Evaluating Knowledge Anchors in Data Graphs Against Basic Level Objects

  • Marwan Al-TawilEmail author
  • Vania Dimitrova
  • Dhavalkumar Thakker
  • Alexandra Poulovassilis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)


The growing number of available data graphs in the form of RDF Linked Data enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they interact with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by users who are not domain experts. We propose a new navigation support approach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cognitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two application domains - semantic browsing (Music) and semantic search (Careers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts.


Data graphs Basic level objects Knowledge anchors Usable semantic data exploration 



This research uses outputs from the EU/FP7 project Dicode and the UK/JISC project L4All. We are grateful to Riccardo Frosini and Mirko Dimartino in helping us prepare the L4All dataset used for the experiments in this paper. We thank all the participants in the experimental studies.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marwan Al-Tawil
    • 1
    Email author
  • Vania Dimitrova
    • 1
  • Dhavalkumar Thakker
    • 2
  • Alexandra Poulovassilis
    • 3
  1. 1.School of ComputingUniversity of LeedsLeedsUK
  2. 2.School of Electrical Engineering and Computer ScienceUniversity of BradfordBradfordUK
  3. 3.Knowledge Lab, BirkbeckUniversity of LondonLondonUK

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