Abstract
Multi-example query over knowledge graphs allows users to provide multiple example fragments to express their intention, overcoming the shortage of single example query asks users to give a specific complete query example. Unfortunately, the existing multi-example query ignores the semantic relevance of entities, and query results are inconsistent in compactness, which causes it difficult for users to check their interesting results. Therefore, we first define a multi-example query over ontology-label knowledge graphs to facilitate users to express their query intentions and improve the semantic relevance of query results. Secondly, we propose a matching pattern to analyze users’ query intention to prioritize the more compact query results to improve users’ satisfaction. Specifically, we first use the OELIndex to filter the search space and improve query efficiency. Then, we construct a connected matching pattern according to query examples to get a more compact query result. After that, we select the pattern fragments with the smallest amount for priority matching by assessing the number of candidate sets, significantly reducing the isomorphic computations. Finally, extensive experiments are carried out on real data sets to verify the effectiveness and practicability of our proposed method.
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References
Chen Y, Subburathinam A, Chen C H, et al (2021) Personalized food recommendation as constrained question answering over a large-scale food knowledge graph. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp 544–552
Ding Linlin, Li Sisi, Li Mo, et al (2022) Example query on ontology-labels knowledge graph based on filter-refine strategy. In: World Wide Web, pp 1–31
Ehsan H, Sharaf MA, Demartini G (2020) Qurve: Query refinement for view recommendation in visual data exploration. In: European Conference on Advances in Databases and Information Systems, pp 154-165
Bakhshi M, Nematbakhsh M, Mohsenzadeh M et al (2020) Data-driven construction of SPARQL queries by approximate question graph alignment in question answering over knowledge graphs. Expert Syst Appl 146:1–19
Gu Y, Zhou T, Cheng G, et al (March 2019) Relevance search over schema-rich knowledge graphs. In: Proceedings of the twelfth acm international conference on web search and data mining, pp 114–122
Huang J, Abadi DJ, Ren K (2011) Scalable SPARQL querying of large RDF graphs. Proc VLDB Endow 4(11):1123–1134
Jayaram N, Khan A, Li C et al (2015) Querying knowledge graphs by example entity tuples. IEEE Trans Knowl Data Eng 27(10):2797–2811
Lan Y, Jiang J (July 2020) Query graph generation for answering multi-hop complex questions from knowledge bases. In: Association for Computational Linguistics, pp 969–974
Li X, Zang H, Yu X et al (2021) On improving knowledge graph facilitated simple question answering system. Neural Comput Appl 33(16):10587–10596
Lissandrini M, Mottin D, Palpanas T et al (2018) Data exploration using example-based methods. Syn Lect Data Manag 10(4):1–164
Lissandrini M, Mottin D, Palpanas T, et al (April 2018) Multi-example search in rich information graphs. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp 809–820
Liu J, Chen Y, Islam SMN, et al (October 2021) Stein variational recommendation system with knowledge embedding enabling the IoT services. In: IECON 2021-47th Annual Conference of the IEEE Industrial Electronics Society, pp 1–6
Meng X, Zhang X, Tang Y et al (2017) Adaptive query relaxation and top-k result ranking over autonomous web databases. Knowl Inf Syst 51(2):395–433
Mottin D, Lissandrini M, Velegrakis Y et al (2016) Exemplar queries: a new way of searching. VLDB J 25(6):741–765
Naacke H (2020) On distributed SPARQL query processing using triangles of RDF triples. Open J Semant Web 7(1):17–32
Namaki MH, Song Q, Wu Y (2019) Navigate: explainable visual graph exploration by examples. In: Proceedings of the 2019 International Conference on Management of Data, pp 1965–1968
Omran PG, Wang K, Wang Z (2019) Learning temporal rules from knowledge graph streams. Combining Machine Learning with Knowledge Engineering, In AAAI Spring Symposium, pp 1–8
Psallidas F, Ding B, Chakrabarti K, et al (May 2015) S4: Top-k spreadsheet-style search for query discovery. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp 2001–2016
Qiu Y, Zhang K, Wang Y, et al (October 2020) Hierarchical query graph generation for complex question answering over knowledge graph. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 1285–1294
Shao B, Li X, Bian G (2021) A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph. Expert Syst Appl 165:117–129
Wu S, Li Y, Zhang D, et al (January 2021) Topicka: Generating commonsense knowledge-aware dialogue responses towards the recommended topic fact. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp 3766–3772
Wang Y, Xu X, Hong Q et al (2021) Top-k star queries on knowledge graphs through semantic-aware bounding match scores. Knowl Based Syst 213:1–17
Xie M, Bhowmick SS, Cong G et al (2017) PANDA: toward partial topology-based search on large networks in a single machine. VLDB J 26(2):203–228
Zhang S, Li JZ, Gao H et al (2010) Approach for efficient subgraph isomorphism testing for multiple graphs. J Softw 21(3):401–414
Zhu S, Cheng X, Su S (2020) Knowledge-based question answering by tree-to-sequence learning. Neurocomputing 372:64–72
Ma H, Alipourlangouri M, Wu Y et al (2019) Ontology-based entity matching in attributed graphs. Proc VLDB Endow 12(10):1195–1207
Liu G, Wang Y, Zheng B et al (2020) Strong social graph based trust-oriented graph pattern matching with multiple constraints[J]. IEEE Trans Emerg Topics Comput Intell 4(5):675–685
Li J, Su J, Xia C et al (2021) Salient object detection with purificatory mechanism and structural similarity loss[J]. IEEE Trans Image Process 30:6855–6868
Blumenthal DB, Gamper J (2020) On the exact computation of the graph edit distance[J]. Patt Recogn Lett 134:46–57
Acknowledgements
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). Natural Science Foundation of Liaoning Province (2022-KF-13-06, 2022-BS-111).
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Ding, L., Li, S., Ma, J. et al. Multi-example query over ontology-label knowledge graphs. Computing (2023). https://doi.org/10.1007/s00607-023-01194-6
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DOI: https://doi.org/10.1007/s00607-023-01194-6