Neural-symbolic integration and the Semantic Web
Abstract
Symbolic Systems in Artificial Intelligence which are based on formal logic and deductive reasoning are fundamentally different from Artificial Intelligence systems based on artificial neural networks, such as deep learning approaches. The difference is not only in their inner workings and general approach, but also with respect to capabilities. Neural-symbolic Integration, as a field of study, aims to bridge between the two paradigms. In this paper, we will discuss neural-symbolic integration in its relation to the Semantic Web field, with a focus on promises and possible benefits for both, and report on some current research on the topic.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Combining symbolic and subsymbolic approaches into neuro-symbolic systems shows significant promise by exploiting complementary strengths.
“More recently, a combination of symbolic and subsymbolic approaches based on artificial neural networks, variously known as neuro-symbolic or neural-symbolic (henceforth NS) systems, has shown significant promise in exploiting the complementary strengths of individual approaches [20,28].”
From Explainable AI (XAI): Core Ideas, Techniques, and Solutions (2023) · cites this paper as [28]
The symbolic element of neuro-symbolic systems commonly consists of knowledge representation and reasoning techniques such as ontologies and knowledge graphs.
“The symbolic element of NS systems commonly consists of Knowledge Representation and Reasoning techniques, such as ontologies and knowledge graphs [28,47].”
From Explainable AI (XAI): Core Ideas, Techniques, and Solutions (2023) · cites this paper as [28]
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926 · see citing works
(2013) · Cited by 5,726 · see citing works
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,977 · see citing works
Knowledge Graph Embedding by Translating on Hyperplanes
Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915 · see citing works
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9491 · Cited by 3,749 · see citing works
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Yang, B., Yih, W.-T., He, X., Gao, J., & Deng, L. (2014) · arXiv · doi:10.48550/arxiv.1412.6575 · Cited by 2,252 · see citing works
Complex Embeddings for Simple Link Prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2016) · arXiv · doi:10.48550/arxiv.1606.06357 · Cited by 2,100 · see citing works
Reasoning With Neural Tensor Networks for Knowledge Base Completion
Socher, R., Chen, D., Manning, C. D., & Ng, A. Y. (2013) · Cited by 1,656 · see citing works
(2016) · doi:10.18653/v1/d16-1147 · Cited by 1,035 · see citing works
Visual interpretability for deep learning: a survey
Zhang, Q., & Zhu, S. C. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700808 · Cited by 975 · see citing works
Learning Structured Embeddings of Knowledge Bases
Bordes, A., Weston, J., Collobert, R., & Bengio, Y. (2011) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v25i1.7917 · Cited by 849 · see citing works
Representing Text for Joint Embedding of Text and Knowledge Bases
Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., & Gamon, M. (2015) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1174 · Cited by 846 · see citing works
Representation Learning of Knowledge Graphs with Entity Descriptions
Xie, R., Liu, Z., Jia, J., Luan, H., & Sun, M. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10329 · Cited by 725 · see citing works
Poincaré Embeddings for Learning Hierarchical Representations
Nickel, M., & Kiela, D. (2017) · arXiv · doi:10.48550/arxiv.1705.08039 · Cited by 667 · see citing works
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works
Relation Extraction with Matrix Factorization and Universal Schemas
Riedel, S., Yao, L., McCallum, A., & Marlin, B. M. (2013) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 575 · see citing works
Foundations of Semantic Web Technologies
Hitzler, P., Krötzsch, M., & Rudolph, S. (2009) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420090512 · Cited by 556 · see citing works
Self-citationDARPA's explainable artificial intelligence (XAI) program
Gunning, D. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3308446 · Cited by 546 · see citing works
A Knowledge-Grounded Neural Conversation Model
Ghazvininejad, M., Brockett, C., Chang, M.-W., Dolan, B., Gao, J., Yih, W.-T., & Galley, M. (2018) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v32i1.11977 · Cited by 457 · see citing works
RDF2Vec: RDF Graph Embeddings for Data Mining
Ristoski, P., & Paulheim, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46523-4_30 · Cited by 424 · see citing works
Factorizing YAGO: scalable machine learning for linked data
Nickel, M., Tresp, V., & Kriegel, H.-P. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187874 · Cited by 370 · see citing works
What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
Doran, D., Schulz, S., & Besold, T. R. (2017) · arXiv · doi:10.48550/arxiv.1710.00794 · Cited by 333 · see citing works
Jointly Embedding Knowledge Graphs and Logical Rules
Guo, S., Wang, Q., Wang, L., Wang, B., & Guo, L. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1019 · Cited by 265 · see citing works
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
Das, R., Neelakantan, A., Belanger, D., & McCallum, A. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1013 · Cited by 239 · see citing works
Concept learning in description logics using refinement operators
Lehmann, J., & Hitzler, P. (2009) · Machine Learning · doi:10.1007/s10994-009-5146-2 · Cited by 196 · see citing works
Self-citationTyped Tensor Decomposition of Knowledge Bases for Relation Extraction
Chang, K.-W., Yih, W.-T., Yang, B., & Meek, C. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1165 · Cited by 190 · see citing works
Representation Tradeoffs for Hyperbolic EmbeddingsPreprint
De, C., Gu, A., Ré, C., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.03329 · Cited by 189 · see citing works
Logic Tensor Networks for Semantic Image Interpretation
Donadello, I., Serafini, L., & d'Avila Garcez, A. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/221 · Cited by 179 · see citing works
Garcez, A. D., Gori, M., Lamb, L. C., Serafini, L., Spranger, M., & Tran, S. N. (2019) · arXiv · doi:10.48550/arxiv.1905.06088 · Cited by 175 · see citing works
Text-enhanced representation learning for knowledge graph
Wang, Z., & Li, J. (2016) · International Joint Conference on Artificial Intelligence · Cited by 170 · see citing works
On The Role of Knowledge Graphs in Explainable AI
Lécué, F. (2020) · PROFILES/SEMEX@ISWC · doi:10.3233/sw-190374 · Cited by 165 · see citing works
Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary
Asai, M., & Fukunaga, A. S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12077 · Cited by 115 · see citing works
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text DescriptionsPreprint
Xiao, H., Huang, M., & Zhu, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.04835 · Cited by 115 · see citing works
Perspectives of Neural-Symbolic Integration
Hammer, B., & Hitzler, P. (2007) · Studies in computational intelligence · doi:10.1007/978-3-540-73954-8 · Cited by 94 · see citing works
Self-citationHitzler, P., & van Harmelen, F. (2010) · Semantic Web · doi:10.3233/sw-2010-0010 · Cited by 92 · see citing works
Self-citationPhan, N., Dou, D., Wang, H., et al. (2016) · Information Sciences · doi:10.1016/j.ins.2016.08.038 · Cited by 81 · see citing works
Knowledge Graph Embeddings with node2vec for Item Recommendation
Palumbo, E., Rizzo, G. F., Raphaël Troncy, A., Baralis, E., Osella, M., & Ferro, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-98192-5_22 · Cited by 75 · see citing works
Learning and Reasoning with Logic Tensor Networks
Serafini, L., & Garcez, A. D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49130-1_25 · Cited by 74 · see citing works
Efficient Concept Induction for Description Logics
Sarker, M. K., & Hitzler, P. (2019) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v33i01.33013036 · Cited by 32 · see citing works
Self-citationMachine Learning for the Semantic Web: Lessons learnt and next research directions
d’Amato, C. (2020) · Semantic Web · doi:10.3233/sw-200388 · Cited by 32 · see citing works
A Fully Connectionist Model Generator for Covered First-Order Logic Programs
Bader, S., Hitzler, P., Hölldobler, S., Witzel, A., & Veloso, M. (2007) · UvA-DARE (University of Amsterdam) · Cited by 31 · see citing works
Self-citationDeep learning for noise-tolerant RDFS reasoning
Makni, B., & Hendler, J. (2019) · Semantic Web · doi:10.3233/sw-190363 · Cited by 31 · see citing works
Explaining Trained Neural Networks with Semantic Web Technologies: First StepsPreprint
Sarker, M. K., Xie, N., Doran, D., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.04324 · Cited by 28 · see citing works
Self-citationHybrid reasoning in knowledge graphs: Combing symbolic reasoning and statistical reasoning
Li, W., Qi, G., & Ji, Q. (2020) · Semantic Web · doi:10.3233/sw-190375 · Cited by 23 · see citing works
Ontology-based semantic search on the Web and its combination with the power of inductive reasoning
d’Amato, C., Fanizzi, N., Fazzinga, B., et al. (2012) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-012-9309-7 · Cited by 19 · see citing works
Rietveld, L., van Beek, W., Hoekstra, R., et al. (2017) · Semantic Web · doi:10.3233/sw-170256 · Cited by 13 · see citing works
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
Yin, W., Yaghoobzadeh, Y., & Schütze, H. (2018) · Open access LMU (Ludwid Maxmilian's Universitat Munchen) · doi:10.5282/ubm/epub.61860 · Cited by 9 · see citing works
A Note on the Relationships between Logic Programs and Neural Networks
Hitzler, P., & Seda, A. K. (2000) · Electronic workshops in computing · doi:10.14236/ewic/iwfm2000.2 · Cited by 7 · see citing works
Self-citationInductive Classification of Semantically Annotated Resources through Reduced Coulomb Energy Networks
Fanizzi, N., d’Amato, C., & Esposito, F. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-593-3.ch013 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Explainable AI (XAI): Core Ideas, Techniques, and Solutions
Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., Qian, B., Wen, Z., Shah, T., Morgan, G., et al. (2023) · ACM Computing Surveys · doi:10.1145/3561048 · Cited by 1,386 · see citing works
- Authors
- Hitzler, P., Bianchi, F., Ebrahimi, M., & Sarker, M. K.
- Affiliations
- Kansas State University (US)
- University of Milan (IT)
- Corresponding author
- Hitzler, Pascal
- Venue
- Semantic Web
- Publisher
- SAGE Publications
- Published
- 2020-01-31
- Volume
- 11
- Issue
- 1
- Pages
- 3-11
- ISSN
- 1570-0844 (ISSN-L)
- 2210-4968
- OpenAlex
- W2979669231
- Cited by
- 70
- References
- 53
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 4 · 2023: 12 · 2022: 12 · 2021: 15 · 2020: 8 · 2019: 2
- FWCI
- 4.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Semantic Web (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
- Keywords
- neural-symbolic integration, symbolic reasoning, deductive reasoning, artificial neural networks, deep learning, hybrid AI models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
