---
title: "PATTY: A Taxonomy of Relational Patterns — Nakashole 2012"
description: "Large knowledge bases store relations as abstract labels such as bornIn, but they do not record the many natural-language phrasings that express those…"
authors: ["Ndapandula Nakashole", "Gerhard Weikum", "Fabian Suchanek"]
year: 2012
venue: "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning"
source: "https://aclanthology.org/D12-1104/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nakashole-2012-patty-taxonomy-relational-patterns-semantic/"
---
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# PATTY: A Taxonomy of Relational Patterns with Semantic Types

**Authors:** Ndapandula Nakashole, [Gerhard Weikum](https://md.research.elamur.ai/free/en/authors/gerhard-weikum/), [Fabian Suchanek](https://md.research.elamur.ai/free/en/authors/fabian-suchanek/) · **Year:** 2012 · **Venue:** Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/D12-1104/) · [PDF](https://aclanthology.org/D12-1104.pdf) · [OpenAlex](https://openalex.org/W1483236033)

**Citations:** 348 citations (OpenAlex, 2026-10-14) · [84](https://md.research.elamur.ai/free/en/papers/nakashole-2012-patty-taxonomy-relational-patterns-semantic/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
Large knowledge bases store relations as abstract labels such as bornIn, but they do not record the many natural-language phrasings that express those relations. Existing pattern collections such as ReVerb also leave their phrases unorganized: they are not grouped into synonyms and not arranged hierarchically. PATTY addresses this by building a WordNet-like resource for binary relational phrases.

The pipeline works as follows:
- Wikipedia sentences are parsed with the Stanford Parser.
- Entity mentions are linked to YAGO2.
- For each entity pair, the shortest dependency path between them becomes a candidate pattern.
- Each pattern is then abstracted into a SOL pattern, which combines syntactic placeholders, ontological type signatures for the arguments and lexical words.
- Frequent itemset mining groups patterns into synsets and infers subsumptions from overlapping entity-pair support.

The resulting taxonomy has 350,569 pattern synsets with about 84.7% sampled accuracy and 8,162 subsumption links with about 75% precision. This matters because typed, organized paraphrases support information extraction, knowledge-base population and question answering.

## Key points
- **Pattern extraction:** Stanford Parser dependency paths are used, keeping only shortest paths that start with subject-like dependencies (nsubj, rcmod, partmod). These paths are expanded with adverbial and adjectival modifiers.
- **Entity linking:** Entities are recognized and typed with the YAGO2 knowledge base and disambiguated with a simple context-similarity prior. The reported linking accuracy is well above 80%.
- **Pattern-language comparison (top-100 precision, Table 5):**

  | Variant | Patterns | Precision |
  |---|---|---|
  | ReVerb-style | 5,996 | 0.96 |
  | PATTY without types | 184,629 | 0.74 |
  | Full PATTY | 350,569 | 0.95 |

  Full PATTY also has 8,162 subsumptions at 0.83 precision and 3.89M facts at 0.88 precision.
- **Relation paraphrasing:** PATTY covered 225 DBpedia relations with 127,811 patterns and 25 YAGO2 relations with 43,124 patterns. The average paraphrase precision was 0.76.
- **Availability:** The resource can be browsed and downloaded at www.mpi-inf.mpg.de/yago-naga/patty/.

## Interesting ideas
- **Typing pattern arguments with a knowledge base.** Replacing entities with semantic classes such as ⟨singer⟩ or ⟨song⟩ separates ambiguous phrases by context, and dropping types caused a large precision loss. The same trick could help any relation-mining effort that has access to a typed entity dictionary.
- **Two complementary ways to generalize.** Syntactic generalization swaps words for POS placeholders, for example collapsing "her"/"his" into [prp]. Semantic generalization moves argument types up the class hierarchy. Treating both together yields compact, reusable patterns from noisy text.
- **Inferring subsumption from instance support.** One pattern is taken to imply another when its entity-pair set is largely contained in the other's. This cheap, data-driven route to a hierarchy could transfer to clustering paraphrases, verbs or query templates.
- **Framing mining as frequent itemset mining.** Treating pattern generation this way keeps the exponential space of candidate patterns tractable at Web scale. This is a useful engineering choice for any combinatorial pattern-discovery task.
- **Extrinsic evaluation via paraphrasing.** Measuring how well mined phrases express existing DBpedia and YAGO2 relations gives a practical usefulness check beyond intrinsic sampling. It also exposes failure cases, such as overly general phrases offered for specific relations.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cite

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

```bibtex
@inproceedings{nakashole2012patty,
  title = {{PATTY: A Taxonomy of Relational Patterns with Semantic Types}},
  author = {Nakashole, Ndapandula and Weikum, Gerhard and Suchanek, Fabian},
  booktitle = {Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
  year = {2012},
  url = {https://aclanthology.org/D12-1104/}
}
```

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## References

40 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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