1 |
蔡祖铭. 北京: 美国军事战略研究[M]. 北京: 军事科学出版社, 1993.
|
|
CAI Z M . Research on American military strategy[M]. Beijing: Military Science Press, 1993.
|
2 |
潘星, 张国忠, 张跃东, 等. 工程弹性系统与系统弹性理论研究综述[J]. 系统工程与电子技术, 2019, 41 (9): 2006- 2015.
|
|
PAN X , ZHANG G Z , ZHANG Y D , et al. Review of engineered resilient systems and system resilience theory[J]. Systems Engineering and Electronics, 2019, 41 (9): 2006- 2015.
|
3 |
BRATMAN M . Intention, plans, and practical reason[M]. Cambridge: Harvard University Press, 1987.
|
4 |
高新民. "BDI模型"与人工智能建模的心灵哲学[J]. 上海师范大学学报(哲学社会科学版), 2019, 48 (5): 99- 111.
|
|
GAO X M . "BDI model" and the philosophical thinking of artificial intelligence modeling[J]. Journal of Shanghai Normal University (Philosophy and Social Sciences Edition), 2019, 48 (5): 99- 111.
|
5 |
VDOGA B , ARP B , RHB B , et al. Reasoning in BDI agents using Toulmin's argumentation model[J]. Theoretical Computer Science, 2020, 805, 76- 91.
doi: 10.1016/j.tcs.2019.10.026
|
6 |
LOWE D. A service-based approach to force design, integration and analysis[C]//Proc. of the INCOSE International Symposium, 2017, 27(1): 1506-1519.
|
7 |
漆桂林, 高桓, 吴天星. 知识图谱研究进展[J]. 情报工程, 2017, 3 (1): 4- 25.
|
|
QI G L , GAO H , WU T X . The research advances of knowledge graph[J]. Technology Intelligence Engineering, 2017, 3 (1): 4- 25.
|
8 |
LIU W J, ZHOU P, ZHAO Z, et al. K-BERT: enabling language representation with knowledge graph[C]//Proc. of the AAAI Confernce on Artificial Intelligence, 2020: 2901-2908.
|
9 |
ZHANG S, TAY Y, YAO L, et al. Quaternion knowledge graph embeddings[C]//Proc. of the 33rd International Conference on Neural Information Processing Systems, 2019: 2735- 2745.
|
10 |
JIANG T W, ZHAO T, QIN B, et al. The role of "condition" a novel scientific knowledge graph representation and construction model[C]//Proc. of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019: 1634-1642.
|
11 |
李涓子, 赵军, 陈华钧, 等. 知识图谱发展报告[R]. 北京: 中国中文信息学会语言与知识计算专委会, 2018: 1-11.
|
|
LI J Z, ZHAO J, CHEN H J, et al. Knowledge graph development report[R]. Beijing: Special Committee on Language and Knowledge Computing of Chinese Information Society of China, 2018: 1-11.
|
12 |
WU X D, WU J, FU X Y, et al. Automatic knowledge graph construction: a report on the 2019 ICDM/ICBK contest[C]//Proc. of the IEEE International Conference on Data Mining, 2019: 1540-1545.
|
13 |
WANG Y Q, MA F L, GAO J. Efficient knowledge graph validation via cross-graph representation learning[C]//Proc. of the 29th ACM International Conference on Information & Know-ledge Management, 2020: 1595-1604.
|
14 |
WEI Z P, SU J L, WANG Y, et al. A novel hierarchical binary tagging framework for joint extraction of entities and relations[EB/OL]. [2021-01-03]. arxiv. org/pdf/1909.03227v1.
|
15 |
LIN J J , ZHAO Y Z , HUANG W Y , et al. Domain knowledge graph-based research progress of knowledge representation[J]. Neural Computing and Applications, 2020, 33 (2): 681- 690.
|
16 |
LI F L, CHEN H, XU G, et al. AliMeKG: domain knowledge graph construction and application in e-commerce[C]//Proc. of the 29th ACM International Conference on Information & Knowledge Management, 2020: 2581-2588.
|
17 |
ZHAO H X , PAN Y L , YANG F . Research on information extraction of technical documents and construction of domain knowledge graph[J]. IEEE Access, 2020, 8, 168087- 168098.
doi: 10.1109/ACCESS.2020.3024070
|
18 |
QI P , SUN Y , LUO H , et al. Scratch-DKG: a framework for constructing scratch domain knowledge graph[J]. IEEE Trans.on Emerging Topics in Computing, 2022, 10 (1): 110- 185.
|
19 |
DOU J H , QIN J Y , JIN Z X , et al. Knowledge graph based on domain ontology and natural language processing technology for Chinese intangible cultural heritage[J]. Journal of Visual Languages & Computing, 2018, 48, 19- 28.
|
20 |
徐增林, 盛泳潘, 贺丽荣, 等. 知识图谱技术综述[J]. 电子科技大学报, 2016, 45 (4): 589- 606.
|
|
XU Z L , SHENG Y P , HE L R , et al. Review on knowledge graph techniques[J]. Journal of University of Electronic Science and Technology of China, 2016, 45 (4): 589- 606.
|
21 |
CHEN X J , JIA S B , XIANG Y . A review: knowledge reasoning over knowledge graph[J]. Expert Systems with Applications, 2020, 141, 112948.
|
22 |
PAULHEIM H . Knowledge graph refinement: a survey of ap proaches and evaluation methods[J]. Semantic Web, 2017, 8 (3): 489- 508.
|
23 |
LIN Y K, LIU Z Y, SUN M S, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2015: 2181- 2187.
|
24 |
JI G L, HE S Z, XU L H, et al. Knowledge graph embedding via dynamic mapping matrix[C]//Proc. of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015: 687-696.
|
25 |
XIAO H, HUANG M L, ZHU X. TransG: a generative model for knowledge graph embedding[C]//Proc. of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 2316-2325.
|
26 |
SUN Z Q, DENG Z H, NIE J Y, et al. RotatE: knowledge graph embedding by relational rotation in complex space[C]//Proc. of the International Conference on Learning Representations, 2018.
|
27 |
KAZEMI S M, POOLE D. SimplE embedding for link prediction in knowledge graphs[C]//Proc. of the 32nd International Conference on Neural Information Processing Systems, 2018: 4289-4300.
|
28 |
BALAZEVIC I, ALLEN C, HOSPEDALES T. TuckER: tensor factorization for knowledge graph completion[C]//Proc. of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 5188-5197.
|
29 |
DETTMERS T, MINERVINI P, STENETORP P, et al. Convolutional 2D knowledge graph embeddings[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2018: 1811-1818.
|
30 |
NGUYEN T D, NGUYEN D Q, PHUNG D. A novel embedding model for knowledge base completion based on convolutional neural network[C]//Proc. of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018: 327-333.
|
31 |
BALAŽEVI C ' I, ALLEN C, HOSPEDALES T M. Hypernetwork knowledge graph embeddings[C]//Proc. of the International Conference on Artificial Neural Networks, 2019: 553- 565.
|
32 |
SHANG C, TANG Y, HUANG J, et al. End-to-end structure-aware convolutional networks for knowledge base completion[C]//Proc. of the 33rd AAAI Conference on Artificial Intelligence, 2019: 3060-3067.
|
33 |
NATHANI D, CHAUHAN J, SHARMA C, et al. Learning attention-based embeddings for relation prediction in knowledge graphs[C]//Proc. of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 4710-4723.
|
34 |
CAI L, WANG W Y. KBGAN: adversarial learning for know-ledge graph embeddings[C]//Proc. of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018: 1470-1480.
|
35 |
XIAO H, HUANG M L, MENG L, et al. SSP: semantic space projection for knowledge graph embedding with text descriptions[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2017: 3104-3110.
|
36 |
ZHANG Z, ZHUANG F Z, QU M, et al. Knowledge graph embedding with hierarchical relation structure[C]//Proc. of the Conference on Empirical Methods in Natural Language Processing, 2018: 3198-3207.
|
37 |
XIE R B, LIU Z Y, LUAN H B, et al. Image-embodied knowledge representation learning[C]//Proc. of the 26th International Joint Conference on Artificial Intelligence, 2017: 3140- 3146.
|
38 |
CHEN X L, CHEN M H, SHI W J, et al. Embedding uncertain knowledge graphs[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2019: 3363-3370.
|
39 |
BORDES A, USUNIER N, GARCIA D A, et al. Translating embeddings for modeling multi-relational data[C]//Proc. of the 26th International Conference on Neural Information Processing Systems, 2013: 2787-2795.
|
40 |
YANG B S, YI W T, HE X D, et al. Embedding entities and relations for learning and inference in knowledge bases[J]. [EB/OL]. [2021-01-03]. arxiv. org/abs/1412.6575.
|
41 |
NGUYEN H L , VU D T , JUNG J J . Knowledge graph fusion for smart systems: a survey[J]. Information Fusion, 2020, 61, 56- 70.
|
42 |
WANG J, LAN Y X, ZHANG S S, et al. Knowledge graph for multi-source data fusion topics research[C]//Proc. of the International Conference on High Performance Big Data and Intelligent Systems, 2020. DOI: 10.11.9/HPBDIS49115.2020.9130586.
|
43 |
WANG Y, RUFFINELLI D, GEMULLA R, et al. On Evaluating embedding models for knowledge base completion[C]//Proc. of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), 2019: 104-112.
|