Estimating Missing Temporal Meta-Information using Knowledge-Based-Trust.

A large number of HTML Tables on the Web contain relational data which can be used to augment knowledge bases such as DB-pedia, Yago, or Wikidata. A large part of this data is time-dependent, i.e., the correctness of a fact depends on a specific temporal scope. In order to use this data for knowledge base augmentation, we need temporal meta-information. Existing methods rely on timestamps within the table itself or its context as temporal meta-information. Yet, the relationship between these timestamps and data within a table is often unclear. Additionally, timestamps are rather sparse, and there are many web tables for which no timestamps exist. Knowledge-Based-Trust (KBT) uses the overlap with ground-truth to estimate the trustworthiness of a dataset. This paper introduces Timed-KBT, which overcomes the dependence on sparse and possibly misinterpreted timestamps by propagating temporal meta-information from a knowledge base to web table data using KBT. It also derives a trust score that estimates the correctness of the data and the assigned temporal meta-information. We evaluate Timed-KBT on the use case of fusing data from a large corpus of web tables for filling missing facts in a knowledge base. Our evaluation shows that Timed-KBT yields an increase in F-0.25-Measure of 19.01 % when compared to KBT and 9.44 % when compared to a method that relies solely on timestamps extracted from the table and its context.