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Example query on ontology-labels knowledge graph based on filter-refine strategy

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Abstract

The query processing on knowledge graphs has attracted significant attention in the past years. Different from the traditional query processing on knowledge graphs, the example query method can capture the users’ query intentions by providing examples. But regrettably, it does not consider the semantic relevance of entities. Therefore, we first define an example query on the ontology-labels knowledge graph to better capture the query interest of users and improve the semantic relevance of query results. Second, a Filter-Refine Strategy-based method is proposed to solve the example queries. Specifically, we propose the ontology-labels tree index to reduce the search space and the bidirectional index to improve query efficiency. Then, an effective candidate results combination technology is used to return top-k results directly. Extensive experiments over two real-world data sets have shown our proposed algorithm is superior to three existing algorithms in terms of efficiency and effectiveness.

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References

  1. Al-Qadhi, A.F.M., Priebe, C.E., Helm, H.S., et al.: Subgraph nomination: Query by example subgraph retrieval in networks. arXiv:2101.12430 (2021)

  2. Baas, J., Dastani, M., Feelders, A.: Exploiting transitivity constraints for entity matching in knowledge graphs. arxiv:2104.12589 (2021)

  3. Cai, T., Li, J., Mian A.S., et al.: Target-aware holistic influence maximization in spatial social networks. IEEE Transactions on Knowledge and Data Engineering(TKDE), https://doi.org/10.1109/TKDE.2020.3003047 (2020)

  4. Chen, J., Zhong, M., Li, J., et al.: Effective deep attributed network representation learning with topology adapted smoothing. IEEE Transactions on Cybernetics PP(99), 1–12 (2021)

    Google Scholar 

  5. Du, J., Michalska, S., Subramani, S., et al.: Neural attention with character embeddings for hay fever detection from twitter. Health Information Science and Systems 7(1), 1–7 (2019)

    Article  Google Scholar 

  6. Ehsan, H., Sharaf, M.A., Demartini, G.: Qurve: query refinement for view recommendation in visual data exploration. In: ADBIS (Short Papers), pp. 154–165 (2020)

  7. Fauzan, R., Siahaan, D.O., Rochimah, S., et al.: A novel approach to automated behavioral diagram assessment using label similarity and subgraph edit distance. Computer Science 22(2), 191–207 (2021)

    Article  Google Scholar 

  8. Gu, Y., Zhou, T., Cheng, G., et al.: Relevance search over schema-rich knowledge graphs. In: WSDM, pp. 114–122 (2019)

  9. Hamilton, K.E., Humble, T.S.: Identifying the minor set cover of dense connected bipartite graphs via random matching edge sets. Quantum Information Processing 16(4), 94 (2017)

    Article  MATH  Google Scholar 

  10. Hu, X., Duan, J., Dang, D.: Natural language question answering over knowledge graph: the marriage of SPARQL query and keyword search. Knowledge and Information Systems 63(4), 819–844 (2021)

    Article  Google Scholar 

  11. Huang, J., Gharbieh, W., Shim, H.S., et al.: (2021) Query-by-example keyword spotting system using multi-head attention and soft-triple loss. In: ICASSP, pp. 6858–6862

  12. Huang, J., Abadi, D.J., Ren, K.: Scalable SPARQL querying of large RDF graphs. Proceedings of the VLDB Endowment 4(11), 1123–1134 (2011)

    Article  Google Scholar 

  13. Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of top-\(k\) query processing techniques in relational database systems. Acm Computing Surveys 40(4), 1–58 (2008)

    Article  Google Scholar 

  14. Jiankai, C., Lianhai, Z.: Query-by-example spoken term detection by applying the HDPHMM Tokenizer. Journal of Signal Processing 33(5), 8 (2017)

    Google Scholar 

  15. Khwildi, R., Zaid, A.O., Dufaux, F.: Query-by-example HDR image retrieval based on CNN. Multimedia Tools and Applications 80(10), 15413–15428 (2021)

    Article  Google Scholar 

  16. Kim, J., Kim, K., Sohn, M., et al.: Q-PD: Query graph extension framework using predicate-based RDF on linked open data. International Journal of Web and Grid Services 16(2), 105–125 (2020)

    Article  Google Scholar 

  17. Lan, Y., Jiang, J.: Query graph generation for answering multi-hop complex questions from knowledge bases. In: ACL, pp. 969–974 (2020)

  18. Li, Y., Gu, C., Dullien, T., et al.: Graph matching networks for learning the similarity of graph structured objects. In: ICML, pp. 3835–3845 (2019)

  19. Li, J., Cai, T., Deng, K., et al.: Community-diversified influence maximization in social networks[J]. Information Systems 92, 101522 (2020)

    Article  Google Scholar 

  20. Li, Z., Wang, X., Li, J., et al.: Deep attributed network representation learning of complex coupling and interaction. Knowledge-Based Systems 212(1), 106618 (2021)

    Article  Google Scholar 

  21. Lissandrini, M., Mottin, D., Palpanas, T., et al.: Multi-example search in rich information graphs. In: ICDE, pp. 809–820 (2018)

  22. Ma, H., Alipourlangouri, M., Wu, Y., et al.: Ontology-based entity matching in attributed graphs. Proceedings of the VLDB Endowment 12(10), 1195–1207 (2019)

    Article  Google Scholar 

  23. Meng, X., Zhang, X., Tang, Y., et al.: Adaptive query relaxation and top-\(k\) result ranking over autonomous web databases. Knowledge and Information Systems 51(2), 395–433 (2017)

    Article  Google Scholar 

  24. Metzger, S., Schenkel, R., Sydow, M.: QBEES: query-by-example entity search in semantic knowledge graphs based on maximal aspects, diversity-awareness and relaxation. Journal of Intelligent Information Systems 49(3), 1–34 (2017)

    Article  Google Scholar 

  25. Mottin, D., Lissandrini, M., Velegrakis, Y., et al.: Exemplar queries: a new way of searching. VLDB Journal 25(6), 1–25 (2016)

    Article  Google Scholar 

  26. Mountasser I, Ouhbi B, Hdioud F, et al.: Semantic-based big data integration framework using scalable distributed ontology matching strategy. Distributed and Parallel Databases 39(4), 891-937 (2021)

  27. Naacke, H., Curé, O.: On distributed SPARQL query processing using triangles of RDF triples. Open Journal of Semantic Web (OJSW) 7(1), 17–32 (2020)

    Google Scholar 

  28. Shao, B., Li, X., Bian, G.: A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph. Expert Systems with Applications 165, 113764 (2021)

    Article  Google Scholar 

  29. Song, X., Li, J., Tang, Y., et al.: KT: A joint graph convolutional network based deep knowledge tracing. Information Sciences 580, 510–523 (2021)

    Article  Google Scholar 

  30. Tang, N., Shen, D.R., Kou, Y., et al.: An example query method for multi-source knowledge graph. Journal of Computer Research and Development S1, 1–8 (2015)

    Google Scholar 

  31. Wang, Y., Khan, A., Wu, T., et al.: Semantic guided and response times bounded top-\(k\) similarity search over knowledge graphs. In: ICDE, pp 445–456 (2020)

  32. Wang, Y., Khan, A., Wu, T., et al.: Semantic guided and response times bounded top-k similarity search over knowledge graphs. In: ICDE, pp. 445–456 (2020)

  33. Wang, J., Wang, J., Zeng, G., et al.: Fast neighborhood graph search using cartesian concatenation. Multimedia Data Mining and Analytics, 397–417 (2015). https://doi.org/10.1007/978-3-319-14998-1_18

  34. Wang, Y., Xu, X., Hong, Q., et al.: Top-\(k\) star queries on knowledge graphs through semantic-aware bounding match scores. Knowledge-Based Systems 213(2), 106655 (2020)

    Google Scholar 

  35. Weller, T., Paulheim, H.: Evidential relational-graph convolutional networks for entity classification in knowledge graphs. In: CIKM, pp. 3533–3537 (2021)

  36. Wu, S., Zhang, Y., Cao, W.: Network security assessment using a semantic reasoning and graph based approach. Computers and Electrical Engineering 64, 96–109 (2017)

    Article  Google Scholar 

  37. Wu, J., Sangaiah, A.K., Gao, W.: Graph learning-based ontology sparse vector computing. Symmetry 12(9), 1562 (2020)

    Article  Google Scholar 

  38. Xu, Z.B., Li, Z., Liu, H.D., et al.: Subgraph isomorphism matching algorithm based on neighbor information aggregation. Journal of Computer Applications 41(1), 43–47 (2021)

    Google Scholar 

  39. Xue, G., Zhong, M., Li, J., et al.: Dynamic network embedding survey. arXiv:2103.15447 (2021)

  40. Yamada, M., Inokuchi, A.: Similar supergraph search based on graph edit distance. Algorithms 14(8), 225 (2021)

    Article  Google Scholar 

  41. Yang, Y., Guan, Z., Li, J., et al.: Interpretable and efficient heterogeneous graph convolutional network. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2021.3101356 (2021)

  42. Yin, J., Tang, M.J., Cao J., et al.: Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web 25, 401–423 (2021)

  43. Zhang, F., Li, Z., Peng, D., et al.: RDF for temporal data management-a survey. Earth Sci. Inf. 14(2), 563–599 (2021)

  44. Zhang, L.Y., Yin, H.F.: A knowledge graph query algorithm based on OAN. Comput Eng Softw 39(1), 54-59 (2018)

  45. Zhang, H.W., Xie, X.F., Duan, Y.Y., et al.: An algorithm for matching based on adaptive structure summary. Chinese Journal of Computers 01, 54–73 (2017)

    Google Scholar 

Download references

Funding

This study was funded by the National Natural Science Foundation of China (No. 62072220, 61502215). Central Government Guides Local Science and Technology Development Foundation Project of Liaoning Province (No. 2022JH6/100100032). China Postdoctoral Science Foundation Funded Project (No. 2020M672134).

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Correspondence to Hao Luo.

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This article belongs to the Topical Collection: Special Issue on Decision Making in Heterogeneous Network Data Scenarios and Applications

Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu

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Ding, L., Li, S., Li, M. et al. Example query on ontology-labels knowledge graph based on filter-refine strategy. World Wide Web 26, 343–373 (2023). https://doi.org/10.1007/s11280-022-01020-7

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  • DOI: https://doi.org/10.1007/s11280-022-01020-7

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