{"type":"ScholarlyArticle","title":"Keyword-Based Knowledge Graph Exploration Based on Quadratic Group Steiner Trees","authors":[{"name":"Yuxuan Shi","slug":"yuxuan-shi","given":"Yuxuan","family":"Shi","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China; Bosch Center for Artificial Intelligence , Renningen , Germany"},{"name":"Gong Cheng","slug":"gong-cheng","given":"Gong","family":"Cheng","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Trung-Kien Tran","slug":"trung-kien-tran","given":"Trung-Kien","family":"Tran","affiliation":"Bosch Center for Artificial Intelligence , Renningen , Germany"},{"name":"Jie Tang","slug":"jie-tang","given":"Jie","family":"Tang","affiliation":"Department of Computer Science and Technology , Tsinghua University , China"},{"name":"Evgeny Kharlamov","slug":"evgeny-kharlamov","given":"Evgeny","family":"Kharlamov","affiliation":"Bosch Center for Artificial Intelligence , Renningen , Germany; Department of Informatics , University of Oslo , Norway"}],"abstract":"Exploring complex structured knowledge graphs (KGs) is challenging for non-experts as it requires knowledge of query languages and the underlying structure of the KGs. Keyword-based exploration is a convenient paradigm, and computing a group Steiner tree (GST) as an answer is a popular implementation. Recent studies suggested improving the cohesiveness of an answer where entities have small semantic distances from each other. However, how to efficiently compute such an answer is open. In this paper, to model cohesiveness in a generalized way, the quadratic group Steiner tree problem (QGSTP) is formulated where the cost function extends GST with quadratic terms representing semantic distances. For QGSTP we design a branch-and-bound best-first (B3F) algorithm where we exploit combinatorial methods to estimate lower bounds for costs. This exact algorithm shows practical performance on medium-sized KGs.","doi":"10.24963/ijcai.2021/215","url":"https://ijcai.org/proceedings/2021/215","pdf":"https://ijcai.org/proceedings/2021/0215.pdf","markdown":"https://ijcai.org/proceedings/2021/215.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/215","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Data Mining","track":"Main Track","number":215,"pages":{"first":1555,"last":1562},"keywords":["Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Knowledge Representation and Reasoning: Semantic Web","Data Mining: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Bryson et al., 2020] Spencer Bryson, Heidar Davoudi, Lukasz Golab, Mehdi Kargar, Yuliya Lytvyn, Piotr Mierzejewski, Jaroslaw Szlichta, and Morteza Zihayat. Robust keyword search in large attributed graphs. Inf. Retr. J., 23(5):502-524, 2020.","title":"Robust keyword search in large attributed graphs","authors":"Spencer Bryson, Heidar Davoudi, Lukasz Golab, Mehdi Kargar, Yuliya Lytvyn, Piotr Mierzejewski, Jaroslaw Szlichta, Morteza Zihayat","year":"2020","venue":"Inf. Retr. J","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cheng and Kharlamov, 2017] Gong Cheng and Evgeny Kharlamov. Towards a semantic keyword search over in- dustrial knowledge graphs (extended abstract). In IEEE BigData 2017, pages 1698-1700, 2017.","title":"Towards a semantic keyword search over industrial knowledge graphs","authors":"Kharlamov Cheng, Gong Cheng, Evgeny Kharlamov","year":"2017","venue":"IEEE BigData 2017","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cheng et al., 2016] Gong Cheng, Cheng Jin, and Yuzhong Qu. HIEDS: A generic and efficient approach to hierarchi- cal dataset summarization. In IJCAI 2016, pages 3705- 3711, 2016.","title":"HIEDS: A generic and efficient approach to hierarchical dataset summarization","authors":"Cheng","year":"2016","venue":"IJCAI 2016","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cheng et al., 2017a] Gong Cheng, Cheng Jin, Wentao Ding, Danyun Xu, and Yuzhong Qu. Generating illustrative snip- pets for open data on the web. In WSDM 2017, pages 151- 159, 2017.","title":"Generating illustrative snippets for open data on the web","authors":"Cheng","year":"2017","venue":"WSDM 2017","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheng et al., 2017b] Gong Cheng, Fei Shao, and Yuzhong Qu. An empirical evaluation of techniques for ranking semantic associations. IEEE Trans. Knowl. Data Eng., 29(11):2388-2401, 2017.","title":"An empirical evaluation of techniques for ranking semantic associations","authors":"Cheng","year":"2017","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cheng et al., 2020] Gong Cheng, Shuxin Li, Ke Zhang, and Chengkai Li. Generating compact and relaxable answers to keyword queries over knowledge graphs. In ISWC 2020, Part I, pages 110-127, 2020.","title":"Generating compact and relaxable answers to keyword queries over knowledge graphs","authors":"Gong Cheng, Shuxin Li, Ke Zhang, Chengkai Li","year":"2020","venue":"ISWC 2020, Part I","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cheng, 2020] Gong Cheng. Relationship search over knowledge graphs. ACM SIGWEB Newsl., 2020(Sum- mer):3, 2020.","title":"Relationship search over knowledge graphs","authors":"Gong Cheng, Cheng","year":"2020","venue":"ACM SIGWEB Newsl","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Coffman and Weaver, 2014] Joel Coffman and Alfred C. Weaver. An empirical performance evaluation of relational keyword search techniques. IEEE Trans. Knowl. Data Eng., 26(1):30-42, 2014.","title":"An empirical performance evaluation of relational keyword search techniques","authors":"Weaver ; ] Joel Coffman, Alfred C Coffman, Weaver","year":"2014","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Dass et al., 2015] Ananya Dass, Aggeliki Dimitriou, Cem Aksoy, and Dimitri Theodoratos. Incorporating cohesive- ness into keyword search on linked data. In WISE 2015, Part II, pages 47-62, 2015.","title":"Incorporating cohesiveness into keyword search on linked data","authors":"Ananya Dass, Aggeliki Dimitriou, Cem Aksoy, Dimitri Theodoratos","year":"2015","venue":"WISE 2015","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ding et al., 2007] Bolin Ding, Jeffrey Xu Yu, Shan Wang, Lu Qin, Xiao Zhang, and Xuemin Lin. Finding top-k min- cost connected trees in databases. In ICDE 2007, pages 836-845, 2007.","title":"Finding top-k mincost connected trees in databases","authors":"Bolin Ding, Jeffrey Xu Yu, Shan Wang, Lu Qin, Xiao Zhang, Xuemin Lin","year":"2007","venue":"ICDE 2007","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gu et al., 2019] Yu Gu, Tianshuo Zhou, Gong Cheng, Ziyang Li, Jeff Z. Pan, and Yuzhong Qu. Relevance search over schema-rich knowledge graphs. In WSDM 2019, pages 114-122, 2019.","title":"Relevance search over schema-rich knowledge graphs","authors":"Yu Gu, Tianshuo Zhou, Gong Cheng, Ziyang Li, Jeff Z Pan, Yuzhong Qu","year":"2019","venue":"WSDM 2019","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hasibi et al., 2017] Faegheh Hasibi, Fedor Nikolaev, Chenyan Xiong, Krisztian Balog, Svein Erik Bratsberg, Alexander Kotov, and Jamie Callan. DBpedia-Entity v2: A test collection for entity search. In SIGIR 2017, pages 1265-1268, 2017.","title":"DBpedia-Entity v2: A test collection for entity search","authors":"Hasibi","year":"2017","venue":"SIGIR 2017","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ihler, 1991] Edmund Ihler. The complexity of approximat- ing the class Steiner tree problem. In WG 1991, pages 85-96, 1991.","title":"The complexity of approximating the class Steiner tree problem","authors":"Edmund Ihler, Ihler","year":"1991","venue":"WG 1991","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kacholia et al., 2005] Varun Kacholia, Shashank Pandit, Soumen Chakrabarti, S. Sudarshan, Rushi Desai, and Hrishikesh Karambelkar. Bidirectional expansion for key- word search on graph databases. In VLDB 2005, pages 505-516, 2005.","title":"Bidirectional expansion for keyword search on graph databases","authors":"Varun Kacholia, Shashank Pandit, Soumen Chakrabarti, S Sudarshan, Rushi Desai, Hrishikesh Karambelkar","year":"2005","venue":"VLDB 2005","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kargar and An, 2011] Mehdi Kargar and Aijun An. Key- word search in graphs: Finding r-cliques. PVLDB, 4(10):681-692, 2011.","title":"Keyword search in graphs: Finding r-cliques","authors":"An ; Kargar, Mehdi Kargar, Aijun An","year":"2011","venue":"PVLDB","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Klein and Ravi, 1995] Philip N. Klein and R. Ravi. A nearly best-possible approximation algorithm for node-weighted Steiner trees. J. Algorithms, 19(1):104-115, 1995.","title":"A nearly best-possible approximation algorithm for node-weighted Steiner trees","authors":"Philip N Klein, R Ravi","year":"1995","venue":"J. Algorithms","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Le et al., 2014] Wangchao Le, Feifei Li, Anastasios Ke- mentsietsidis, and Songyun Duan. Scalable keyword search on large RDF data. IEEE Trans. Knowl. Data Eng., 26(11):2774-2788, 2014.","title":"Wangchao Le, Feifei Li, Anastasios Kementsietsidis","authors":"Le","year":"2014","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2016] Rong-Hua Li, Lu Qin, Jeffrey Xu Yu, and Rui Mao. Efficient and progressive group Steiner tree search. In SIGMOD 2016, pages 91-106, 2016.","title":"Efficient and progressive group Steiner tree search","authors":"Rong-Hua Li, Lu Qin, Jeffrey Xu Yu, Rui Mao","year":"2016","venue":"SIGMOD 2016","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2020] Shuxin Li, Gong Cheng, and Chengkai Li. Relaxing relationship queries on graph data. J. Web Se- mant., 61-62:100557, 2020.","title":"Relaxing relationship queries on graph data","authors":"Li","year":"2020","venue":"J. Web Semant","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lissandrini et al., 2020] Matteo Lissandrini, Torben Bach Pedersen, Katja Hose, and Davide Mottin. Knowledge graph exploration: where are we and where are we going? ACM SIGWEB Newsl., 2020(Summer):4, 2020.","title":"Knowledge graph exploration: where are we and where are we going? ACM SIGWEB Newsl","authors":"Matteo Lissandrini, Torben Bach Pedersen, Katja Hose, Davide Mottin","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Pound et al., 2012] Jeffrey Pound, Alexander K. Hudek, Ihab F. Ilyas, and Grant E. Weddell. Interpreting keyword queries over web knowledge bases. In CIKM 2012, pages 305-314, 2012.","title":"Interpreting keyword queries over web knowledge bases","authors":"Jeffrey Pound, Alexander K Hudek, F Ihab, Grant E Ilyas, Weddell","year":"2012","venue":"CIKM 2012","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Shekarpour et al., 2013] Saeedeh Shekarpour, Axel- Cyrille Ngonga Ngomo, and Sören Auer. Question answering on interlinked data. In WWW 2013, pages 1145-1156, 2013.","title":"Saeedeh Shekarpour, Axel-Cyrille Ngonga Ngomo","authors":"Shekarpour","year":"2013","venue":"WWW 2013","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Shi et al., 2020] Yuxuan Shi, Gong Cheng, and Evgeny Kharlamov. Keyword search over knowledge graphs via static and dynamic hub labelings. In WWW 2020, pages 235-245, 2020.","title":"Keyword search over knowledge graphs via static and dynamic hub labelings","authors":"Yuxuan Shi, Gong Cheng, Evgeny Kharlamov","year":"2020","venue":"WWW 2020","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Shi et al., 2021] Yuxuan Shi, Gong Cheng, Trung-Kien Tran, Evgeny Kharlamov, and Yulin Shen. Efficient com- putation of semantically cohesive subgraphs for keyword- based knowledge graph exploration. In WWW 2021, 2021.","title":"Efficient computation of semantically cohesive subgraphs for keywordbased knowledge graph exploration","authors":"Yuxuan Shi, Gong Cheng, Trung-Kien Tran, Evgeny Kharlamov, Yulin Shen","year":"2021","venue":"WWW 2021","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Sun et al., 2020] Yawei Sun, Lingling Zhang, Gong Cheng, and Yuzhong Qu. SPARQA: skeleton-based semantic parsing for complex questions over knowledge bases. In AAAI-IAAI-EAAI 2020, pages 8952-8959, 2020.","title":"SPARQA: skeleton-based semantic parsing for complex questions over knowledge bases","authors":"Yawei Sun, Lingling Zhang, Gong Cheng, Yuzhong Qu","year":"2020","venue":"AAAI-IAAI-EAAI 2020","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Tran et al., 2009] Thanh Tran, Haofen Wang, Sebastian Rudolph, and Philipp Cimiano. Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data. In ICDE 2009, pages 405-416, 2009.","title":"Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data","authors":"Thanh Tran, Haofen Wang, Sebastian Rudolph, Philipp Cimiano","year":"2009","venue":"ICDE 2009","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Yang et al., 2019] Yueji Yang, Divyakant Agrawal, H. V. Ja- gadish, Anthony K. H. Tung, and Shuang Wu. An efficient parallel keyword search engine on knowledge graphs. In ICDE 2019, pages 338-349, 2019.","title":"An efficient parallel keyword search engine on knowledge graphs","authors":"Yueji Yang, Divyakant Agrawal, H V Jagadish, K H Anthony, Shuang Tung, Wu","year":"2019","venue":"ICDE 2019","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhou et al., 2020] Tianshuo Zhou, Ziyang Li, Gong Cheng, Jun Wang, and Yuang Wei. GREASE: A generative model for relevance search over knowledge graphs. In WSDM 2020, pages 780-788, 2020.","title":"GREASE: A generative model for relevance search over knowledge graphs","authors":"Zhou","year":"2020","venue":"WSDM 2020","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhu et al., 2018] Yuanyuan Zhu, Qian Zhang, Lu Qin, Lijun Chang, and Jeffrey Xu Yu. Querying cohesive subgraphs by keywords. In ICDE 2018, pages 1324-1327, 2018.","title":"Querying cohesive subgraphs by keywords","authors":"Yuanyuan Zhu, Qian Zhang, Lu Qin, Lijun Chang, Jeffrey Xu, Yu","year":"2018","venue":"ICDE 2018","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}