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Cited by 5

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

How and Why is An Answer (Still) Correct? Maintaining Provenance in Dynamic Knowledge Graphs

Gaur, Garima, Bhattacharya, Arnab, Bedathur, Srikanta·2020·Proceedings of the 29th ACM International Conference on Information & Knowledge Management
DOI 10.1145/3340531.3411958
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Abstract

Knowledge graphs (KGs), that have become the backbone of many critical knowledge-centric applications, are mostly automatically constructed based on an ensemble of extraction techniques applied over diverse data sources. It is, therefore, important to establish the provenance of results for a query to determine how these were computed. Provenance is shown to be useful for assigning confidence scores to the results, for debugging the KG generation itself, and for providing answer explanations. In many such applications, certain queries are registered as standing queries since their answers are needed often. However, KGs keep continuously changing due to reasons such as changes in the source data, improvements to the extraction techniques, refinement/enrichment of information, and so on. This raises the issue of efficiently maintaining the provenance polynomials of complex graph pattern queries for dynamic and large KGs instead of having to recompute them from scratch each time the KG is updated. Addressing this issue, we present a framework HUKA that uses provenance polynomials for tracking the derivation of query results over knowledge graphs by encoding the edges involved in generating the answer. More importantly, HUKA also maintains these provenance polynomials in the face of updates---insertions as well as deletions of facts---in the underlying KG. Experimental results over large real-world KGs such as YAGO and DBpedia with various benchmark SPARQL query workloads reveals that HUKA can be almost 50 times faster than existing systems for provenance computation on dynamic KGs.

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Research works printed but not linked: 2

  1. B. Alexe L. Chiticariu and W.-C. Tan. 2006. Spider: a schema mapping debugger. In VLDB. B. Alexe L. Chiticariu and W.-C. Tan. 2006. Spider: a schema mapping debugger. In VLDB.
  2. S. Babu . 2009. Continuous Query . In Encyclopedia of Database Systems, LING LIU and M. TAMER ÖZSU (Eds.). 492--493. S. Babu. 2009. Continuous Query. In Encyclopedia of Database Systems, LING LIU and M. TAMER ÖZSU (Eds.). 492--493.

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Metadata

Authors
Gaur, G., Bhattacharya, A., & Bedathur, S.
Venue
Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Publisher
ACM
Published
2020-10-19
Pages
405-414
DOI
10.1145/3340531.3411958
Cited by
5
References
42
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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