Extracting new knowledge from web tables: Novelty or confidence?

To extend the coverage of Knowledge Bases (KBs), it is useful to integrate factual information from public tabular data. Ideally, the extracted information should not only be correct, but also novel. So far, the evaluation of state-of-the-art techniques for this task has focused primarily on the correctness of the extractions, but the novelty is less well analysed. To fill this gap, we replicated the evaluation of two state-of-the-art techniques and analyse the amount of novel extractions using two new metrics. We observe that current techniques are biased towards confidence, but this comes at the expense of novelty. We sketch a possible solution for this problem as part of our ongoing research

Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version)

We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aims to find more novel facts. We introduce a new technique for table interpretation based on a scalable graphical model using entity similarities. Our method further disambiguates cell values using KG embeddings as additional ranking method. Other distinctive features are the lack of assumptions about the underlying KG and the enabling of a fine-grained tuning of the precision/recall trade-off of extracted facts. Our experiments show that our approach has a higher recall during the interpretation process than the state-of-the-art, and is more resistant against the bias observed in extracting mostly redundant facts since it produces more novel extractions

TAKCO: A platform for extracting novel facts from tables

Web tables contain a large amount of useful knowledge. Takco is a new large-scale platform designed for extracting facts from tables that can be added to Knowledge Graphs (KGs) like Wikidata. Focusing on achieving high precision, current techniques are biased towards extracting redundant facts, i.e., facts already in the KG. Takco aims to find more novel facts, still at high precision. Our demonstration has two goals. The first one is to illustrate the main features of Takco's novel interpretation algorithm. The second goal is to show to what extent other state-of-the-art systems are biased towards the extraction of redundant facts using our platform, thus raising awareness on this important problem

takco

takco is a modular system for extracting knowledge from tables. For example, you can use it to extend Wikidata with information from Wikipedia tables

Tab2Know evaluation data

Evaluation data for the paper "Tab2Know: Building a Knowledge Base from Tables in Scientific Papers" published at ISWC2020

Tab2Know

Building a Knowledge Base from Tables in Scientific Paper