---
title: "Deriving a Web-Scale Common Sense Fact Database (2011)"
description: "Everyday knowledge, such as the fact that birds have feathers, is largely missing from… 32 references, cited by 9 papers here."
authors: ["Niket Tandon", "Gerard de Melo", "Gerhard Weikum"]
year: 2011
venue: "AAAI-11"
source: "https://ojs.aaai.org/index.php/AAAI/article/view/7841"
licence: "All rights reserved — free to read at the source"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tandon-2011-deriving-web-scale-common-sense/"
---
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# Deriving a Web-Scale Common Sense Fact Database

**Authors:** [Niket Tandon](https://md.research.elamur.ai/free/en/authors/niket-tandon/), [Gerard de Melo](https://md.research.elamur.ai/free/en/authors/gerard-de-melo/) and [Gerhard Weikum](https://md.research.elamur.ai/free/en/authors/gerhard-weikum/) · **Year:** 2011 · **Venue:** [AAAI-11](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/) ([Proceedings of the AAAI Conference on Artificial Intelligence 2011](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/2011/)) · **Publisher:** [Association for the Advancement of Artificial Intelligence](https://md.research.elamur.ai/free/en/publishers/association-for-the-advancement-of-artificial-intelligence/) · **Licence:** All rights reserved — free to read at the source

**Links:** [Publisher's page](https://doi.org/10.1609/aaai.v25i1.7841) · [PDF](https://cdn.aaai.org/ojs/7841/7841-13-11369-1-2-20201228.pdf) · [OpenAlex](https://openalex.org/W1494776307)

**Citations:** 36 citations (OpenAlex, 2026-10-14) · [9](https://md.research.elamur.ai/free/en/papers/tandon-2011-deriving-web-scale-common-sense/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Everyday knowledge, such as the fact that birds have feathers, is largely missing from machine-readable resources. Earlier automatic knowledge base construction concentrated on taxonomies and relations between named entities. Tandon, de Melo and Weikum instead target common sense relations like CapableOf, PartOf and HasProperty. They start from a large set of seed facts taken from ConceptNet, not from a handful of examples. Rather than reading a text corpus or querying a search engine, they mine a Web-scale n-gram dataset (Brants and Franz 2006). Patterns are induced wherever seed pairs co-occur inside n-grams. A new scoring function then removes patterns that either sit in the long tail of seed matches or fire across many different relations. The surviving patterns collect candidate facts. Each candidate is represented by its pattern frequency statistics and classified by a learner trained on ConceptNet's own positive and negative examples, so no extra annotation is needed. The resulting database is many orders of magnitude larger than ConceptNet at similar precision. This shows that n-gram statistics can serve as a practical stand-in for the whole Web in information extraction.

## Key points
- **Data and method:** ConceptNet seeds are combined with Web n-gram statistics. Pattern scoring accounts for the power-law spread of seeds per pattern, estimated by log-log regression, and for pattern overlap with other relations. A threshold θ_min = 0.6 kept fewer than 1000 patterns per relation.
- **Fact classification:** Facts are assessed with C4.5 decision trees boosted by AdaBoost (M1). Only facts with leaf probability above 50% are kept. The Stanford tagger checks part-of-speech tags during matching.
- **Manual check:** On 100 random accepted facts per relation, accuracy was 64% for CapableOf, 78% for HasProperty and 67% for IsA. Because the labelled sets are balanced, the random baseline is 50%.
- **Pattern clustering:** Modularity-based clustering of HasProperty patterns reached a modularity of 0.61, indicating clear community structure. Patterns that differ only in verb tense, joining \<X> to \<Y> with "is" in one case and "was" in the other, ended up grouped together, which is useful for paraphrasing.
- **Limitations and future work:** The approach handles only single-token terms and binary relations, and it does not yet disambiguate word senses. The authors plan to address these and to add relations, for example from WordNet.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Max Planck Institute for Informatics](https://md.research.elamur.ai/free/en/labs/max-planck-institute-for-informatics/) — Research institute, Saarbrücken, Germany · 22 works · 4,687 citations

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## Cite

Cite as: Niket Tandon, Gerard de Melo and Gerhard Weikum. 2011. Deriving a Web-Scale Common Sense Fact Database. AAAI-11.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tandon2011deriving,
  title = {{Deriving a Web-Scale Common Sense Fact Database}},
  author = {Niket Tandon, Gerard {de Melo} and Gerhard Weikum},
  booktitle = {AAAI-11},
  year = {2011},
  doi = {10.1609/aaai.v25i1.7841},
  url = {https://doi.org/10.1609/aaai.v25i1.7841}
}
```

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*Updated 3 November 2026*