---
title: "Learning a Compositional Semantics for Freebase with an Open"
description: "Semantic parsers for Freebase usually map language onto a small, hand-built set of formal predicates. 43 references, cited by 14 papers here."
authors: ["Jayant Krishnamurthy", "Tom M. Mitchell"]
year: 2015
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/Q15-1019/"
doi: "10.1162/tacl_a_00137"
licence: "CC BY-NC-SA 4.0 (TACL)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/krishnamurthy-2015-learning-compositional-semantics-freebase-open/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/krishnamurthy-2015-learning-compositional-semantics-freebase-open/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2015](https://md.research.elamur.ai/free/en/papers/2015/page/3/) / Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary

# Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary

**Authors:** [Jayant Krishnamurthy](https://md.research.elamur.ai/free/en/authors/jayant-krishnamurthy/) and [Tom M. Mitchell](https://md.research.elamur.ai/free/en/authors/tom-m-mitchell/) · **Year:** 2015 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2015](https://md.research.elamur.ai/free/en/venues/tacl/2015/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY-NC-SA 4.0 (TACL)](https://creativecommons.org/licenses/by-nc-sa/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/Q15-1019/) · [PDF](https://aclanthology.org/Q15-1019.pdf) · [DOI 10.1162/tacl\_a\_00137](https://doi.org/10.1162/tacl_a_00137) · [OpenAlex](https://openalex.org/W2156621282)

**Citations:** 27 citations (OpenAlex, 2026-10-14) · [14](https://md.research.elamur.ai/free/en/papers/krishnamurthy-2015-learning-compositional-semantics-freebase-open/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Semantic parsers for Freebase usually map language onto a small, hand-built set of formal predicates. This limits coverage and erases fine distinctions: Freebase has no concept for "front-runner", for example. Krishnamurthy and Mitchell let each word define its own predicate while still grounding meaning in Freebase entities. A fixed, rule-based component converts the CCG parse of an entity-linked text into a logical form. That form is a conjunction of word-derived unary and binary predicates. It is evaluated against a learned probabilistic database, which gives each entity a marginal probability of belonging to the denotation. The database comes from probabilistic matrix factorization over entity and entity-pair embeddings. Training uses query/answer pairs extracted automatically from entity-linked ClueWeb 2009 sentences and a new ranking objective. On compositional question answering, the system beats several baselines, and the new objective improves on an earlier one. Hand-annotated Freebase queries are more precise where Freebase has coverage, but the open vocabulary answers many questions Freebase cannot express. This reverses the usual setup: the parser is fixed and the knowledge base is learned.

## Key points
- The pipeline has three stages: syntactic parsing with ASP-SYN trained on CCGbank, entity linking, and rule-based semantic composition. The parse is transformed first, for example by marking conjoined NPs and turning verb modifiers into core arguments.
- Entity links for ClueWeb 2009 come from Gabrilovich et al. (2013). They are kept only when they match an N/NP constituent or a group of noun-compound modifiers.
- The probabilistic database treats each predicate instance as independent, for example P(FRONT-RUNNER(/EN/GEORGE BUSH)) = 0.9. This allows efficient exact inference for safe queries.
- The query/answer ranking objective trains the model to score observed answers above unobserved ones. It outperforms a previously proposed training objective.
- The authors name three limitations: semantics restricted to existentially quantified conjunctions, no events or n-ary relations, and low recall. They suggest scaling training data 10–100× to address recall, for instance by using all of ClueWeb.

*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

- [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 539 works · 179,533 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

## Cite

Cite as: Jayant Krishnamurthy and Tom M. Mitchell. 2015. Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary. Transactions of the Association for Computational Linguistics.

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

```bibtex
@article{krishnamurthy2015learning,
  title = {{Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary}},
  author = {Jayant Krishnamurthy and Tom M. Mitchell},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2015},
  doi = {10.1162/tacl\_a\_00137},
  url = {https://doi.org/10.1162/tacl_a_00137}
}
```

- [← previous Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment](https://md.research.elamur.ai/free/en/papers/dutta-2015-cross-document-co-reference-resolution/)
- [next → Chinese Zero Pronoun Resolution: A Joint Unsupervised Discourse-Aware Model Rivaling State-of-the-Art Resolvers](https://md.research.elamur.ai/free/en/papers/chen-2015-chinese-zero-pronoun-resolution-joint/)
- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/page/3/)

## References

43 references, 12 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Michele Banko, Michael J. Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni. 2007. [Open information extraction from the web](https://md.research.elamur.ai/free/en/papers/banko-2007-open-information-extraction-web/). In Proceedings of the 20th International Joint Conference on Artifical Intelligence.
2. Islam Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, and Raymond Mooney. 2013. Montague meets markov: Deep semantics with probabilistic logical form. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Ta… [find](https://scholar.google.com/scholar?q=Islam+Beltagy%2C+Cuong+Chau%2C+Gemma+Boleda%2C+Dan+Garrette%2C+Katrin+Erk%2C+and+Raymond+Mooney%2E+2013%2E+Montague+meets+markov%3A+Deep+semantics+with+probabilistic+logical+form%2E+In+Second+Joint+Conference+on+Lexica)
3. Jonathan Berant and Percy Liang. 2014. [Semantic parsing via paraphrasing](https://md.research.elamur.ai/free/en/papers/berant-2014-semantic-parsing-paraphrasing/). In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.
4. Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. [Semantic parsing on Freebase from question-answer pairs](https://md.research.elamur.ai/free/en/papers/berant-2013-semantic-parsing-freebase-question-answer/). In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.
5. Chris Biemann. 2006. [Chinese whispers: An efficient graph clustering algorithm and its application to natural language processing problems](https://md.research.elamur.ai/free/en/papers/biemann-2006-chinese-whispers-efficient-graph-clustering/). In Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing.
6. Johan Bos. 2008. Wide-coverage semantic analysis with boxer. In Proceedings of the 2008 Conference on Semantics in Text Processing. [find](https://scholar.google.com/scholar?q=Johan+Bos%2E+2008%2E+Wide%2Dcoverage+semantic+analysis+with+boxer%2E+In+Proceedings+of+the+2008+Conference+on+Semantics+in+Text+Processing%2E)
7. Qingqing Cai and Alexander Yates. 2013. Large-scale Semantic Parsing via Schema Matching and Lexicon Extension. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL). [find](https://scholar.google.com/scholar?q=Qingqing+Cai+and+Alexander+Yates%2E+2013%2E+Large%2Dscale+Semantic+Parsing+via+Schema+Matching+and+Lexicon+Extension%2E+In+Proceedings+of+the+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28A)
8. Nilesh Dalvi and Dan Suciu. 2007. Efficient query evaluation on probabilistic databases. The VLDB Journal, 16(4), October. [find](https://scholar.google.com/scholar?q=Nilesh+Dalvi+and+Dan+Suciu%2E+2007%2E+Efficient+query+evaluation+on+probabilistic+databases%2E+The+VLDB+Journal%2C+16%284%29%2C+October%2E)
9. David R. Dowty, Robert E. Wall, and Stanley Peters. 1981. Introduction to Montague Semantics. [find](https://scholar.google.com/scholar?q=David+R%2E+Dowty%2C+Robert+E%2E+Wall%2C+and+Stanley+Peters%2E+1981%2E+Introduction+to+Montague+Semantics%2E)
10. John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12:2121–2159, July. [find](https://scholar.google.com/scholar?q=John+Duchi%2C+Elad+Hazan%2C+and+Yoram+Singer%2E+2011%2E+Adaptive+subgradient+methods+for+online+learning+and+stochastic+optimization%2E+Journal+of+Machine+Learning+Research%2C+12%3A2121%E2%80%932159%2C+July%2E)
11. Anthony Fader, Stephen Soderland, and Oren Etzioni. 2011. Identifying relations for open information extraction. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Anthony+Fader%2C+Stephen+Soderland%2C+and+Oren+Etzioni%2E+2011%2E+Identifying+relations+for+open+information+extraction%2E+In+Proceedings+of+the+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2E)
12. Anthony Fader, Luke Zettlemoyer, and Oren Etzioni. 2013. [Paraphrase-driven learning for open question answering](https://md.research.elamur.ai/free/en/papers/fader-2013-paraphrase-driven-learning-open-question/). In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics.
13. Evgeniy Gabrilovich, Michael Ringgaard, and Amarnag Subramanya. 2013. FACC1: Freebase annotation of ClueWeb corpora, Version 1 (Release date 2013-06-26, Format version 1, Correction level 0). [http://lemurproject.org/clueweb09/](http://lemurproject.org/clueweb09/). [↗](http://lemurproject.org/clueweb09/)
14. Dan Garrette, Katrin Erk, and Raymond Mooney. 2011. Integrating logical representations with probabilistic information using markov logic. In Proceedings of the International Conference on Computational Semantics. [find](https://scholar.google.com/scholar?q=Dan+Garrette%2C+Katrin+Erk%2C+and+Raymond+Mooney%2E+2011%2E+Integrating+logical+representations+with+probabilistic+information+using+markov+logic%2E+In+Proceedings+of+the+International+Conference+on+Computation)
15. Dan Garrette, Katrin Erk, and Raymond J. Mooney. 2013. A formal approach to linking logical form and vector-space lexical semantics. In Harry Bunt, Johan Bos, and Stephen Pulman, editors, Computing Meaning, volume 4, pages 27–48. [find](https://scholar.google.com/scholar?q=Dan+Garrette%2C+Katrin+Erk%2C+and+Raymond+J%2E+Mooney%2E+2013%2E+A+formal+approach+to+linking+logical+form+and+vector%2Dspace+lexical+semantics%2E+In+Harry+Bunt%2C+Johan+Bos%2C+and+Stephen+Pulman%2C+editors%2C+Computing+Me)
16. Wolfgang Gatterbauer and Dan Suciu. 2015. Approximate lifted inference with probabilistic databases. Proceedings of the VLDB Endowment, 8(5), January. [find](https://scholar.google.com/scholar?q=Wolfgang+Gatterbauer+and+Dan+Suciu%2E+2015%2E+Approximate+lifted+inference+with+probabilistic+databases%2E+Proceedings+of+the+VLDB+Endowment%2C+8%285%29%2C+January%2E)
17. Wolfgang Gatterbauer, Abhay Kumar Jha, and Dan Suciu. 2010. Dissociation and propagation for efficient query evaluation over probabilistic databases. In Proceedings of the Fourth International VLDB workshop on Management of Uncertain Data (MUD 2010) in conjunction with VLDB 2010, Singapore, Septembe… [find](https://scholar.google.com/scholar?q=Wolfgang+Gatterbauer%2C+Abhay+Kumar+Jha%2C+and+Dan+Suciu%2E+2010%2E+Dissociation+and+propagation+for+efficient+query+evaluation+over+probabilistic+databases%2E+In+Proceedings+of+the+Fourth+International+VLDB+wo)
18. Thomas L. Griffiths, Joshua B. Tenenbaum, and Mark Steyvers. 2007. Topics in semantic representation. Psychological Review 114. [find](https://scholar.google.com/scholar?q=Thomas+L%2E+Griffiths%2C+Joshua+B%2E+Tenenbaum%2C+and+Mark+Steyvers%2E+2007%2E+Topics+in+semantic+representation%2E+Psychological+Review+114%2E)
19. Julia Hockenmaier and Mark Steedman. 2002. Acquiring compact lexicalized grammars from a cleaner treebank. In Proceedings of Third International Conference on Language Resources and Evaluation. [find](https://scholar.google.com/scholar?q=Julia+Hockenmaier+and+Mark+Steedman%2E+2002%2E+Acquiring+compact+lexicalized+grammars+from+a+cleaner+treebank%2E+In+Proceedings+of+Third+International+Conference+on+Language+Resources+and+Evaluation%2E)
20. Jayant Krishnamurthy and Tom M. Mitchell. 2012. [Weakly supervised training of semantic parsers](https://md.research.elamur.ai/free/en/papers/krishnamurthy-2012-weakly-supervised-training-semantic-parsers/). In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.
21. Jayant Krishnamurthy and Tom M. Mitchell. 2014. Joint syntactic and semantic parsing with combinatory categorial grammar. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jayant+Krishnamurthy+and+Tom+M%2E+Mitchell%2E+2014%2E+Joint+syntactic+and+semantic+parsing+with+combinatory+categorial+grammar%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Association+for+Computational)
22. Tom Kwiatkowski, Eunsol Choi, Yoav Artzi, and Luke Zettlemoyer. 2013. [Scaling semantic parsers with on-the-fly ontology matching](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2013-scaling-semantic-parsers-fly-ontology/). In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.
23. Mike Lewis and Mark Steedman. 2013. Combined distributional and logical semantics. Transactions of the Association for Computational Linguistics, 1:179– 192. [find](https://scholar.google.com/scholar?q=Mike+Lewis+and+Mark+Steedman%2E+2013%2E+Combined+distributional+and+logical+semantics%2E+Transactions+of+the+Association+for+Computational+Linguistics%2C+1%3A179%E2%80%93+192%2E)
24. Dekang Lin and Patrick Pantel. 2001. DIRT — discovery of inference rules from text. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. [find](https://scholar.google.com/scholar?q=Dekang+Lin+and+Patrick+Pantel%2E+2001%2E+DIRT+%E2%80%94+discovery+of+inference+rules+from+text%2E+In+Proceedings+of+the+Seventh+ACM+SIGKDD+International+Conference+on+Knowledge+Discovery+and+Data+Mining%2E+Christophe)
25. Cambridge University Press, New York, NY, USA. David Milne and Ian H. Witten. 2008. Learning to link with wikipedia. In Proceedings of the 17th ACM Conference on Information and Knowledge Management. [find](https://scholar.google.com/scholar?q=Cambridge+University+Press%2C+New+York%2C+NY%2C+USA%2E+David+Milne+and+Ian+H%2E+Witten%2E+2008%2E+Learning+to+link+with+wikipedia%2E+In+Proceedings+of+the+17th+ACM+Conference+on+Information+and+Knowledge+Management%2E)
26. Hoifung Poon and Pedro Domingos. 2009. [Unsupervised semantic parsing](https://md.research.elamur.ai/free/en/papers/poon-2009-unsupervised-semantic-parsing/). In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing.
27. Lev Ratinov, Dan Roth, Doug Downey, and Mike Anderson. 2011. Local and global algorithms for disambiguation to wikipedia. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. [find](https://scholar.google.com/scholar?q=Lev+Ratinov%2C+Dan+Roth%2C+Doug+Downey%2C+and+Mike+Anderson%2E+2011%2E+Local+and+global+algorithms+for+disambiguation+to+wikipedia%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+for+Computational)
28. Siva Reddy, Mirella Lapata, and Mark Steedman. 2014. Large-scale semantic parsing without question-answer pairs. Transactions of the Association of Computational Linguistics – Volume 2, Issue 1. [find](https://scholar.google.com/scholar?q=Siva+Reddy%2C+Mirella+Lapata%2C+and+Mark+Steedman%2E+2014%2E+Large%2Dscale+semantic+parsing+without+question%2Danswer+pairs%2E+Transactions+of+the+Association+of+Computational+Linguistics+%E2%80%93+Volume+2%2C+Issue+1%2E)
29. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Steffen+Rendle%2C+Christoph+Freudenthaler%2C+Zeno+Gantner%2C+and+Lars+Schmidt%2DThieme%2E+2009%2E+BPR%3A+Bayesian+personalized+ranking+from+implicit+feedback%2E+In+Proceedings+of+the+Twenty%2DFifth+Conference+on+Uncert)
30. Matthew Richardson and Pedro Domingos. 2006. Markov logic networks. Machine Learning, 62(1-2):107–136, February. [find](https://scholar.google.com/scholar?q=Matthew+Richardson+and+Pedro+Domingos%2E+2006%2E+Markov+logic+networks%2E+Machine+Learning%2C+62%281%2D2%29%3A107%E2%80%93136%2C+February%2E)
31. Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M. Marlin. 2013. Relation extraction with matrix factorization and universal schemas. In Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Sebastian+Riedel%2C+Limin+Yao%2C+Andrew+McCallum%2C+and+Benjamin+M%2E+Marlin%2E+2013%2E+Relation+extraction+with+matrix+factorization+and+universal+schemas%2E+In+Joint+Human+Language+Technology+Conference%2FAnnual+Me)
32. Sameer Singh, Amarnag Subramanya, Fernando Pereira, and Andrew McCallum. 2011. [Large-scale cross-document coreference using distributed inference and hierarchical models](https://md.research.elamur.ai/free/en/papers/singh-2011-large-scale-cross-document-coreference/). In Association for Computational Linguistics: Human Language Technologies (ACL HLT).
33. Robert Speer, Catherine Havasi, and Henry Lieberman. 2008. AnalogySpace: Reducing the dimensionality of common sense knowledge. In AAAI. [find](https://scholar.google.com/scholar?q=Robert+Speer%2C+Catherine+Havasi%2C+and+Henry+Lieberman%2E+2008%2E+AnalogySpace%3A+Reducing+the+dimensionality+of+common+sense+knowledge%2E+In+AAAI%2E)
34. Dan Suciu, Dan Olteanu, Christopher Ré, and Christoph Koch. 2011. Probabilistic databases. Synthesis Lectures on Data Management, 3(2):1–180. [find](https://scholar.google.com/scholar?q=Dan+Suciu%2C+Dan+Olteanu%2C+Christopher+Re%CC%81%2C+and+Christoph+Koch%2E+2011%2E+Probabilistic+databases%2E+Synthesis+Lectures+on+Data+Management%2C+3%282%29%3A1%E2%80%93180%2E)
35. Ivan Titov and Alexandre Klementiev. 2011. A bayesian model for unsupervised semantic parsing. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Ivan+Titov+and+Alexandre+Klementiev%2E+2011%2E+A+bayesian+model+for+unsupervised+semantic+parsing%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2E)
36. Peter D. Turney and Patrick Pantel. 2010. [From frequency to meaning: vector space models of semantics](https://md.research.elamur.ai/free/en/papers/turney-2010-frequency-meaning-vector-space-models/). Journal of Artificial Intelligence Research, 37(1), January.
37. Peter D. Turney. 2008. The latent relation mapping engine: Algorithm and experiments. Journal of Artificial Intelligence Research, 33(1):615–655, December. [find](https://scholar.google.com/scholar?q=Peter+D%2E+Turney%2E+2008%2E+The+latent+relation+mapping+engine%3A+Algorithm+and+experiments%2E+Journal+of+Artificial+Intelligence+Research%2C+33%281%29%3A615%E2%80%93655%2C+December%2E)
38. Mohamed Yahya, Klaus Berberich, Shady Elbassuoni, Maya Ramanath, Volker Tresp, and Gerhard Weikum. 2012. Natural language questions for the web of data. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. [find](https://scholar.google.com/scholar?q=Mohamed+Yahya%2C+Klaus+Berberich%2C+Shady+Elbassuoni%2C+Maya+Ramanath%2C+Volker+Tresp%2C+and+Gerhard+Weikum%2E+2012%2E+Natural+language+questions+for+the+web+of+data%2E+In+Proceedings+of+the+2012+Joint+Conference+on+)
39. Limin Yao, Sebastian Riedel, and Andrew McCallum. 2012. Unsupervised relation discovery with sense disambiguation. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1. [find](https://scholar.google.com/scholar?q=Limin+Yao%2C+Sebastian+Riedel%2C+and+Andrew+McCallum%2E+2012%2E+Unsupervised+relation+discovery+with+sense+disambiguation%2E+In+Proceedings+of+the+50th+Annual+Meeting+of+the+Association+for+Computational+Lingui)
40. Limin Yao, Sebastian Riedel, and Andrew McCallum. 2013. Universal schema for entity type prediction. In Proceedings of the 2013 Workshop on Automated Knowledge Base Construction. [find](https://scholar.google.com/scholar?q=Limin+Yao%2C+Sebastian+Riedel%2C+and+Andrew+McCallum%2E+2013%2E+Universal+schema+for+entity+type+prediction%2E+In+Proceedings+of+the+2013+Workshop+on+Automated+Knowledge+Base+Construction%2E)
41. Alexander Yates and Oren Etzioni. 2007. Unsupervised resolution of objects and relations on the web. In Proceedings of the 2007 Annual Conference of the North American Chapter of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Alexander+Yates+and+Oren+Etzioni%2E+2007%2E+Unsupervised+resolution+of+objects+and+relations+on+the+web%2E+In+Proceedings+of+the+2007+Annual+Conference+of+the+North+American+Chapter+of+the+Association+for+C)
42. John M. Zelle and Raymond J. Mooney. 1996. [Learning to parse database queries using inductive logic programming](https://md.research.elamur.ai/free/en/papers/zelle-1996-learning-parse-database-queries-using/). In Proceedings of the thirteenth national conference on Artificial Intelligence.
43. Luke S. Zettlemoyer and Michael Collins. 2005. [Learning to map sentences to logical form: structured classification with probabilistic categorial grammars](https://md.research.elamur.ai/free/en/papers/zettlemoyer-2012-learning-map-sentences-logical-form/). In UAI ’05, Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence.

*Updated 30 October 2026*