---
title: "Commonsense Reasoning and Commonsense Knowledge in"
description: "The authors then describe four areas where well-developed theory already exists: reasoning about… 49 references, cited by 80 papers here."
authors: ["Ernest Davis", "Gary Marcus"]
year: 2015
venue: "Communications of the ACM 58(9):92–103"
source: "https://cacm.acm.org/research/commonsense-reasoning-and-commonsense-knowledge-in-artificial-intelligence/"
doi: "10.1145/2701413"
licence: "author's copy — read only"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/davis-2015-commonsense-reasoning-commonsense-knowledge-artificial/"
---
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# Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence

**Authors:** [Ernest Davis](https://md.research.elamur.ai/free/en/authors/ernest-davis/) and [Gary Marcus](https://md.research.elamur.ai/free/en/authors/gary-marcus/) · **Year:** 2015 · **Venue:** [Communications of the ACM 58(9):92–103](https://md.research.elamur.ai/free/en/venues/communications-of-the-acm/) ([Communications of the ACM 2015](https://md.research.elamur.ai/free/en/venues/communications-of-the-acm/2015/)) · **Publisher:** [Association for Computing Machinery](https://md.research.elamur.ai/free/en/publishers/association-for-computing-machinery/) · **Licence:** author's copy — read only

**Links:** [Publisher's page](https://doi.org/10.1145/2701413) · [PDF](https://cs.nyu.edu/~davise/papers/CommonsenseFinal.pdf) · [DOI 10.1145/2701413](https://doi.org/10.1145/2701413) · [OpenAlex](https://openalex.org/W2073302931)

**Citations:** 428 citations (OpenAlex, 2026-10-14) · [80](https://md.research.elamur.ai/free/en/papers/davis-2015-commonsense-reasoning-commonsense-knowledge-artificial/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This is a survey article on commonsense reasoning and knowledge about the everyday world, one of the oldest problems in artificial intelligence and one where progress has been slowest. Using examples, the authors show where such knowledge is lacking. In NLP and machine translation, ambiguity cannot be resolved without it. In computer vision, partially visible or occluded objects cannot be recognized without it. Robots need it to respond appropriately to unforeseen events. The authors then describe four areas where well-developed theory already exists: reasoning about taxonomies, time, actions and change, and qualitative reasoning based on the signs of quantities (sign calculus). Next, they explain why the general problem is so difficult and critically examine the main approaches: large knowledge bases such as CYC, automatic extraction of facts from the web, and crowdsourcing. The main conclusion is that there is no universal solution: each direction has its advantages, but none provides reliable reasoning. The authors call for creating benchmarks and metrics, systematically evaluating CYC, combining approaches, covering broader kinds of reasoning, and drawing on cognitive science. For NLP projects, the article is useful as an argument about the limits of purely statistical methods.

## Key points
- **Winograd schemas.** These are pairs of sentences in which the referent of a pronoun changes when a single word is replaced (the example with the city council and the demonstrators: "feared" vs. "advocated"). Solving them requires knowledge about the world rather than linguistic cues. A collection of such examples was compiled by Levesque, Davis, and Morgenstern (2012).
- **The Google Translate test (9.06.2015, English → German).** In short sentences about an electrician and a telephone, "working" is translated correctly (arbeitet / funktioniert). When a few words are inserted between the key words, however, the choice of sense becomes wrong. The statistical substitute for common sense breaks down on more complex sentences.
- **Watson** (Jeopardy). According to Kalyanpur (2012), taxonomic, geographic, and temporal reasoning made only a small contribution to its success. The system is based on highly tuned rules for matching words to fragments of documents such as Wikipedia.
- **Taxonomies** (WordNet, semantic networks) support transitivity, property inheritance, and default inheritance: Robin inherits Bird → CanFly, but Penguin does not. For cross-cutting or abstract categories they become problematic.
- **Web mining and crowdsourcing.** The OIE system does not distinguish "wood" as a material from "wood" as a forest. The crowdsourced ConceptNet 3.5 contains contradictory links, uneven frequency of facts, and polysemous nodes (cook as a profession and as an action), and the restriction to binary relations limits its expressiveness. At the same time, for the query "What is made of wood?" (November 2014) it gave mostly sensible answers.

*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

- [New York University](https://md.research.elamur.ai/free/en/universities/new-york-university/) — University, New York, United States · 219 works · 103,487 citations

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works

🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,685 works

💡 Concepts (1): [💡 Artificial intelligence](https://md.research.elamur.ai/free/en/concepts/artificial-intelligence/) · 49 works

## Cite

Cite as: Ernest Davis and Gary Marcus. 2015. Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence. Communications of the ACM 58(9):92–103.

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

```bibtex
@inproceedings{davis2015commonsense,
  title = {{Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence}},
  author = {Ernest Davis and Gary Marcus},
  booktitle = {Communications of the ACM 58(9):92–103},
  year = {2015},
  doi = {10.1145/2701413},
  url = {https://doi.org/10.1145/2701413}
}
```

- [← previous Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation](https://md.research.elamur.ai/free/en/papers/xing-2015-normalized-word-embedding-orthogonal-transform/)
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## References

49 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Allison, B., Guthrie, D., & Guthrie, L. (2006). Another Look at the Data Sparsity Problem. Text, Speech, and Dialogue: 9th International Conference, (pp. 327-334). [find](https://scholar.google.com/scholar?q=Allison%2C+B%2E%2C+Guthrie%2C+D%2E%2C+%26+Guthrie%2C+L%2E+%282006%29%2E+Another+Look+at+the+Data+Sparsity+Problem%2E+Text%2C+Speech%2C+and+Dialogue%3A+9th+International+Conference%2C+%28pp%2E+327%2D334%29%2E)
2. Baader, F., Horrocks, I., & Sattler, U. (2008). Descriptions Logics. In F. Van Harmelen, V. Lifschitz, & B. Porter, Handbook of Knowledge Representation (pp. 135-179). Amsterdam: Elsevier. [find](https://scholar.google.com/scholar?q=Baader%2C+F%2E%2C+Horrocks%2C+I%2E%2C+%26+Sattler%2C+U%2E+%282008%29%2E+Descriptions+Logics%2E+In+F%2E+Van+Harmelen%2C+V%2E+Lifschitz%2C+%26+B%2E+Porter%2C+Handbook+of+Knowledge+Representation+%28pp%2E+135%2D179%29%2E+Amsterdam%3A+Elsevier%2E)
3. Bar-Hillel, Y. (1960). The present status of automatic translation of languges. In F. Alt, Advances in Computers (Vol. 1, pp. 91-163). New York: Academic Press. [find](https://scholar.google.com/scholar?q=Bar%2DHillel%2C+Y%2E+%281960%29%2E+The+present+status+of+automatic+translation+of+languges%2E+In+F%2E+Alt%2C+Advances+in+Computers+%28Vol%2E+1%2C+pp%2E+91%2D163%29%2E+New+York%3A+Academic+Press%2E)
4. Bojanowski, P., Lajugie, R., Bach, F., Laptev, I., Ponce, J., Schmid, C., & Sivic, J. (2014). Weakly supervised action labeling in videos under ordering constraints. ECCV, (pp. 628-643). [find](https://scholar.google.com/scholar?q=Bojanowski%2C+P%2E%2C+Lajugie%2C+R%2E%2C+Bach%2C+F%2E%2C+Laptev%2C+I%2E%2C+Ponce%2C+J%2E%2C+Schmid%2C+C%2E%2C+%26+Sivic%2C+J%2E+%282014%29%2E+Weakly+supervised+action+labeling+in+videos+under+ordering+constraints%2E+ECCV%2C+%28pp%2E+628%2D643%29%2E)
5. Brewka, G., Niemelli, I., & Truszczynski, M. (2008). Nonmonotonic Reasoning. In F. Van Harmelen, V. Lifschitz, & B. Porter, Handbook of Knowledge Representation (pp. 239-284). Amsterdam: Elsevier. [find](https://scholar.google.com/scholar?q=Brewka%2C+G%2E%2C+Niemelli%2C+I%2E%2C+%26+Truszczynski%2C+M%2E+%282008%29%2E+Nonmonotonic+Reasoning%2E+In+F%2E+Van+Harmelen%2C+V%2E+Lifschitz%2C+%26+B%2E+Porter%2C+Handbook+of+Knowledge+Representation+%28pp%2E+239%2D284%29%2E+Amsterdam%3A+Elsevier%2E)
6. Cohn, A., & Renz, J. (2007). Qualitative Spatial Reasoning. In F. van Harmelen, V. Lifschitz, & B. Porter, Handbook of Knowledge Representation (pp. 551-596). Elsevier. [find](https://scholar.google.com/scholar?q=Cohn%2C+A%2E%2C+%26+Renz%2C+J%2E+%282007%29%2E+Qualitative+Spatial+Reasoning%2E+In+F%2E+van+Harmelen%2C+V%2E+Lifschitz%2C+%26+B%2E+Porter%2C+Handbook+of+Knowledge+Representation+%28pp%2E+551%2D596%29%2E+Elsevier%2E)
7. Conesa, J., Storey, V., & Sugumaran, V. (2008). Improving web-query processing through semantic knowledge. Data and Knowledge Engineering, 66(1), 18-34. [find](https://scholar.google.com/scholar?q=Conesa%2C+J%2E%2C+Storey%2C+V%2E%2C+%26+Sugumaran%2C+V%2E+%282008%29%2E+Improving+web%2Dquery+processing+through+semantic+knowledge%2E+Data+and+Knowledge+Engineering%2C+66%281%29%2C+18%2D34%2E)
8. Conesa, J., Storey, V., & Sugumaran, V. (2010). Usability of upper level ontologies: The case of ResearchCyc. Data and Knowledge Engineering, 69, 343-356. [find](https://scholar.google.com/scholar?q=Conesa%2C+J%2E%2C+Storey%2C+V%2E%2C+%26+Sugumaran%2C+V%2E+%282010%29%2E+Usability+of+upper+level+ontologies%3A+The+case+of+ResearchCyc%2E+Data+and+Knowledge+Engineering%2C+69%2C+343%2D356%2E)
9. Curtis, J., Matthews, G., & Baxter, D. (2005). On the effective use of Cyc in a question answering system. IJCAI Workshop on Knowledge and Reasoning for Answering Questions. [find](https://scholar.google.com/scholar?q=Curtis%2C+J%2E%2C+Matthews%2C+G%2E%2C+%26+Baxter%2C+D%2E+%282005%29%2E+On+the+effective+use+of+Cyc+in+a+question+answering+system%2E+IJCAI+Workshop+on+Knowledge+and+Reasoning+for+Answering+Questions%2E)
10. Davis, E. (1998). The naive physics perplex. AI Magazine, 19(4), 51-79. From [http://www.aaai.org/ojs/index.php/aimagazine/article/view/1424/1323](http://www.aaai.org/ojs/index.php/aimagazine/article/view/1424/1323) de Kleer, J. (1975). Qualitative and quantitative knowledge in classical mechanics. [↗](http://www.aaai.org/ojs/index.php/aimagazine/article/view/1424/1323)
11. MIT AI Lab. de Kleer, J., & Brown, J. (1985). A qualitative physics based on confluences. In D. Bobrow, Qualitative Reasoning about Physical Systems (pp. 7-84). Cambridge: MIT Press. [find](https://scholar.google.com/scholar?q=MIT+AI+Lab%2E+de+Kleer%2C+J%2E%2C+%26+Brown%2C+J%2E+%281985%29%2E+A+qualitative+physics+based+on+confluences%2E+In+D%2E+Bobrow%2C+Qualitative+Reasoning+about+Physical+Systems+%28pp%2E+7%2D84%29%2E+Cambridge%3A+MIT+Press%2E)
12. Dennett, D. (2013). Intuition Pumps and Other Tools for Thinking. Norton. [find](https://scholar.google.com/scholar?q=Dennett%2C+D%2E+%282013%29%2E+Intuition+Pumps+and+Other+Tools+for+Thinking%2E+Norton%2E)
13. Etizoni, O., Fader, A., Christensen, J., Soderland, S., & Mausam. (2011). Open Information Extraction: The Second Generation. IJAI, (pp. 3-10). [find](https://scholar.google.com/scholar?q=Etizoni%2C+O%2E%2C+Fader%2C+A%2E%2C+Christensen%2C+J%2E%2C+Soderland%2C+S%2E%2C+%26+Mausam%2E+%282011%29%2E+Open+Information+Extraction%3A+The+Second+Generation%2E+IJAI%2C+%28pp%2E+3%2D10%29%2E)
14. Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popsecu, A., Shaked, T., . . . Yates, A. (2004). Web-scale extraction in KnowItAll (preliminary results). 13th International Conference on World Wide Web, (pp. 100-110). Retrieved from [http://dl.acm.org/citation.cfm?id=988687](http://dl.acm.org/citation.cfm?id=988687) [↗](http://dl.acm.org/citation.cfm?id=988687)
15. Ferrein, A., Fritz, C., & Lakemeyer, G. (2005). Using Golog for Deliberation and Team Coordination in Robotic Soccer. KI (Kuntzliche Intelligenz), 19(1), 24-30. [find](https://scholar.google.com/scholar?q=Ferrein%2C+A%2E%2C+Fritz%2C+C%2E%2C+%26+Lakemeyer%2C+G%2E+%282005%29%2E+Using+Golog+for+Deliberation+and+Team+Coordination+in+Robotic+Soccer%2E+KI+%28Kuntzliche+Intelligenz%29%2C+19%281%29%2C+24%2D30%2E)
16. Fisher, M. (2008). Temporal Representation and Reasoning. In F. Van Harmelen, V. Lifschitz, & B. Porter, Handbook of Knowledge Representation (pp. 513-550). Amsterdam: Elsevier. [find](https://scholar.google.com/scholar?q=Fisher%2C+M%2E+%282008%29%2E+Temporal+Representation+and+Reasoning%2E+In+F%2E+Van+Harmelen%2C+V%2E+Lifschitz%2C+%26+B%2E+Porter%2C+Handbook+of+Knowledge+Representation+%28pp%2E+513%2D550%29%2E+Amsterdam%3A+Elsevier%2E)
17. Forbus, K. (1985). Qualitative process theory. In D. Bobrow, Qualitative Reasoning about Physical Systems (pp. 85-168). Cambridge, Mass.: MIT Press. [find](https://scholar.google.com/scholar?q=Forbus%2C+K%2E+%281985%29%2E+Qualitative+process+theory%2E+In+D%2E+Bobrow%2C+Qualitative+Reasoning+about+Physical+Systems+%28pp%2E+85%2D168%29%2E+Cambridge%2C+Mass%2E%3A+MIT+Press%2E)
18. Forbus, K., Birnbaum, L., Wagner, E., Baker, J., & Witbrock, M. (2005). Analogy, Intelligent IR, and Knowledge Integration for Intelligence Analysis: Situation Tracking and the Whodunit Problem. International Conference on Intelligence Analysis. [find](https://scholar.google.com/scholar?q=Forbus%2C+K%2E%2C+Birnbaum%2C+L%2E%2C+Wagner%2C+E%2E%2C+Baker%2C+J%2E%2C+%26+Witbrock%2C+M%2E+%282005%29%2E+Analogy%2C+Intelligent+IR%2C+and+Knowledge+Integration+for+Intelligence+Analysis%3A+Situation+Tracking+and+the+Whodunit+Problem%2E+Inter)
19. Fromherz, M., Bobrow, D., & de Kleer, J. (2003). Model-based Computing for Design and Control of Reconfigurable Systems. AI Magazine, 24(4), 120. [find](https://scholar.google.com/scholar?q=Fromherz%2C+M%2E%2C+Bobrow%2C+D%2E%2C+%26+de+Kleer%2C+J%2E+%282003%29%2E+Model%2Dbased+Computing+for+Design+and+Control+of+Reconfigurable+Systems%2E+AI+Magazine%2C+24%284%29%2C+120%2E)
20. Gene Ontology Consortium. (2004). The Gene Ontology (GO) database and informatics resource. Nucleic acids research, 32(suppl. 1), D258-D261. [find](https://scholar.google.com/scholar?q=Gene+Ontology+Consortium%2E+%282004%29%2E+The+Gene+Ontology+%28GO%29+database+and+informatics+resource%2E+Nucleic+acids+research%2C+32%28suppl%2E+1%29%2C+D258%2DD261%2E)
21. Gentner, D., & Forbus, K. (2011). Computational models of analogy. WIREs Cognitive Science, 2, 266-276. [find](https://scholar.google.com/scholar?q=Gentner%2C+D%2E%2C+%26+Forbus%2C+K%2E+%282011%29%2E+Computational+models+of+analogy%2E+WIREs+Cognitive+Science%2C+2%2C+266%2D276%2E)
22. Halpern, J. (2003). Reasoning about Uncertainty. Cambridge, MA: MIT Press. [find](https://scholar.google.com/scholar?q=Halpern%2C+J%2E+%282003%29%2E+Reasoning+about+Uncertainty%2E+Cambridge%2C+MA%3A+MIT+Press%2E)
23. Havasi, C., Pustjekovsky, J., Speer, R., & Lieberman, H. (2009). Digital Intuition: Applying Common Sense Using Dimensionality Reduction. IEEE Intelligent Systems, 24(4), 24-35. doi:dx.doi.org/10.1109/MIS.2009.72 — [doi:10.1109/mis.2009.72](https://doi.org/10.1109/mis.2009.72) [find](https://scholar.google.com/scholar?q=Havasi%2C+C%2E%2C+Pustjekovsky%2C+J%2E%2C+Speer%2C+R%2E%2C+%26+Lieberman%2C+H%2E+%282009%29%2E+Digital+Intuition%3A+Applying+Common+Sense+Using+Dimensionality+Reduction%2E+IEEE+Intelligent+Systems%2C+24%284%29%2C+24%2D35%2E+doi%3Adx%2Edoi%2Eorg%2F10%2E1109)
24. Hearst, M. (1992). [Automatic acquisition of hyponyms from large text corpora](https://md.research.elamur.ai/free/en/papers/hearst-1992-automatic-acquisition-hyponyms-large-text/). ACL, (pp. 539-545).
25. Hofstadter, D., & Sander, E. (2013). Surfaces and Essences: Analogy as the Fuel and Fire of Thinking. New York: Basic Books. [find](https://scholar.google.com/scholar?q=Hofstadter%2C+D%2E%2C+%26+Sander%2C+E%2E+%282013%29%2E+Surfaces+and+Essences%3A+Analogy+as+the+Fuel+and+Fire+of+Thinking%2E+New+York%3A+Basic+Books%2E)
26. Kalyanpur, A. (2012). Structured data and inference in DeepQA. IBM Journal of Research and Development, 53(3-4), 10:1-14. [find](https://scholar.google.com/scholar?q=Kalyanpur%2C+A%2E+%282012%29%2E+Structured+data+and+inference+in+DeepQA%2E+IBM+Journal+of+Research+and+Development%2C+53%283%2D4%29%2C+10%3A1%2D14%2E)
27. Kolodner, J. (1993). Case-based reasoning. San Mateo, Calif.: Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=Kolodner%2C+J%2E+%281993%29%2E+Case%2Dbased+reasoning%2E+San+Mateo%2C+Calif%2E%3A+Morgan+Kaufmann%2E)
28. Kuehne, S. (2004). Understanding natural language description of physical phenomena. Evanston, I.: Northwestern University. [find](https://scholar.google.com/scholar?q=Kuehne%2C+S%2E+%282004%29%2E+Understanding+natural+language+description+of+physical+phenomena%2E+Evanston%2C+I%2E%3A+Northwestern+University%2E)
29. Kuipers, B. (1986). Qualitative simulation. Artificial Intelligence, 29, 289-338. [find](https://scholar.google.com/scholar?q=Kuipers%2C+B%2E+%281986%29%2E+Qualitative+simulation%2E+Artificial+Intelligence%2C+29%2C+289%2D338%2E)
30. Lenat, D., Prakash, M., & Shepherd, M. (1985). CYC: Using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks. AI Magazine, 6(4), 65-85. [find](https://scholar.google.com/scholar?q=Lenat%2C+D%2E%2C+Prakash%2C+M%2E%2C+%26+Shepherd%2C+M%2E+%281985%29%2E+CYC%3A+Using+common+sense+knowledge+to+overcome+brittleness+and+knowledge+acquisition+bottlenecks%2E+AI+Magazine%2C+6%284%29%2C+65%2D85%2E)
31. Levesque, H., Davis, E., & Morgenstern, L. (2012). The Winograd schema challenge. Principles of Knowledge Representation and Reasoing. [find](https://scholar.google.com/scholar?q=Levesque%2C+H%2E%2C+Davis%2C+E%2E%2C+%26+Morgenstern%2C+L%2E+%282012%29%2E+The+Winograd+schema+challenge%2E+Principles+of+Knowledge+Representation+and+Reasoing%2E)
32. Lovett, A., Tomei, E., Forbus, K., & Usher, J. (2009). Solving Geometric Analogy Problems through Two-Stage Analogical Mapping. Cognitive Science, 33(7), 1192-1231. [find](https://scholar.google.com/scholar?q=Lovett%2C+A%2E%2C+Tomei%2C+E%2E%2C+Forbus%2C+K%2E%2C+%26+Usher%2C+J%2E+%282009%29%2E+Solving+Geometric+Analogy+Problems+through+Two%2DStage+Analogical+Mapping%2E+Cognitive+Science%2C+33%287%29%2C+1192%2D1231%2E)
33. Miller, G. (1995). [WordNet:A Lexical Database for English](https://md.research.elamur.ai/free/en/papers/miller-1994-wordnet/). Communications of the ACM, 38(11), 39-41.
34. Minsky, M. (1975). A Framework for Representing Knowledge. In P. Winston, The Psychology of Computer Vision. New York: McGraw Hill. [find](https://scholar.google.com/scholar?q=Minsky%2C+M%2E+%281975%29%2E+A+Framework+for+Representing+Knowledge%2E+In+P%2E+Winston%2C+The+Psychology+of+Computer+Vision%2E+New+York%3A+McGraw+Hill%2E)
35. Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Betteridge, J., & Carlson, A. (2015). Never Ending Learning. AAAI. [find](https://scholar.google.com/scholar?q=Mitchell%2C+T%2E%2C+Cohen%2C+W%2E%2C+Hruschka%2C+E%2E%2C+Talukdar%2C+P%2E%2C+Betteridge%2C+J%2E%2C+%26+Carlson%2C+A%2E+%282015%29%2E+Never+Ending+Learning%2E+AAAI%2E)
36. Mueller, E. (2006). Commonsense Reasoning. San Francisco: Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=Mueller%2C+E%2E+%282006%29%2E+Commonsense+Reasoning%2E+San+Francisco%3A+Morgan+Kaufmann%2E)
37. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=Pearl%2C+J%2E+%281988%29%2E+Probabilistic+Reasoning+in+Intelligent+Systems%3A+Networks+of+Plausible+Inference%2E+San+Mateo%2C+CA%3A+Morgan+Kaufmann%2E)
38. Peppas, P. (2008). Belief Revision. In F. Van Harmelen, V. Lifschitz, & B. Porter, Handbook of Knowledge Representation (pp. 317-359). Amsterdam: Elsevier. [find](https://scholar.google.com/scholar?q=Peppas%2C+P%2E+%282008%29%2E+Belief+Revision%2E+In+F%2E+Van+Harmelen%2C+V%2E+Lifschitz%2C+%26+B%2E+Porter%2C+Handbook+of+Knowledge+Representation+%28pp%2E+317%2D359%29%2E+Amsterdam%3A+Elsevier%2E)
39. Pisanelli, D. (2004). Ontologies in Medicine. Amsterdam: IOS Press. [find](https://scholar.google.com/scholar?q=Pisanelli%2C+D%2E+%282004%29%2E+Ontologies+in+Medicine%2E+Amsterdam%3A+IOS+Press%2E)
40. Price, C., Pugh, D., Wilson, M., & Snooke, N. (1995). The FLAME System: Automating electrical failure mode and effects analysis (FEMA). IEEE Reliability and Maintainability Symposium, (pp. 90-95). [find](https://scholar.google.com/scholar?q=Price%2C+C%2E%2C+Pugh%2C+D%2E%2C+Wilson%2C+M%2E%2C+%26+Snooke%2C+N%2E+%281995%29%2E+The+FLAME+System%3A+Automating+electrical+failure+mode+and+effects+analysis+%28FEMA%29%2E+IEEE+Reliability+and+Maintainability+Symposium%2C+%28pp%2E+90%2D95%29%2E)
41. Reiter, R. (2001). Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systemsi. Cambridge, Mass.: MIT Press. [find](https://scholar.google.com/scholar?q=Reiter%2C+R%2E+%282001%29%2E+Knowledge+in+Action%3A+Logical+Foundations+for+Specifying+and+Implementing+Dynamical+Systemsi%2E+Cambridge%2C+Mass%2E%3A+MIT+Press%2E)
42. Russell, B., Torralba, A., Murphy, K., & Freeman, W. (2008). Labelme: a database and web-based tool for image annotation. International Journal of Computer Vision, 77(1-3), 157-173. [find](https://scholar.google.com/scholar?q=Russell%2C+B%2E%2C+Torralba%2C+A%2E%2C+Murphy%2C+K%2E%2C+%26+Freeman%2C+W%2E+%282008%29%2E+Labelme%3A+a+database+and+web%2Dbased+tool+for+image+annotation%2E+International+Journal+of+Computer+Vision%2C+77%281%2D3%29%2C+157%2D173%2E)
43. Schank, R., & Abelson, R. (1977). Scripts, Plans, Goals, and Understanding. Hillsdale, N.J.: Lawrence Erlbaum. [find](https://scholar.google.com/scholar?q=Schank%2C+R%2E%2C+%26+Abelson%2C+R%2E+%281977%29%2E+Scripts%2C+Plans%2C+Goals%2C+and+Understanding%2E+Hillsdale%2C+N%2EJ%2E%3A+Lawrence+Erlbaum%2E)
44. Schubert, L. (2015). Semantic Representation. AAAI. [find](https://scholar.google.com/scholar?q=Schubert%2C+L%2E+%282015%29%2E+Semantic+Representation%2E+AAAI%2E)
45. Shepard, B., Matuszek, C., Fraser, C., Wechtenhiser, W., Crabbe, D., Gungordu, Z. J., . . . Larson, E. (2005). A knowledge-base approach to network security: Applying Cyc in the domain of network risk assessment. Association for the Advancement of Artificial Intelligence, (pp. 1563-1568). [find](https://scholar.google.com/scholar?q=Shepard%2C+B%2E%2C+Matuszek%2C+C%2E%2C+Fraser%2C+C%2E%2C+Wechtenhiser%2C+W%2E%2C+Crabbe%2C+D%2E%2C+Gungordu%2C+Z%2E+J%2E%2C+%2E+%2E+%2E+Larson%2C+E%2E+%282005%29%2E+A+knowledge%2Dbase+approach+to+network+security%3A+Applying+Cyc+in+the+domain+of+network+risk)
46. Surdeanu, M. (2013). Overview of the tac2013 knowledge base population evaluation: English slot filling and temporal slot filling. Proceedings of the Sixth Text Analysis Conference (TAC 2013). [find](https://scholar.google.com/scholar?q=Surdeanu%2C+M%2E+%282013%29%2E+Overview+of+the+tac2013+knowledge+base+population+evaluation%3A+English+slot+filling+and+temporal+slot+filling%2E+Proceedings+of+the+Sixth+Text+Analysis+Conference+%28TAC+2013%29%2E)
47. Winograd, T. (1972). Understanding Natural Language. New York: Academic Press. [find](https://scholar.google.com/scholar?q=Winograd%2C+T%2E+%281972%29%2E+Understanding+Natural+Language%2E+New+York%3A+Academic+Press%2E)
48. Woods, W. (1975). What's in a Link: Foundations for Semantic Networks. In D. Bobrow, & A. Collins, Representation and Understanding: Studies in Cognitive Science. New York: Academic Press. [find](https://scholar.google.com/scholar?q=Woods%2C+W%2E+%281975%29%2E+What%27s+in+a+Link%3A+Foundations+for+Semantic+Networks%2E+In+D%2E+Bobrow%2C+%26+A%2E+Collins%2C+Representation+and+Understanding%3A+Studies+in+Cognitive+Science%2E+New+York%3A+Academic+Press%2E)
49. Wu, W., Li, H., Wang, H., & Zhu, K. Q. (2012). Probase: A probabilistic taxonomy for text understanding. ACM SIGMOD. [find](https://scholar.google.com/scholar?q=Wu%2C+W%2E%2C+Li%2C+H%2E%2C+Wang%2C+H%2E%2C+%26+Zhu%2C+K%2E+Q%2E+%282012%29%2E+Probase%3A+A+probabilistic+taxonomy+for+text+understanding%2E+ACM+SIGMOD%2E)

*Updated 30 October 2026*