Attract me!: how could end-users identify interesting resources?

ABSTRACT

Recently, the interest in semantic web technologies increased in various domains, e.g., software engineering or biology. Since this technologies address the long tail of information domains with missing content types, the number of linked datasets will grow even more rapidly. Tech-savvy users and scientists benefit from this trend as they have the knowledge to created complex queries, and thus, to retrieve interesting subsets and answers. However, end-users have difficulties to understand the data's paradigm and need appropriate tool support to slice and dice the data to understandable parts or particular resources. In this paper, we propose a novel approach to enable end-users to browse huge semantic datasets, to detect, and to select interesting resources according to their specific tasks. Based on our evaluation results of two user studies using a web-based prototype we explain, which visualization and interaction techniques in combination with automatic filters are well-suited for novices.

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Index Terms

• Human-centered computing

  • Human computer interaction (HCI)

• Information systems

  • Information retrieval