Conference Paper

Have Green ¿ A Visual Analytics Framework for Large Semantic Graphs

Pacific Northwest Nat. Lab., Richland, WA
DOI: 10.1109/VAST.2006.261432 Conference: IEEE Symposium On Visual Analytics Science And Technology, IEEE VAST 2006, October 31-November 2, 2006, Baltimore, Maryland, USA
Source: DBLP


A semantic graph is a network of heterogeneous nodes and links annotated with a domain ontology. In intelligence analysis, investigators use semantic graphs to organize concepts and relationships as graph nodes and links in hopes of discovering key trends, patterns, and insights. However, as new information continues to arrive from a multitude of sources, the size and complexity of the semantic graphs will soon overwhelm an investigator's cognitive capacity to carry out significant analyses. We introduce a powerful visual analytics framework designed to enhance investigators' natural analytical capabilities to comprehend and analyze large semantic graphs. The paper describes the overall framework design, presents major development accomplishments to date, and discusses future directions of a new visual analytics system known as Have Green

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Available from: Patrick Mackey
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    • "We discuss their work and align it with our efforts in more detail in Section 11. Many Visual Analytics tools such as [83] [2] [84] [47] [44] [52] have been built over the years, though not applied to semantic web data. However, such tools continue to provide inspiration for Visual Analytics research in the semantic web community. "
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    ABSTRACT: The essence and value of Linked Data lies in the ability of humans and machines to query, access and reason upon highly structured and formalised data. Ontology structures provide an unambiguous description of the structure and content of data. While a multitude of software applications and visualization systems have been developed over the past years for Linked Data, there is still a significant gap that exists between applications that consume Linked Data and interfaces that have been designed with significant focus on aesthetics. Though the importance of aesthetics in affecting the usability, effectiveness and acceptability of user interfaces have long been recognised, little or no explicit attention has been paid to the aesthetics of Linked Data applications. In this paper, we introduce a formalised approach to developing aesthetically pleasing semantic web interfaces by following aesthetic principles and guidelines identified from literature. We apply such principles to design and develop a generic approach of using visualizations to support exploration of Linked Data, in an interface that is pleasing to users. This provides users with means to browse ontology structures, enriched with statistics of the underlying data, facilitating exploratory activities and enabling visual query for highly precise information needs. We evaluated our approach in three ways: an initial objective evaluation comparing our approach with other well-known interfaces for the semantic web and two user evaluations with semantic web researchers.
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    • "Our team is involved in a largescale project that focuses on developing a new generation of systems to support task-based exploratory work of intelligence analysts. While the majority of projects in this area focus on developing visualization systems that help analysts to examine the information space (Acosta-Diaz et al., 2006; Gotz, Zhou, & Aggarwal, 2006; McColgin, Gregory, Hetzler, & Turner, 2006; Wong, Chin, Foote, Mackey, & Thomas, 2006), we focus on artificial intelligence techniques. Among other techniques, our team explores the application of adaptive filtering (AF) (Hanani, Shapira, & Shoval, 2001) to support exploratory searches, especially task-based information exploration. "

    Full-text · Dataset · Jan 2013
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    • "Text analytics is used to extract entities from the text and the relationship between those entities is visualized. The Have Green framework [20] uses an interactive graph visualization to represent concepts and relationships extracted through its analytical capabilities. In Jigsaw [19], multiple coordinated views are used to visualize the connections between entities extracted from a collection of text documents. "
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    ABSTRACT: During visual analysis, users must often connect insights discovered at various points of time. This process is often called ldquoconnecting the dots.rdquo When analysts interactively explore complex datasets over multiple sessions, they may uncover a large number of findings. As a result, it is often difficult for them to recall the past insights, views and concepts that are most relevant to their current line of inquiry. This challenge is even more difficult during collaborative analysis tasks where they need to find connections between their own discoveries and insights found by others. In this paper, we describe a context-based retrieval algorithm to identify notes, views and concepts from users' past analyses that are most relevant to a view or a note based on their line of inquiry. We then describe a related notes recommendation feature that surfaces the most relevant items to the user as they work based on this algorithm. We have implemented this recommendation feature in HARVEST, a Web based visual analytic system. We evaluate the related notes recommendation feature of HARVEST through a case study and discuss the implications of our approach.
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