Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, but abstain from taking any stance towards statements not contained in them. In this work, we make the case for explicitly stating interesting or useful statements which are *NOT* true.
- 1.4M interesting negative statements about the most popular 130K people, organizations, and literature work. - [HERE] *
Methodology used: peer-based statistical inference (similarity-based) method in AKBC 2020 paper.
- 12.5M interesting negative statements about 545K entities from various types (mostly people, literature work, and sport events). - [HERE] **
Methodology used: temporal statistical inference method in SOON TO BE PUBLISHED WORK.
- 40K time-based peer groups. - [HERE]
ENTITIES_IN_GROUP_i = set_1|set_2|etc = e1;e2;e3|e4|e5;e6|etc
Description: time-based peer groups created as inputs for dataset **.
- 6.2K interesting negative statements about the most popular 2.4K people. - [HERE]
Methodology used: pattern-based query log extraction method in AKBC 2020 paper.
- 1K mturk-annotated negative statements - [HERE]
[Format: ROW_ID[tab]S_ID[tab]S_LABEL[tabl]S_TYPE[tab]CORRECTNESS_LABEL], retrived from *.
Correctness assessment: correct, incorrect, not sure.
We make our similarity-based statistical inference method public, for users to try it on their own tabular datasets.
Visit our Github repository!
We provide three sample datasets and their useful negations, on: Turing award winners, U.S. presidents, and hotels in India.