Name
Abstract | ||
|---|---|---|
Knowledge graphs (KGs) are essential resources for many applications including Web search and question answering. As KGs are often automatically constructed, they may contain incorrect facts. Detecting them is a crucial, yet extremely expensive task. Prominent solutions detect and explain inconsistency in KGs with respect to accompanying ontologies that describe the KG domain of interest. Compared to machine learning methods they are more reliable and human-interpretable but scale poorly on large KGs. In this paper, we present a novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns. Though much smaller, KG abstractions preserve inconsistency and their explanations. Our experiments with large KGs (e.g., DBpedia and Yago) demonstrate the feasibility of our approach and show that it significantly outperforms the popular baseline.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3366423.3380014 | WWW '20: The Web Conference 2020
Taipei
Taiwan
April, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7023-3 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Trung-Kien Tran | 1 | 4 | 2.74 |
| Mohamed H. Gad-Elrab | 2 | 6 | 1.15 |
| Daria Stepanova | 3 | 46 | 12.10 |
| Evgeny Kharlamov | 4 | 791 | 68.18 |
| Jannik Strötgen | 5 | 492 | 38.20 |