Deriving a Web-Scale Common Sense Fact Database
By Niket Tandon, Gerard de Melo, Gerhard Weikum. 2011
41 citations recorded by the source corpus.
The fact that birds have feathers and ice is cold seems trivially true. Yet, most machine-readable sources of knowledge either lack such common sense facts entirely or have only limited coverage. Prior work on automated knowledge base construction has largely focused on relations between named entities and on taxonomic knowledge, while disregarding common sense properties. In this paper, we show how to gather large amounts of common sense facts from Web n-gram data, using seeds from the ConceptNet collection. Our novel contributions include scalable methods for tapping onto Web-scale data and a new scoring model to determine which patterns and facts are most reliable. The experimental results show that this approach extends ConceptNet by many orders of magnitude at comparable levels of precision.
Subfields: Artificial Intelligence, Molecular Biology, Information Retrieval & Search
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Andreas Vlachos
- Christopher D. Manning
- Sebastian Riedel
- Zhiyuan Liu
- Caiming Xiong
- James Thorne
- Maosong Sun
In Molecular Biology
- Christopher D. Manning
- Gabor Angeli
- Danqi Chen
- Gerard de Melo
- Andreas Vlachos
- Richard Socher
- Andrew Y. Ng
- Zhiyuan Liu
In Information Retrieval & Search
- Christopher D. Manning
- Zhiyuan Liu
- James Thorne
- Andreas Vlachos
- Richard Socher
- Gabor Angeli
- Arpit Mittal
- Christos Christodoulopoulos
Citations in the corpus
- FEVER: a Large-scale Dataset for Fact Extraction and VERification (2018, background): “However, the claims were small in number (1,378) and limited in variety as they were derived from eight binary ConceptNet relations (Tandon et al., 2011).”
- NaturalLI: Natural Logic Inference for Common Sense Reasoning (2014, methodology): “For our evaluation, we infer the top 689 most confident facts from the ConceptNet project (Tandon et al., 2011).”
- Relation Classification via Multi-Level Attention CNNs (2016, background): “for various information extraction applications as well as for question answering and knowledge base completion (Tandon et al., 2011; Chen et al., 2015).”
- Reasoning With Neural Tensor Networks for Knowledge Base Completion (2013, background): “This can be seen as common sense reasoning [24] over known facts or link prediction in relationship networks.”