The alignment and fusion of knowledge graphs have been at entity alignment and fusion which match and align knowledge graphs (KGs) by measuring similarity of the entities in KGs. Nevertheless, false fusion of completely different KGs can be easily caused if only considering entity similarity but ignoring entity relationship similarity. This paper focuses on entity relationship similarity model and KGs fusion method to achieve automatic construction of KGs. First, Same Destination Paths is developed based on Maximum Common Subgraph, which is used to build the Local Maximum Connected Same Destination Structure (LMCSDS) model to measure the entity relationship similarity of KGs. Then, a fusion method for similar fragmentation KGs (FKGs) is developed by analyzing the types of FKGs. Third, a Reinforcement Local Maximum Connected Same Destination Structure (RLMCSDS) similarity model is developed to ensure that the similarity between FKGs can still be measured correctly after fusion of FKGs. Meanwhile, the fusion results obtained by the developed RLMCSDS model and fusion method are theoretically proved to be independent with the fusion order of FKGs. Finally, experimental results on several datasets demonstrate the outstanding performance of the RLMCSDS model in comparison with some existing methods. Moreover, a complete KG can be built by the RLMCSDS model and the fusion method.
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Aouicha MB, Taieb MAH, Hamadou AB (2016) Taxonomy-based information content and wordnet-wiktionary-wikipedia glosses for semantic relatedness. Appl Intell 45(2):475–511
Babel L (1994) A fast algorithm for the maximum weight clique problem. Computing 52(1):31–38
Batet M, Sánchez D, Valls A, Gibert K (2013) Semantic similarity estimation from multiple ontologies. Appl Intell 38(1):29–44
Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (surf). Comput Vis Image Underst 110(3):346–359
Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems, pp 2787–2795
Bron C, Kerbosch J (1973) Algorithm 457: finding all cliques of an undirected graph. Commun ACM 16 (9):575–577
Depolli M, Konc J, Rozman K, Trobec R, Janezic D (2013) Exact parallel maximum clique algorithm for general and protein graphs. J Chem Inf Model 53(9):2217–2228
Douze M, Jégou H, Sandhawalia H, Amsaleg L, Schmid C (2009) Evaluation of gist descriptors for web-scale image search. In: Proceedings of the ACM international conference on image and video retrieval. ACM, p 19
Duesbury E, Holliday JD, Willett P (2017) Maximum common subgraph isomorphism algorithms. MATCH Commun Math Comput Chem 77(2):213–232
Durand PJ, Pasari R, Baker JW, Tsai Cc (1999) An efficient algorithm for similarity analysis of molecules. Int J Chem 2(17):1–16
Gan W, Chun-Wei J, Chao HC, Wang SL, Philip SY (2018) Privacy preserving utility mining: a survey. In: 2018 IEEE International conference on big data (big data). IEEE, pp 2617–2626
Grosso A, Locatelli M, Pullan W (2008) Simple ingredients leading to very efficient heuristics for the maximum clique problem. J Heuristics 14(6):587–612
Jiang Cy, Wang Wp, Li Q (2006) Sysml: a new systems modeling language. J Syst Simul, 6
Kawabata T (2011) Build-up algorithm for atomic correspondence between chemical structures. J Chem Inf Model 51(8):1775–1787
Kim JB, Park RH, Kim HI (2017) Comprehensive analysis and evaluation to unsupervised binary hashing method in image similarity measurement. IET Image Process 11(8):633–639
Koch I (2001) Enumerating all connected maximal common subgraphs in two graphs. Theor Comput Sci 250 (1–2):1–30
Lever J, Gakkhar S, Gottlieb M, Rashnavadi T, Lin S, Siu C, Smith M, Jones MR, Krzywinski M, Jones SJ (2017) A collaborative filtering-based approach to biomedical knowledge discovery. Bioinformatics 34(4):652–659
Lowe DG, et al. (1999) Object recognition from local scale-invariant features. In: iccv, vol 99, pp 1150–1157
Mahalanobis A, Carlson DW, Kumar BV (1998) Evaluation of mach and dccf correlation filters for sar atr using the mstar public database. In: Algorithms for synthetic aperture radar imagery V, vol 3370. International Society for Optics and Photonics, pp 460–468
McGregor JJ (1982) Backtrack search algorithms and the maximal common subgraph problem. Softw: Pract Exper 12(1):23–34
O’shea K, Crockett K, Bandar Z, O’shea J (2014) Erratum: an approach to conversational agent design using semantic sentence similarity (appl intell. Appl Intell 40(1):199–199
Qazi N, Wong BW (2017) Behavioural & tempo-spatial knowledge graph for crime matching through graph theory. In: 2017 European intelligence and security informatics conference (EISIC). IEEE, pp 143-146
Raymond JW, Gardiner EJ, Willett P (2002) Heuristics for similarity searching of chemical graphs using a maximum common edge subgraph algorithm. J Chem Inf Comput Sci 42(2):305-316
Rosen KH Discrete mathematics and its applications
Singhal A (2012) Introducing the knowledge graph: things, not strings. Official google blog, 5
Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32
Wang C, Ma X, Chen J, Chen J (2018) Information extraction and knowledge graph construction from geoscience literature. Comput Geosci 112:112–120
Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Twenty-Eighth AAAI conference on artificial intelligence
Xiao H, Huang M, Meng L, Zhu X (2017) Ssp: semantic space projection for knowledge graph embedding with text descriptions. In: Thirty-First AAAI conference on artificial intelligence
Xiao H, Huang M, Zhu X (2015) From one point to a manifold: Knowledge graph embedding for precise link prediction. arXiv:https://arxiv.org/abs/1512.04792
Yu T, Li J, Yu Q, Tian Y, Shun X, Xu L, Zhu L, Gao H (2017) Knowledge graph for tcm health preservation: design, construction, and applications. Artif Intell Med 77:48–52
Zhang C, You FC (2012) The technique of shape-based multi-feature combination of trademark image retrieval. In: Advanced materials research, vol 429. Trans Tech Publ, pp 287–291
Zhang C, Zhou M, Han X, Hu Z, Ji Y (2017) Knowledge graph embedding for hyper-relational data. Tsinghua Sci Technol 22(2):185-197
Zhang X, Liu X, Li X, Pan D (2017) Mmkg: an approach to generate metallic materials knowledge graph based on dbpedia and wikipedia. Comput Phys Commun 211:98-112
Zhu Y, Qin L, Yu JX, Ke Y, Lin X (2013) High efficiency and quality: large graphs matching. VLDB J 22(3):345-368
This work was supported by national key research and development project (grant numbers 2018YFB1700902) and the national natural science foundation of China (grant numbers 51775132).
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Lin, L., Liu, J., Lv, Y. et al. A similarity model based on reinforcement local maximum connected same destination structure oriented to disordered fusion of knowledge graphs. Appl Intell 50, 2867–2886 (2020). https://doi.org/10.1007/s10489-020-01673-9
- Maximum common subgraph
- Same destination paths
- Similarity model
- Fusion modeling
- Fragmentation knowledge graph