Abstract
A high-level associative memory modelling method was developed to explore the realization of associative memory. In the proposed method, two stage procedures are progressively performed to construct a unified associative knowledge network. In the first stage, some direct weighted associative links are created according to original context relations, and in the second stage, dynamic link reduction operations are executed to optimize associative access efficiency. Moreover, two kinds of link reduction strategies are designed including a global link reduction strategy and a dynamic link reduction strategy based on Hebb learning rule. Two independent datasets are considered to examine the performance of proposed modelling method. By means of reasonable performance indices, the experimental results displayed that, about 70% original links can be reduced almost without associative access failure but better total associative access efficiency. Particularly, the dynamic reduction strategy based on Hebb learning rule may achieve better associative access performance.
Article PDF
Avoid common mistakes on your manuscript.
References
A. Singhal, Introducing the knowledge graph: things, not strings, Official Blog of Google, 2012, Available from: https://www.blog.google/products/search/introducing-knowledge-graph-things-not/
R.F. Simmons, Technologies for machine translation, Future Gen. Comput. Syst. 2 (1986), 83–94.
R.F. Simmons, Natural language question-answering systems, Commun. ACM 13 (1970), 15–30.
Y.H. Yu, R.F. Simmons, Truly parallel understanding of text, National Conference on Fifth Artificial Intelligence, Boston, MA, USA, 1990, pp. 996–1001.
L.F. Rau, Extracting company names from text, Proceedings of the Seventh IEEE Conference on Artificial Intelligence Application, IEEE, Miami Beach, FL, USA, 1991, pp. 29–32.
X. Liu, S. Zhang, F. Wei, M. Zhou, Recognizing named entities in tweets, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACM, Portland, Oregon, USA, 2011, pp. 359–367.
R. Socher, D. Chen, C.D. Manning, A.Y. Ng, Reasoning with neural tensor networks for knowledge base completion, Proceedings of the 26th International Conference on Neural Information Processing Systems, ACM, Lake Tahoe, N V, USA, 2013, pp. 926–934.
S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, D.U. Hwang, Complex networks: structure and dynamics, Phys. Rep. 424 (2006), 175–308.
M. Arenas, G. Diaz, A. Fokoue, A. Kementsietsidis K. Srinivas, A principled approach to bridging the gap between graph data and their schemas, Proc. VLDB Endow. 7 (2014), 601–612.
Z.L. Xu, Y.P. Sheng, L.R. He, Y.F. Wang, Review on knowledge graph techniques, J. Univ. Electron. Sci. Technol. China 45 (2016), 589–606.
Q. Liu, Y. Li, H. Duan, Y. Liu, Z.G. Qin, Knowledge graph construction techniques, Journal of Computer Research and Development, 53 (2016), 582–600.
Y. Li, C. Wang, F. Han, J. Han, D. Roth, X. Yan, Mining evidences for named entity disambiguation, Proceedings of the 19th ACM SIGKDD, International Conference on Knowledge discovery and data mining, ACM, Chicago, IL, USA, 2013, pp. 1070–1078.
X. Han, L. Sun, J. Zhao, Collective entity linking in web text: a graph-based method, Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, ACM, Beijing, China, 2011, pp. 765–774.
T.W. Lee, M.S. Lewicki, M. Girolami, T.J. Sejnowski, Blind source separation of more sources than mixtures using overcomplete representations, IEEE Signal Process. Lett. 6 (1999), 87–90.
S.Y. Lu, K.H. Hsu, L.J. Kuo, A semantic service match approach based on WordNet and SWRL rules, IEEE 10th International Conference on e-Business Engineering, IEEE, Coventry, UK, 2013, pp. 419–422.
K. Liu, F. Li, L. Liu, Y. Han, Implementation of a kernel-based Chinese relation extraction system, J. Comput. Res. Dev. 44 (2007), 1406–1411.
A. Carlson, J. Getteridge, R.C. Wang, E.R. Hruschka, T.M. Mitchell, Coupled semi-supervised learning for information extraction, Proceedings of the Third ACM International Conference on Web Search and Data Mining, ACM, New York, NY, USA, 2010, pp. 101–110.
Y.M. Zhang, J.F. Zhou, A trainable method for extracting Chinese entity names and their relations, Proceedings of the Second Workshop on Chinese Language Conjunction with the 38th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Hong Kong, China, 2000, pp. 66–72.
M. Banko, M.J. Cafarella, S. Soderland, M. Broadhead, O. Etzioni, Open information extraction from the web, Proceedings of the 20th International Joint Conference on Artificial Intelligence, ACM, Hyderabad, India, 2007, pp. 2670–2676.
Z.P. Xie, C. Jin, Y. Liu, Personalized knowledge recommendation model based on constructivist learning theory, J. Comput. Res. Dev. 55 (2018), 125–138.
Z.Y. Liu, M.S. Sun, Y.K. Lin, Knowledge representation learning: a review, J. Comput. Res. Dev. 53 (2016), 247–261.
A.N. Michel, J.A. Farrell, Associative memories via artificial neural networks, IEEE Control Syst. Mag. 10 (1990), 6–17.
D.S. Bassett, M.G. Mattar, A network neuroscience of human learning: potential to inform quantitative theories of brain and behavior, Trends Cogn. Sci. 21 (2017), 250–264.
B.Y. Liu, J. Ma, X.F. Li, A topic representation model of ‘Feature Dimensionality Reduction’ text complex network, Data Anal. Knowl. Discov. 1 (2017), 53–61.
K. Subbian, C. Aggarwal, J. Srivastava, Mining influencers using information flows in social streams, ACM Trans. Knowl. Discov. Data 10 (2016), 1–28.
E. Rosenberg, Maximal entropy coverings and the information dimension of a complex network, Phys. Lett. A 381 (2017), 574–580.
Meishi-Baike, Available from: https://www.meishi-baike.com (accessed October 11, 2017).
FoodBK, Available from: https://www.foodbk.com/ (accessed October 12, 2017).
C.H. Wang, Sohu News Data, Available from: https://www.sogou.com/labs/resource/cs.php (accessed November 3, 2017).
Sohu Sports, Available from: https://sports.sohu.com/ (accessed November 5, 2017).
S.Y. Liu, B.C. Li, Z.G. Guo, et al., Review of entity relationship extraction, J. Inform. Eng. Univ. 17 (2016), 541–547.
M. Shao, X. Wu, Y. Fu, Scalable nearest neighbor sparse graph approximation by exploiting graph structure, IEEE Trans. Big Data 2 (2016), 365–380.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
About this article
Cite this article
Xie, Z., Wang, K. & Liu, Y. On Learning Associative Relationship Memory among Knowledge Concepts. Int J Netw Distrib Comput 8, 124–130 (2020). https://doi.org/10.2991/ijndc.k.200515.005
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.2991/ijndc.k.200515.005