Vedran Sabol

Ontology Alignment—A Survey with Focus on Visually Supported Semi-Automatic Techniques

Semantic technologies are of paramount importance to the future Internet. The reuse and integration of semantically described resources, such as data or services, necessitates the bringing of ontologies into mutual agreement. Ontology alignment deals with the discovery of correspondences between concepts and relations from different ontologies. Alignment provides the key ingredient to semantic interoperability. This paper gives an overview on the state of the art in the field of visually supported semi-automatic alignment techniques and presents recent trends and developments. Particular attention is given to user interfaces and visualization techniques supporting involvement of humans in the alignment process. We derive and summarize requirements for visual semi-automatic alignment systems, provide an overview of existing approaches, and discuss the possibilities for further improvements and future research.

Evaluating a System for Interactive Exploration of Large, Hierarchically Structured Document Repositories

The InfoSky visual explorer is a system enabling users to interactively explore large, hierarchically structured document collections. Similar to a real-world telescope, InfoSky employs a planar graphical representation with variable magnification. Documents of similar content are placed close to each other and displayed as stars, while collections of documents at a particular level in the hierarchy are visualised as bounding polygons. Usability testing of an early prototype implementation of InfoSky revealed several design issues which prevented users from fully exploiting the power of the visual metaphor. Evaluation results have been incorporated into an advanced prototype, and another usability test has been conducted. A comparison of test results demonstrates enhanced system performance and points out promising directions for further work

Search result visualisation with xFIND

The xFIND gatherer-broker architecture provides a wealth of metadata, which can be used to provide sophisticated search functionality. Local or remote documents are indexed and summaries and metadata are stored on an xFIND broker (server). An xFIND client can search a particular broker and access rich metadata for search result presentation, without having to fetch the original documents themselves. Search result sets are not only presented as a traditional ranked list, but also in an interactive scatterplot (Search Result Explorer) and using dynamic thematic clustering (VisIslands).

What is Knowledge Visualization? Perspectives on an Emerging Discipline

This paper collates eight expert opinions about Knowledge Visualization, what it is and what it should be. An average of 581 words long, topics span from representation, storytelling and criticizing the lack of theory, to communication, analytics for the masses and reasoning, to trendy Visual Thinking and creativity beyond PowerPoint. These individual views provide a picture of the present and the future of a discipline that could not be more timely, aiming for a common understanding of the visualization of knowledge.

Visual Knowledge Discovery in Dynamic Enterprise Text Repositories

Knowledge discovery involves data driven processes where data is transformed and processed by various algorithms to identify new knowledge. KnowMiner is a service oriented framework providing a rich set of knowledge discovery functionalities with focus on text data sets. Complementing results of automatic machine analysis with the immense processing power of human visual apparatus has the potential of significantly improving the process of acquiring new knowledge. VisTools is a lightweight visual analytics framework based on multiple coordinated views (MCV) paradigm designed for deployment atop the KnowMiner’s service architecture. In this paper we briefly present both frameworks and, driven by real-world customer requirements, describe how visual techniques can be synergistically combined with machine processing for effective analysis of dynamically changing, metadata-rich text documents sets.

Applications of a Lightweight, Web-Based Retrieval, Clustering, and Visualisation Framework

Todays web search engines return very large result sets for query formulations consisting of few specific keywords. Results are presented as ranked lists containing textual description of found items. Such representations do not allow identification of topical clusters, and consequentially make it difficult for users to refine queries efficiently.In this paper, we present WebRat, a framework for web-based retrieval, clustering and visualisation which enables parallel querying of multiple search engines, merging of retrieved result sets, automatic identification of topical clusters and interactive visualisation of the result sets and clusters for query refinement. This framework is lightweight in the sense that it consists of a small, platform-independent component which can be easily integrated into exisiting Internet or Intranet search forms without requiring specific system environments, server resources or precalculation efforts.The WebRat system extends existing approaches to web search result visualisation in many aspects: Found results are added incrementally as they arrive, labelling is performed in 2-dimensional space on clusters the user can see and rendering is optimised to provide sufficient performance on standard office machines.The WebRat framework has been used to implement a variety of applications: We have provided enhanced web search capabilities for users doing scientific research. Overview and refinement capabilities have been implemented for the environmental domain. Finally, abstracts generated on the fly by a knowledge management system have been used to provide topical navigation capabilities to developers searching for technical information in mailing list archives.

WebRat: supporting agile knowledge retrieval through dynamic, incremental clustering and automatic labelling of Web search result sets

WebRat is an interactive system for visualising and refining and refining search result sets. Documents matching a query are dynamically clustered on the fly and visualised as a contour map of islands. Thematic clusters are built, analysed, and visualised in real time. Users can interactively explore the visualisation and refine queries by selecting from the keywords and clusters presented to them. WebRat does not rely on precalculated meta data. Instead, necessary information is directly extracted from query result representations provided by search engines, as for example ranked lists of document snippets. The system is language-independent and can easily be adapted to a number of data sources and visualisation modes. WebRat supports agile knowledge retrieval by transforming unstructured information input into a representation enriched with structure and meta information even when environmental conditions and user demands change frequently and rapidly.

Discovery and Visual Analysis of Linked Data for Humans

Linked Data has grown to become one of the largest available knowledge bases. Unfortunately, this wealth of data remains inaccessible to those without in-depth knowledge of semantic technologies. We describe a toolchain enabling users without semantic technology background to explore and visually analyse Linked Data. We demonstrate its applicability in scenarios involving data from the Linked Open Data Cloud, and research data extracted from scientific publications. Our focus is on the Web-based front-end consisting of querying and visualisation tools. The performed usability evaluations unveil mainly positive results confirming that the Query Wizard simplifies searching, refining and transforming Linked Data and, in particular, that people using the Visualisation Wizard quickly learn to perform interactive analysis tasks on the resulting Linked Data sets. In making Linked Data analysis effectively accessible to the general public, our tool has been integrated in a number of live services where people use it to analyse, discover and discuss facts with Linked Data.

Unleashing Semantics of Research Data

Research depends to a large degree on the availability and quality of primary research data, i.e., data generated through experiments and evaluations. While the Web in general and Linked Data in particular provide a platform and the necessary technologies for sharing, managing and utilizing research data, an ecosystem supporting those tasks is still missing. The vision of the CODE project is the establishment of a sophisticated ecosystem for Linked Data. Here, the extraction of knowledge encapsulated in scientific research paper along with its public release as Linked Data serves as the major use case. Further, Visual Analytics approaches empower end users to analyse, integrate and organize data. During these tasks, specific Big Data issues are present.

Scalable Recursive Top-Down Hierarchical Clustering Approach with Implicit Model Selection for Textual Data Sets

Automatic generation of taxonomies can be useful for a wide area of applications. In our application scenario a topical hierarchy should be constructed reasonably fast from a large document collection to aid browsing of the data set. The hierarchy should also be used by the InfoSky projection algorithm to create an information landscape visualization suitable for explorative navigation of the data. We developed an algorithm that applies a scalable, recursive, top-down clustering approach to generate a dynamic concept hierarchy. The algorithm recursively applies a workflow consisting of preprocessing, clustering, cluster labeling and projection into 2D space. Besides presenting and discussing the benefits of combining hierarchy browsing with visual exploration, we also investigate the clustering results achieved on a real world data set.