applications, the process of transferring and adopting knowledge to and by humans is neglected, although the complex structure of knowledge-design opens many research-questions. The following paper describes a new approach for visualizing semantic information as a composition of different adaptable ontology-visualization techniques. We start with a categorized description of existing ontology visualization techniques and show potential gaps. After that the new approach will be described and its added value to existing systems. A case study within the greatest German program for semantic information processing will show the usage of the system in real scenarios. Close Full text Semantic Scholars Close
1.	Dirk Burkhardt; Cristian Erik Hofmann; Kawa Nazemi; Christian Stab; Matthias Breyer; Dieter W. Fellner Intuitive Semantic-Editing for Regarding Needs of Domain-Experts Conference Proceedings of ED-Media 2010, The Association for the Advancement of Computing in Education (AACE), 2010, ISBN: 978-1-880094-81-5. Abstract \| Links \| BibTeX \| Tags: Graphical interactive user interfaces, Intelligent user interfaces, Semantic data modeling, Semantic visualization, Semantic web @conference{C35-P-21399, title = {Intuitive Semantic-Editing for Regarding Needs of Domain-Experts}, author = {Dirk Burkhardt and Cristian Erik Hofmann and Kawa Nazemi and Christian Stab and Matthias Breyer and Dieter W. Fellner}, url = {http://www.learntechlib.org/p/34735/, link to paper}, isbn = {978-1-880094-81-5}, year = {2010}, date = {2010-01-01}, booktitle = {Proceedings of ED-Media 2010}, pages = {860-869}, publisher = {The Association for the Advancement of Computing in Education (AACE)}, abstract = {Ontologies are used to represent knowledge and their semantic information from different topics, to allow users a better way to explore knowledge and find information faster, because of the data-structuring. To achieve a well filled knowledgebase, editors have to be used, to enter new and to edit existing information. But most of the existing ontology-editors are designed for experienced ontology-experts. Experts from other topic fields e.g. physicians are often novices in the area of ontology-creating, they need adequate tools, which hide the complexity of ontology-structures. In the area of e-learning experts are also teachers as well. In this paper we will present a method, how the needs of domain-experts can be regarded and so an editor can designed, which allows an editing and adding of information by users without having experiences of creating ontologies. With such an editor domain-experts are able to commit their expert-knowledge into the ontology.}, keywords = {Graphical interactive user interfaces, Intelligent user interfaces, Semantic data modeling, Semantic visualization, Semantic web}, pubstate = {published}, tppubtype = {conference} } Close Ontologies are used to represent knowledge and their semantic information from different topics, to allow users a better way to explore knowledge and find information faster, because of the data-structuring. To achieve a well filled knowledgebase, editors have to be used, to enter new and to edit existing information. But most of the existing ontology-editors are designed for experienced ontology-experts. Experts from other topic fields e.g. physicians are often novices in the area of ontology-creating, they need adequate tools, which hide the complexity of ontology-structures. In the area of e-learning experts are also teachers as well. In this paper we will present a method, how the needs of domain-experts can be regarded and so an editor can designed, which allows an editing and adding of information by users without having experiences of creating ontologies. With such an editor domain-experts are able to commit their expert-knowledge into the ontology. Close link to paper Close

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