Paper Info
Title
Profiling the Potential of Web Tables for Augmenting Cross-domain Knowledge Bases.
Abstract
Cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph have gained increasing attention over the last years and are starting to be deployed within various use cases. However, the content of such knowledge bases is far from being complete, far from always being correct, and suffers from deprecation (i.e. population numbers become outdated after some time). Hence, there are efforts to leverage various types of Web data to complement, update and extend such knowledge bases. A source of Web data that potentially provides a very wide coverage are millions of relational HTML tables that are found on the Web. The existing work on using data from Web tables to augment cross-domain knowledge bases reports only aggregated performance numbers. The actual content of the Web tables and the topical areas of the knowledge bases that can be complemented using the tables remain unclear. In this paper, we match a large, publicly available Web table corpus to the DBpedia knowledge base. Based on the matching results, we profile the potential of Web tables for augmenting different parts of cross-domain knowledge bases and report detailed statistics about classes, properties, and instances for which missing values can be filled using Web table data as evidence. In order to estimate the potential quality of the new values, we empirically examine the Local Closed World Assumption and use it to determine the maximal number of correct facts that an ideal data fusion strategy could generate. Using this as ground truth, we compare three data fusion strategies and conclude that knowledge-based trust outperforms PageRank- and voting-based fusion.
Year
DOI
Venue
2016
10.1145/2872427.2883017
WWW
Keywords
Field
DocType
web tables, data profiling, knowledge base augmentation, slot filling, schema and data matching, data fusion
Data mining,Population,Computer science,Data profiling,Artificial intelligence,Knowledge base,Closed-world assumption,PageRank,World Wide Web,Domain knowledge,Web mapping,Data Web,Machine learning
Conference
Citations 
PageRank 
References 
19
0.71
24
Authors
4
Name
Order
Citations
PageRank
Dominique Ritze127418.58
Oliver Lehmberg21799.59
Yaser Oulabi3191.38
Christian Bizer48448524.93