Ariani Indrawati
ABSTRACT
Various sectors now widely adopt knowledge graphs to describe and share their organizational knowledge bases. Unfortunately, the majority of knowledge-sharing systems are designed for domain experts. Making it extremely difficult for a non-expert to understand the content and explore the graph. A solution to this issue is using a machine-assisted knowledge graph exploration approach. This research introduces a knowledge exploration method to systematically and efficiently navigate a knowledge graph. First, we modeled the knowledge graphs based on the existing common schema. Second, we created a search tree technique to navigate the knowledge graph efficiently. The algorithm solves the problem by determining the path of knowledge graph exploration. We evaluated the method using a knowledge base of morphological characteristics of Capsicum. The goal of graph exploration was to identify a Capsicum species correctly. As a result, the proposed mechanism can achieve high precision, even when the search’s starting point is unknown beforehand.
- Zaenal Akbar, Hani Febri Mustika, Dwi Setyo Rini, Lindung Parningotan Manik, Ariani Indrawati, Agusdin Dharma Fefirenta, and Tutie Djarwaningsih. 2021. An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum. Future Internet 13, 7 (June 2021), 172. https://doi.org/10.3390/fi13070172Google Scholar
Cross Ref
- Dieter Fensel, Umutcan Şimşek, Kevin Angele, Elwin Huaman, Elias Kärle, Oleksandra Panasiuk, Ioan Toma, Jürgen Umbrich, and Alexander Wahler. 2020. Introduction: What Is a Knowledge Graph?Springer International Publishing, Cham, 1–10. https://doi.org/10.1007/978-3-030-37439-6_1Google ScholarCross Ref
- Google. 2012. Introducing the Knowledge Graph: things, not strings. https://blog.google/products/search/introducing-knowledge-graph-things-not/. Accessed: 23-07-2022.Google Scholar
- Liang He, Bin Shao, Yanghua Xiao, Yatao Li, Tie-Yan Liu, Enhong Chen, and Huanhuan Xia. 2022. Neurally-Guided Semantic Navigation in Knowledge Graph. IEEE Transactions on Big Data 8, 3 (June 2022), 607–615. https://doi.org/10.1109/TBDATA.2018.2805363Google ScholarCross Ref
- Emil Kuric, Javier D. Fernández, and Olha Drozd. 2019. Knowledge Graph Exploration: A Usability Evaluation of Query Builders for Laypeople. In Semantic Systems. The Power of AI and Knowledge Graphs, Maribel Acosta, Philippe Cudré-Mauroux, Maria Maleshkova, Tassilo Pellegrini, Harald Sack, and York Sure-Vetter (Eds.). Vol. 11702. Springer International Publishing, Cham, 326–342. https://doi.org/10.1007/978-3-030-33220-4_24 Series Title: Lecture Notes in Computer Science.Google Scholar
- Daifeng Li and Andrew Madden. 2019. Cascade embedding model for knowledge graph inference and retrieval. Information Processing & Management 56, 6 (2019), 102093. https://doi.org/10.1016/j.ipm.2019.102093Google ScholarDigital Library
- Matteo Lissandrini, Torben Bach Pedersen, Katja Hose, and Davide Mottin. 2020. Knowledge graph exploration: where are we and where are we going?ACM SIGWEB NewsletterSummer 2020 (July 2020), 1–8. https://doi.org/10.1145/3409481.3409485Google ScholarDigital Library
- Mohammed Najah Mahdi, Abdul Rahim Ahmad, Roslan Ismail, Hayder Natiq, and Mohammed Abdulameer Mohammed. 2020. Solution for Information Overload Using Faceted Search–A Review. IEEE Access 8(2020), 119554–119585. https://doi.org/10.1109/ACCESS.2020.3005536Google ScholarCross Ref
- Diana Maynard, Wim Peters, and Yaoyong Li. 2006. Metrics for evaluation of ontology-based information extraction. CEUR Workshop Proceedings 179 (2006).Google Scholar
- Steffen Metzger, Ralf Schenkel, and Marcin Sydow. 2017. 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 (01 Dec 2017), 333–366. https://doi.org/10.1007/s10844-017-0443-xGoogle ScholarDigital Library
- Davide Mottin, Matteo Lissandrini, Yannis Velegrakis, and Themis Palpanas. 2016. Exemplar Queries: A New Way of Searching. The VLDB Journal 25, 6 (dec 2016), 741–765. https://doi.org/10.1007/s00778-016-0429-2Google ScholarDigital Library
- Natasha Noy, Yuqing Gao, Anshu Jain, Anant Narayanan, Alan Patterson, and Jamie Taylor. 2019. Industry-scale knowledge graphs: Lessons and challenges. Commun. ACM 62, 8 (2019), 36–43.Google ScholarDigital Library
- Jacqueline Peng, David Xu, Ryan Lee, Siwei Xu, Yunyun Zhou, and Kai Wang. 2022. Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology. BMC Medical Informatics and Decision Making 22, S2 (Dec. 2022), 147. https://doi.org/10.1186/s12911-022-01848-zGoogle ScholarCross Ref
- S. Schmid, C. Henson, and T. Tran. 2019. Using Knowledge Graphs to Search an Enterprise Data Lake. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11762 LNCS. 262–266 pages. www.scopus.comCited By :10.Google Scholar
- Yuxuan Shi, Gong Cheng, Trung-Kien Tran, Jie Tang, and Evgeny Kharlamov. 2021. Keyword-Based Knowledge Graph Exploration Based on Quadratic Group Steiner Trees. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. IJCAI Organization, Montreal, Canada, 1555–1562. https://doi.org/10.24963/ijcai.2021/215Google ScholarCross Ref
- Liang Zheng, Shuo Liu, Zhuofei Song, and Fangtong Dou. 2021. Diversity-Aware Entity Exploration on Knowledge Graph. IEEE Access 9(2021), 118782–118793. https://doi.org/10.1109/ACCESS.2021.3107732Google ScholarCross Ref
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