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Cognitive Computation

30-08-2024 | Research

PrimeNet: A Framework for Commonsense Knowledge Representation and Reasoning Based on Conceptual Primitives

Authors: Qian Liu, Sooji Han, Erik Cambria, Yang Li, Kenneth Kwok

Published in: Cognitive Computation

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Abstract

Commonsense knowledge acquisition and representation is a core topic in artificial intelligence (AI), which is crucial for building more sophisticated and human-like AI systems. However, existing commonsense knowledge bases organize facts in an isolated manner like bag of facts, lacking the cognitive-level connections that humans commonly possess. People have the ability to efficiently organize vast amounts of knowledge by linking or generalizing concepts using a limited set of conceptual primitives that serve as the fundamental building blocks of reasoning. These conceptual primitives are basic, foundational elements of thought that humans use to make sense of the world. By combining and recombining these primitives, people can construct complex ideas, solve problems, and understand new concepts. To emulate this cognitive mechanism, we design a new commonsense knowledge base, termed PrimeNet, organized in a three-layer structure: a small core of conceptual primitives (e.g., FOOD), a bigger set of concepts that connect to such primitives (e.g., fruit), and an even larger layer of entities connecting to the concepts (e.g., banana). First, we collect commonsense knowledge and employ a gradual expansion strategy for knowledge integration. After refinement, PrimeNet contains 6 million edges between 2 million nodes, with 34 different types of relations. Then, we design a new conceptualization method by leveraging a probabilistic taxonomy, to build the concept layer of PrimeNet. Finally, we conduct primitive detection to build the primitive layer, where a lexical substitution task is used to identify related concepts, and large language models are employed to generate a rational primitive to label each concept cluster as well as verify the primitive detection process.

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Footnotes
2
We use the ConceptNet version 5.7.0, which is available at https://​github.​com/​commonsense/​conceptnet5/​wiki/​Downloads.
 
3
We use the DBpedia version 2022.09.01, which is available at https://​www.​dbpedia.​org/​resources/​.
 
4
The project description and mappings are available on https://​github.​com/​usc-isi-i2/​cskg. Please refer to o Ilievski et al. [7] for more details on processing individual sources, performing node resolution, and constructing mappings.
 
8
In our experiment, the used pretrained model is all-mpnet-base-v2 Having undergone pretraining on over 1 billion sentence pairs, this model is capable of mapping input text to a 768-dimensional vector space, ideal for tasks such as clustering or semantic search. Further details can be found at: https://​huggingface.​co/​sentence-transformers/​all-mpnet-base-v2.
 
10
Performances of compared knowledge bases are reported by [36], which are evaluated through crowdsourcing on the Amazon Mechanical Turk platform.
 
12
We use the Text8Corpus which is available in Gensim: https://​github.​com/​RaRe-Technologies/​gensim-data, and the CBOW model for training: https://​code.​google.​com/​archive/​p/​word2vec/​
 
Literature
1.
Cambria E, Hussain A, Havasi C, Eckl C. Common sense computing: from the society of mind to digital intuition and beyond. In: Biometric ID management and multimodal communication. Lecture Notes in Computer Science; 2009. vol. 5707, pp. 252–9.
2.
Lenat DB. CYC: a large-scale investment in knowledge infrastructure. Commun ACM. 1995;38(11):32–8.CrossRef
3.
Baker CF, Fillmore CJ, Lowe JB. The Berkeley FrameNet project. In: Proceedings of annual meeting of the Association for Computational Linguistics, ACL. 1998. pp. 86–90.
4.
Speer R, Chin J, Havasi C. Conceptnet 5.5: an open multilingual graph of general knowledge. In: Proceedings of AAAI conference on artificial intelligence (AAAI). 2017. pp. 4444–51.
5.
Zhang H, Khashabi D, Song Y, Roth D. Transomcs: from linguistic graphs to commonsense knowledge. In: Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI. 2020. pp. 4004–10.
6.
Sap M, Le Bras R, Allaway E, Bhagavatula C, Lourie N, Rashkin H, Roof B, Smith NA, Choi Y. Atomic: an atlas of machine commonsense for if-then reasoning. In: Proceedings of the AAAI conference on artificial intelligence. 2019. vol. 33, pp. 3027–35.
7.
Ilievski F, Szekely PA, Zhang B. CSKG: the commonsense knowledge graph. In: Proceedings of the semantic web - 18th international conference, ESWC. Lecture Notes in Computer Science; 2021. vol. 12731, pp. 680–96.
8.
Liu J, Chen T, Wang C, Liang J, Chen L, Xiao Y, Chen Y, Jin K. Vocsk: verb-oriented commonsense knowledge mining with taxonomy-guided induction. Artif Intell. 2022;310: 103744.MathSciNetCrossRef
9.
Cambria E, Mao R, Chen M, Wang Z, Ho S-B. Seven pillars for the future of artificial intelligence. IEEE Intell Syst. 2023;38(6):62–9.CrossRef
10.
Zechmeister EB, Chronis AM, Cull WL, D’Anna CA, Healy NA. Growth of a functionally important lexicon. J Read Behav. 1995;27(2):201–12.CrossRef
11.
Jackendoff R. Toward an explanatory semantic representation. Linguist Inq. 1976;7(1):89–150.
12.
Minsky M. A framework for representing knowledge. Cambridge: MIT; 1974.
13.
Rumelhart DE, Ortony A. The representation of knowledge in memory. Schooling and the acquisition of knowledge. 1977;99:135.
14.
Schank RC. Conceptual dependency: a theory of natural language understanding. Cogn Psychol. 1972;3(4):552–631.CrossRef
15.
Wierzbicka A. Semantics: primes and universals: primes and universals. UK: Oxford University Press; 1996.CrossRef
16.
Ge M, Mao R, Cambria E. Explainable metaphor identification inspired by conceptual metaphor theory. Proc AAAI Conf Artif Intell. 2022;36(10):10681–9.
17.
Mao R, Li X, He K, Ge M, Cambria E. MetaPro Online: a computational metaphor processing online system. In: Proceedings of the annual meeting of the association for computational linguistics (Volume 3: System Demonstrations). 2023. pp. 127–35.
18.
Mao R, Du K, Ma Y, Zhu L, Cambria E. Discovering the cognition behind language: financial metaphor analysis with MetaPro. In: 2023 IEEE International Conference on Data Mining (ICDM). IEEE; 2023. pp. 1211–16.
19.
Cambria E, Zhang X, Mao R, Chen M, Kwok K. SenticNet 8: fusing emotion AI and commonsense AI for interpretable, trustworthy, and explainable affective computing. In: International conference on Human-Computer Interaction (HCII). 2024.
20.
Zhang H, Liu X, Pan H, Song Y, Leung CW. ASER: a large-scale eventuality knowledge graph. In: Proceedings of The Web Conference 2020, WWW. 2020. pp. 201–11.
21.
Wu W, Li H, Wang H, Zhu KQ. Probase: a probabilistic taxonomy for text understanding. In: Proceedings of the ACM SIGMOD international conference on management of data, SIGMOD. 2012. pp. 481–92.
22.
Wang Z, Wang H, Wen J, Xiao Y. An inference approach to basic level of categorization. In: Proceedings of the ACM international Conference on Information and Knowledge Management, CIKM. 2015. pp. 653–62.
23.
24.
Jackendoff RS, et al. Semantics and cognition. Cambridge, Massachusetts: The MIT Press; 1983.
25.
Pesina S, Solonchak T. Semantic primitives and conceptual focus. Procedia Soc Behav Sci. 2015;192:339–45.CrossRef
26.
Piaget J, Cook M, et al. The origins of intelligence in children, vol. 8. New York: International Universities Press; 1952.CrossRef
27.
Winograd T. Towards a procedural understanding of semantics. Revue internationale de philosophie. 1976;260–303.
28.
Bobrow DG, Norman DA. Some principles of memory schemata. In: Representation and understanding. Morgan Kaufmann, San Diego; 1975. pp. 131–49.
29.
Johnson M. The body in the mind: the bodily basis of meaning, imagination, and reason. J Aesthetics and Art Criticism. 1989;47(4).
30.
31.
West M. Developing high quality data models. Morgan Kaufmann Publishers Inc., 340 Pine Street, Sixth FloorSan FranciscoCAUnited States; 2011.
32.
Wachowiak L, Gromann D. Systematic analysis of image schemas in natural language through explainable multilingual neural language processing. In: Proceedings of the international conference on computational linguistics, COLING. 2022. pp. 5571–81.
33.
Miller GA. Wordnet: a lexical database for english. Commun ACM. 1995;38:39–41.CrossRef
34.
Kipfer BA. Roget’s 21st century thesaurus in dictionary form. 3rd ed. New York, NY: Bantam Dell; 2006.
35.
Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives ZG. DBpedia: a nucleus for a web of open data. In: Proceedings of the semantic web, 6th international semantic web conference, 2nd Asian Semantic Web Conference. 2007. vol. 4825, pp. 722–35.
36.
Hwang JD, Bhagavatula C, Bras RL, Da J, Sakaguchi K, Bosselut A, Choi Y. Comet-atomic 2020: on symbolic and neural commonsense knowledge graphs. In: Proceedings of the AAAI conference on artificial intelligence. 2020.
37.
Krishna R, Zhu Y, Groth O, Johnson J, Hata K, Kravitz J, Chen S, Kalantidis Y, Li L, Shamma DA, Bernstein MS, Fei-Fei L. Visual genome: connecting language and vision using crowdsourced dense image annotations. Int J Comput Vision. 2017;123(1):32–73.MathSciNetCrossRef
38.
Reimers N, Gurevych I. Sentence-bert: sentence embeddings using siamese bert-networks. In: Proceedings of the conference on empirical methods in natural language processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP. 2019. pp. 3980–90.
39.
Cambria E, Mao R, Han S, Liu Q. Sentic parser: a graph-based approach to concept extraction for sentiment analysis. In: Proceedings of ICDM workshops. 2022. pp. 413–20.
40.
Guarino N. Formal ontology, conceptual analysis and knowledge representation. Int J Hum Comput Stud. 1995;43(5–6):625–40.CrossRef
41.
42.
Faruqui M, Dodge J, Jauhar SK, Dyer C, Hovy EH, Smith NA. Retrofitting word vectors to semantic lexicons. In: Proceedings of the conference of the North American chapter of the association for computational linguistics: human language technologies. 2015. pp. 1606–15.
43.
Liu Q, Huang H, Zhang G, Gao Y, Xuan J, Lu J. Semantic structure-based word embedding by incorporating concept convergence and word divergence. In: Proceedings of the AAAI conference on artificial intelligence. 2018. pp. 5261–8.
44.
Myers JL, Well AD. Research design & statistical analysis. New York: Routledge; 1995.
45.
Yang D, Powers DM. Measuring semantic similarity in the taxonomy of WordNet. Australia: Australian Computer Society; 2005.
46.
Bruni E, Boleda G, Baroni M, Tran N. Distributional semantics in technicolor. In: Proceedings of the annual meeting of the Association for Computational Linguistics, ACL. 2012. pp. 136–45.
47.
Rubenstein H, Goodenough JB. Contextual correlates of synonymy. Commun ACM. 1965;8(10):627–33.CrossRef
48.
Halawi G, Dror G, Gabrilovich E, Koren Y. Large-scale learning of word relatedness with constraints. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining. 2012. pp. 1406–14.
49.
Hill F, Reichart R, Korhonen A. Simlex-999: evaluating semantic models with (genuine) similarity estimation. Comput Linguist. 2015;41(4):665–95.MathSciNetCrossRef
50.
Gerz D, Vulic I, Hill F, Reichart R, Korhonen A. Simverb-3500: a large-scale evaluation set of verb similarity. In: Proceedings of the conference on empirical methods in natural language processing, EMNLP. 2016. pp. 2173–82.
51.
Baker S, Reichart R, Korhonen A. An unsupervised model for instance level subcategorization acquisition. In: Proceedings of the conference on empirical methods in natural language processing, EMNLP. 2014. pp. 278–89.
52.
Finkelstein L, Gabrilovich E, Matias Y, Rivlin E, Solan Z, Wolfman G, Ruppin E. Placing search in context: the concept revisited. In: Proceedings of the international World Wide Web Conference, WWW. 2001. pp. 406–14.
53.
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. In: Proceedings of advances in neural information processing systems. 2013. pp. 3111–9.
54.
Pennington J, Socher R, Manning CD. Glove: global vectors for word representation. In: Proceedings of the conference on Empirical Methods in Natural Language Processing, EMNLP. 2014. pp. 1532–43.
55.
Liu Q, Geng X, Wang Y, Cambria E, Jiang D. Disentangled retrieval and reasoning for implicit question answering. IEEE Trans Neural Netw Learn Syst. 2024;35(6):7804–15.CrossRef
56.
Ilievski F, Oltramari A, Ma K, Zhang B, McGuinness DL, Szekely PA. Dimensions of commonsense knowledge. Knowl-Based Syst. 2021;229:107347.CrossRef
57.
Ma K, Ilievski F, Francis J, Bisk Y, Nyberg E, Oltramari A. Knowledge-driven data construction for zero-shot evaluation in commonsense question answering. In: Proceedings of thirty-fifth AAAI conference on artificial intelligence, AAAI. 2021. pp. 13507–15.
58.
Shwartz V, West P, Bras RL, Bhagavatula C, Choi Y. Unsupervised commonsense question answering with self-talk. In: Proceedings of the 2020 conference on Empirical Methods in Natural Language Processing, EMNLP. 2020. pp. 4615–29.
59.
Banerjee P, Baral C. Self-supervised knowledge triplet learning for zero-shot question answering. In: Proceedings of the conference on Empirical Methods in Natural Language Processing, EMNLP. 2020. pp. 151–62.
60.
Levesque HJ. The winograd schema challenge. In: Logical formalizations of commonsense reasoning, Papers from the 2011 AAAI Spring Symposium, Technical Report SS-11-06. 2011. pp. 1–1.
61.
Bhagavatula C, Bras RL, Malaviya C, Sakaguchi K, Holtzman A, Rashkin H, Downey D, Yih W, Choi Y. Abductive commonsense reasoning. In: Proceedings of International Conference on Learning Representations, ICLR. 2020.
62.
Talmor A, Herzig J, Lourie N, Berant J. Commonsenseqa: a question answering challenge targeting commonsense knowledge. In: Proceedings of the conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT. 2019. pp. 4149–58.
63.
Bisk Y, Zellers R, Bras RL, Gao J, Choi Y. PIQA: reasoning about physical commonsense in natural language. In: Proceedings of the thirty-fourth AAAI conference on Artificial Intelligence, AAAI. 2020. pp. 7432–9.
64.
Sap M, Rashkin H, Chen D, Bras RL, Choi Y. Social iqa: commonsense reasoning about social interactions. In: Proceedings of the conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP. 2019. pp. 4462–72.
65.
Sakaguchi K, Bras RL, Bhagavatula C, Choi Y. Winogrande: an adversarial winograd schema challenge at scale. In: Proceedings of the thirty-fourth AAAI conference on Artificial Intelligence, AAAI. 2020. pp. 8732–40.
66.
Singh P, Lin T, Mueller ET, Lim G, Perkins T, Zhu WL. Open mind common sense: knowledge acquisition from the general public. In: On the move to meaningful internet systems. Lecture Notes in Computer Science; 2002. vol. 2519, pp. 1223–37.
67.
Chklovski T. Learner: a system for acquiring commonsense knowledge by analogy. In: Gennari JH, Porter BW, Gil Y, editors. Proceedings of the 2nd international conference on knowledge capture (K-CAP 2003). 2003. pp. 4–12.
68.
Ahn L, Kedia M, Blum M. Verbosity: a game for collecting common-sense facts. In: Proceedings of the 2006 conference on human factors in computing systems, CHI. 2006. pp. 75–8.
69.
Kuo Y, Lee J, Chiang K, Wang R, Shen E, Chan C, Hsu JY. Community-based game design: experiments on social games for commonsense data collection. In: Proceedings of the ACM SIGKDD workshop on human computation. 2009. pp. 15–22.
70.
Gangemi A, Guarino N, Masolo C, Oltramari A, Schneider L. Sweetening ontologies with DOLCE. In: Knowledge engineering and knowledge management. Ontologies and the Semantic Web, 13th International Conference, EKAW. Lecture Notes in Computer Science; 2002. vol. 2473, pp. 166–81.
71.
Bollacker KD, Evans C, Paritosh PK, Sturge T, Taylor J. Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the ACM SIGMOD international conference on management of data, SIGMOD. 2008. pp. 1247–50.
72.
Singhal A. Official google blog: introducing the knowledge graph: things, not strings. 2012.
73.
Dodge E, Hong J, Stickles E. MetaNet: deep semantic automatic metaphor analysis. In: Proceedings of the third workshop on metaphor in NLP. 2015. pp. 40–9.
74.
Schuler KK. VerbNet: a broad-coverage, comprehensive verb lexicon. University of Pennsylvania, Philadelphia, PA, United States; 2005.
75.
Palmer M, Kingsbury PR, Gildea D. The proposition bank: an annotated corpus of semantic roles. Comput Linguist. 2005;31(1):71–106.CrossRef
76.
Vrandecic D, Krötzsch M. Wikidata: a free collaborative knowledgebase. Commun ACM. 2014;57(10):78–85.CrossRef
77.
Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes PN, Hellmann S, Morsey M, Kleef P, Auer S, Bizer C. Dbpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web. 2015;6(2):167–95.CrossRef
78.
Young T, Cambria E, Chaturvedi I, Zhou H, Biswas S, Huang M. Augmenting end-to-end dialogue systems with commonsense knowledge. In: Proceedings of the Thirty-Second AAAI conference on artificial intelligence. 2018. pp. 4970–7.
79.
Mitchell T, Fredkin E. Never-ending language learning. In: 2014 IEEE International conference on big data (Big Data). 2014. pp. 1–1.
80.
Tandon N, Melo G, Suchanek FM, Weikum G. Webchild: harvesting and organizing commonsense knowledge from the web. In: Proceedings of the 7th ACM international conference on web search and data mining, WSDM. 2014. pp. 523–32.
81.
Ji L, Wang Y, Shi B, Zhang D, Wang Z, Yan J. Microsoft concept graph: mining semantic concepts for short text understanding. Data Intelligence. 2019;1(3):238–70.CrossRef
82.
Navigli R, Ponzetto SP. Babelnet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif Intell. 2012;193:217–50.MathSciNetCrossRef
83.
Shen X, Wu S, Xia R. Dense-atomic: towards densely-connected ATOMIC with high knowledge coverage and massive multi-hop paths. In: Proceedings of the annual meeting of the Association for Computational Linguistics, ACL. 2023. pp. 13292–305.
84.
Suchanek FM, Kasneci G, Weikum G. Yago: a core of semantic knowledge. In: Proceedings of the international conference on the World Wide Web, WWW. 2007. pp. 697–706.
85.
Bouraoui Z, Konieczny S, Ma T, Schwind N, Varzinczak I. Region-based merging of open-domain terminological knowledge. In: Proceedings of the international conference on principles of knowledge representation and reasoning, KR. 2022. pp. 81–90.
86.
AlKhamissi B, Li M, Celikyilmaz A, Diab MT, Ghazvininejad M. A review on language models as knowledge bases. CoRR abs/2204.06031. 2022.
87.
Bhargava P, Ng V. Commonsense knowledge reasoning and generation with pre-trained language models: a survey. In: Proceedings of thirty-sixth AAAI conference on Artificial Intelligence, AAAI. 2022. pp. 12317–25.
88.
Radford A, Narasimhan K, Salimans T, Sutskever I, et al. Improving language understanding by generative pre-training. Open AI Preprint. 2018.
89.
Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al. Language models are unsupervised multitask learners. OpenAI blog. 2019;1(8):9.
90.
Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler DM, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D. Language models are few-shot learners. In: Proceedings of advances in Neural Information Processing Systems, NeurIPS. 2020.
91.
Yao L, Mao C, Luo Y. KG-BERT: BERT for knowledge graph completion. CoRR abs/1909.03193. 2019.
92.
Fang T, Wang W, Choi S, Hao S, Zhang H, Song Y, He B. Benchmarking commonsense knowledge base population with an effective evaluation dataset. In: Proceedings of the conference on Empirical Methods in Natural Language Processing, EMNLP. 2021. pp. 8949–64.
93.
Fang T, Do QV, Zhang H, Song Y, Wong GY, See S. Pseudoreasoner: leveraging pseudo labels for commonsense knowledge base population. In: Findings of the association for computational linguistics, EMNLP. 2022. pp. 3379–94.
94.
Bosselut A, Rashkin H, Sap M, Malaviya C, Celikyilmaz A, Choi Y. COMET: commonsense transformers for automatic knowledge graph construction. In: Proceedings of the 57th conference of the Association for Computational Linguistics, ACL. 2019. pp. 4762–79.
95.
Petroni F, Rocktäschel T, Riedel S, Lewis PSH, Bakhtin A, Wu Y, Miller AH. Language models as knowledge bases? In: Proceedings of the conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP. 2019. pp. 2463–73.
96.
Petroni F, Lewis PSH, Piktus A, Rocktäschel T, Wu Y, Miller AH, Riedel S. How context affects language models’ factual predictions. In: Proceedings of conference on automated knowledge base construction, AKBC0. 2020.
97.
West P, Bhagavatula C, Hessel J, Hwang JD, Jiang L, Bras RL, Lu X, Welleck S, Choi Y. Symbolic knowledge distillation: from general language models to commonsense models. In: Proceedings of the conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL. 2022. pp. 4602–25.
98.
Goel V, Navarrete G, Noveck IA, Prado J. The reasoning brain: the interplay between cognitive neuroscience and theories of reasoning. Frontiers Media SA. 2017.
99.
Salmon MH. Introduction to logic and critical thinking. 1989.
100.
Clark P, Tafjord O, Richardson K. Transformers as soft reasoners over language. In: Proceedings of IJCAI. 2020. pp. 3882–90.
101.
Yang Z, Dong L, Du X, Cheng H, Cambria E, Liu X, Gao J, Wei F. Language models as inductive reasoners. In: Proceedings of EACL. 2024. pp. 209–225
102.
Geva M, Khashabi D, Segal E, Khot T, Roth D, Berant J. Did Aristotle use a laptop? A question answering benchmark with implicit reasoning strategies. Trans Assoc Comput Linguist. 2021;9:346–61.CrossRef
103.
Rajpurkar P, Zhang J, Lopyrev K, Liang P. SQuAD: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 conference on empirical methods in natural language processing. 2016. pp. 2383–92.
104.
Joshi M, Choi E, Weld D, Zettlemoyer L. TriviaQA: a large scale distantly supervised challenge dataset for reading comprehension. In: Proceedings of the annual meeting of the association for computational linguistics, ACL. 2017. pp. 1601–11.
105.
Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ. Building machines that learn and think like people. Behav Brain Sci. 2017;40:253.
106.
Musen MA, Lei J. Of brittleness and bottlenecks: challenges in the creation of pattern-recognition and expert-system models. In: Machine intelligence and pattern recognition. 1988. vol. 7, pp. 335–52.
107.
Li W, Zhu L, Mao R, Cambria E. SKIER: a symbolic knowledge integrated model for conversational emotion recognition. In: Proceedings of the AAAI conference on artificial intelligence. 2023. pp. 13121–9.
108.
Smolensky P, McCoy R, Fernandez R, Goldrick M, Gao J. Neurocompositional computing: from the central paradox of cognition to a new generation of ai systems. AI Mag. 2022;43(3):308–22.
Metadata
Title
PrimeNet: A Framework for Commonsense Knowledge Representation and Reasoning Based on Conceptual Primitives
Authors
Qian Liu
Sooji Han
Erik Cambria
Yang Li
Kenneth Kwok
Publication date
30-08-2024
Publisher
Springer US
Published in
Cognitive Computation
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-024-10345-6

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