| 1. |
- Ivanova, Valentina, 1983-, et al.
(author)
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Alignment Cubes: Towards Interactive Visual Exploration and Evaluation of Multiple Ontology Alignments
- 2017
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In: The Semantic Web – ISWC 2017 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I. - Cham, Switzerland : Springer Publishing Company. - 9783319682877 - 9783319682884 - 9783319682884 ; , s. 400-417
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Conference paper (peer-reviewed)abstract
- Ontology alignment is an area of active research where many algorithms and approaches are being developed. Their performance is usually evaluated by comparing the produced alignments to a reference alignment in terms of precision, recall and F-measure. These measures, however, only provide an overall assessment of the quality of the alignments, but do not reveal differences and commonalities between alignments at a finer-grained level such as, e.g., regions or individual mappings. Furthermore, reference alignments are often unavailable, which makes the comparative exploration of alignments at different levels of granularity even more important. Making such comparisons efficient calls for a “human-in-the-loop” approach, best supported through interactive visual representations of alignments. Our approach extends a recent tool, Matrix Cubes, used for visualizing dense dynamic networks. We first identify use cases for ontology alignment evaluation that can benefit from interactive visualization, and then detail how our Alignment Cubes support interactive exploration of multiple ontology alignments. We demonstrate the usefulness of Alignment Cubes by describing visual exploration scenarios, showing how Alignment Cubes support common tasks identified in the use cases.
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| 2. |
- Hartig, Olaf, 1976-, et al.
(author)
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A Formal Framework for Comparing Linked Data Fragments
- 2017
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In: The Semantic Web - ISWC 2017 - 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part I. - Cham : Springer. - 9783319682877
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Conference paper (peer-reviewed)abstract
- The Linked Data Fragment (LDF) framework has been proposed as a uniform view to explore the trade-offs of consuming Linked Data when servers provide (possibly many) different interfaces to access their data. Every such interface has its own particular properties regarding performance, bandwidth needs, caching, etc. Several practical challenges arise. For example, before exposing a new type of LDFs in some server, can we formally say something about how this new LDF interface compares to other interfaces previously implemented in the same server? From the client side, given a client with some restricted capabilities in terms of time constraints, network connection, or computational power, which is the best type of LDFs to complete a given task? Today there are only a few formal theoretical tools to help answer these and other practical questions, and researchers have embarked in solving them mainly by experimentation.In this paper we propose the Linked Data Fragment Machine (LDFM) which is the first formalization to model LDF scenarios. LDFMs work as classical Turing Machines with extra features that model the server and client capabilities. By proving formal results based on LDFMs, we draw a fairly complete expressiveness lattice that shows the interplay between several combinations of client and server capabilities. We also show the usefulness of our model to formally analyze the fine grain interplay between several metrics such as the number of requests sent to the server, and the bandwidth of communication between client and server.
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