Abstract
To reduce the cognitive gap between human cognition and semantic search engines, there is a need for the incorporation of crowdsourced Ontologies and closed-world reasoning for recommending contents from the Web 3.0. In this paper, an OntoDynS framework has been proposed to tackle the ambiguity and the serendipity problem in recommending queries and URLs from the Semantic Web. A novel scheme called Ontology Bagging based on lateral co-similarity and collective similarity has been proposed to encompass diversity in the search results. OntoDynS adapts the Second Order Co-occurrence Pointwise mutual information in a semantic environment and proposes a novel Blend of Similarity measure with scenario-based weighting for query recommendation for yielding initial recommendations. Personalization is achieved by capturing current user-clicks through Dynamic Ontology Alignment by the integration of agent-based semantic rules for yielding minimal mappings for diversified user-centric query recommendation which is succeeded by recommending web pages. The dynamic amalgamation of crowdsourced Ontologies and Open Linked Data facilitates human–computer interaction. OntoDynS yields an F-Measure of 94.70% and 95.22% for recommending queries and web pages respectively for the DMOZ dataset. Experiments on CACM datasets have furnished an F-Measure of 76.56% and 77.71% for recommending queries and web pages respectively.
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References
Adnan SK, Abdullah NAZ (2019) Arabic query expansion using wordnet and cuckoo algorithm. ARPN J Eng Appl Sci 14(10):1899–1903
Antonella Carbonaro A (2021) Linked data and semantic web technologies to model context information for policy-making. J Ambient Intell Humaniz Comput 12:4395–4406
Bouma G (2009) Normalized (pointwise) mutual information in collocation extraction. Proceedings of GSCL 30:31–40
Câmara A, Santos RL (2019) Traversing semantically annotated queries for task-oriented query recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems (pp. 511–515)
Church KW, Hanks P (1990) Word association norms, mutual information, and lexicography. Comput Linguist 16(1):22–29
Chy AN, Ullah MZ, Aono M (2019) Query expansion for microblog retrieval focusing on an ensemble of features. J Inf Process 27:61–76
Cousyn C, Bouchard K, Gaboury S (2021) Web-based objects detection to discover key objects in human activities. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03433-0
Dahir S, El Qadi A, Bennis H (2018) Enriching user queries using Dbpedia features and relevance feedback. Procedia Comput Sci 127:499–504
Fang F, Zhang BW, Yin XC (2018) Semantic sequential query expansion for biomedical article search. IEEE Access 6:45448–45457
Fernández-Reyes FC, Hermosillo-Valadez J, Montes-y-Gómez M (2018) A prospect-guided global query expansion strategy using word embeddings. Inf Process Manage 54(1):1–13
Gao R, Shah C (2020) Toward creating a fairer ranking in search engine results. Inf Process Manag 57(1):102138
Guo L, Su X, Zhang L, Huang G, Gao X, Ding Z (2018) Query expansion based on semantic related network. In Pacific Rim International Conference on Artificial Intelligence (pp. 19–28). Springer, Cham
Habi A, Effantin B, Kheddouci H (2019) Diversified top-k search with relaxed graph simulation. Soc Netw Anal Min 9(1):55
Huang Q, Yang Y, Cheng M (2019) Deep learning the semantics of change sequences for query expansion. Softw Pract Exp 49(11):1600–1617
Huang Z, Cautis B, Cheng R, Zheng Y (2016) Kb-enabled query recommendation for long-tail queries. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (pp. 2107–2112)
Islam A, Inkpen D (2006) Second order co-occurrence pmi for determining the semantic similarity of words. In: LREC, pp 1033–1038
Jin H, Xiong L (2019) A query expansion method based on evolving source code. Wuhan Univ J Nat Sci 24(5):391–399
Kandasamy S, Cherukuri AK (2020) Query expansion using named entity disambiguation for a question-answering system. Concurr Comput Pract Exp 32(4):e5119
Khalaf ZA, Shtaet IA (2019) News retrieval based on short queries expansion and best matching. J Theor Appl Inf Technol 97(2):490–500
Khennak I, Drias H (2020) A Novel Hybrid Correlation Measure for Query Expansion-Based Information Retrieval. In Critical Approaches to Information Retrieval Research (pp. 1–19). IGI Global
Kokkoras F, Ntonas K, Bassiliades N (2013) DEiXTo: a web data extraction suite. In Proceedings of the 6th Balkan Conference in Informatics (pp. 9–12).
Krishnan A, Deepak P, Ranu S, Mehta S (2018) Leveraging semantic resources in diversified query expansion. World Wide Web 21(4):1041–1067
Leena Giri G, Manjula SH, Venugopal KR, Patnaik LM (2014) Mathematical Model of Semantic Look-An Efficient Context Driven Search Engine. arXiv preprint arrXiv:1402.7200.
Li W, Xia Q (2011) A method of concept similarity computation based on semantic distance. Procedia Eng 15:3854–3859
Liang S, Cai F, Ren Z, de Rijke M (2016) Efficient structured learning for personalized diversification. IEEE Trans Knowl Data Eng 28(11):2958–2973
Liu J, Zhou M, Lin L, Kim H, Wang J (2017) Rank web documents based on multi-domain ontology. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-017-0566-5
Nasir JA, Varlamis I, Ishfaq S (2019) A knowledge-based semantic framework for query expansion. Inf Process Manage 56(5):1605–1617
Pang W, Du J (2019) Query expansion and query fuzzy with large-scale click-through data for microblog retrieval. Int J Mach Learn Comput 9(3):279
Pang S, Ma J, Zhu J, Xue J, Tian Q (2018) Improving object retrieval quality by integration of similarity propagation and query expansion. IEEE Trans Multimed 21(3):760–770
Pushpa CN, Deepak G, Thriveni J, Venugopal KR (2015) Onto Collab: Strategic review oriented collaborative knowledge modeling using ontologies. In 2015 Seventh International Conference on Advanced Computing (ICoAC) (pp. 1–7). IEEE
Shokeen J, Rana C (2021) A trust and semantic based approach for social recommendation. J Ambient Intell Humaniz Comput 12:10289–10303
Singh J, Sharan A (2018) Rank fusion and semantic genetic notion based automatic query expansion model. Swarm Evol Comput 38:295–308
Song S, Huang W, Sun Y (2019) Semantic query graph based SPARQL generation from natural language questions. Clust Comput 22(1):847–858
Velagandula A, Chatterjee P, Mamatha C, Rajesh K (2018) Optimized query expansion based classifier for web information retrieval. Int J Pure Appl Math 120(6):3617–3630
Wibowo WC (2020) Hashtag and highest scored terms for expanding query. J Inf Commun Technol 16(1):121–135
Wu S, Zhang Z, Xu C (2019) Evaluating the effectiveness of Web search engines on results diversification. Inf Res Int Electron J 24(1):n1
Wu Bin, Xiong C, Sun M, Liu Z (2018) Query suggestion with feedback memory network. In Proceedings of the 2018 World Wide Web Conference (pp. 1563–1571)
Xu Bo, Lin H, Lin Y (2018) Learning to refine expansion terms for biomedical information retrieval using semantic resources. IEEE/ACM Trans Comput Biol Bioinf 16(3):954–966
Xu Bo, Lin H, Yang L, Xu K, Zhang Y, Zhang D, Yin F (2019) A supervised term ranking model for diversity enhanced biomedical information retrieval. BMC Bioinform 20(16):1–11
Xu Bo, Lin H, Lin Y, Guan Y (2020) Integrating social annotations into topic models for personalized document retrieval. Soft Comput 24(3):1707–1716
Yu H, Shi C, Bai Y, Zhang C, Hearne R (2019) Query expansion based on formal concept analysis from retrieved documents. J Internet Technol 20(2):409–421
Acknowledgements
I wholeheartedly thank God, the Almighty and Eternal Father, my Lord Jesus Christ for giving me the necessary strength and grace for completing this work with immense ease and perfection.I thank the Ministry of Human Resource Development and the Government of India for supporting me financially for my Ph.D. Research through the Fellowship.
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Deepak, G., Santhanavijayan, A. OntoDynS: expediting personalization and diversification in semantic search by facilitating cognitive human interaction through ontology bagging and dynamic ontology alignment. J Ambient Intell Human Comput 14, 8667–8691 (2023). https://doi.org/10.1007/s12652-021-03624-9
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DOI: https://doi.org/10.1007/s12652-021-03624-9