CMPUT 605 - Reading List (in progress)

UNIT 0: Introduction to the relevant topics

• a. A reading on finding similar items, Chapter 3 from the book Mining of Massive Datasets, Leskovec, Rajaraman, and Ullman, 2014 (more emphasis on Sec, 3.3-3.7).
• c. A reading on neural nets and deep learning, Chapter 13 from the book Mining of Massive Datasets, Leskovec, Rajaraman, and Ullman, 2014.

UNIT 1: Entity type detection

• a. Y. Onoe, M. Boratko, A. McCallum, G. Durrett. Modeling fine-grained entity types with box embeddings. arXiv preprint arXiv:2101.00345. 2021 [Maliha]
• b. H. Zhang et al. Learning with noise: improving distantly-supervised fine-grained entity typing via automatic relabeling, IJCAI 2021. [Mohammadreza]
• c. Y. Onoe, G. Durrett. Fine-grained entity typing for domain-independent entity linking. AAAI 2020. [Maliha]

UNIT 2: Knowledge graph auto-completion

• a. Heng Ji, Ralph Grishman, Knowledge base population: successful approaches and challenges, ACL 2011. [Maliha]
• b. Baoxu Shi, Tim Weninger, Open-world knowledge graph completion, AAAI 2018. [Mohammadreza]

UNIT 3: Text to data (or Natural language interfaces to databases)

• a. Yaghmazadeh, Navid, Yuepeng Wang, Isil Dillig, and Thomas Dillig. SQLizer: query synthesis from natural language, OOPSLA 2017.
• b. Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. RATSQL: Relation-aware schema encoding and linking for text-to-SQL parsers, ACL 2020. [Maliha]
• c. Li Done, Mirella Lapata. Language to logical form with neural attention. ACL, 2016.
• d. Li Dong, Mirella Lapata. Coarse-to-Fine Decoding for Neural Semantic Parsing. ACL 2018.
• e. Ruichu Cai, Boyan Xu, Zhenjie Zhang, Xiaoyan Yang, Zijian Li, Zhihao Liang. An Encoder-Decoder Framework Translating Natural Language to Database Queries. IJCAI 2018. [Mohammadreza]
• f. Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task, EMNLP 2018. (dataset-not good for presentation)
• g. Zhong, Victor, Caiming Xiong, and Richard Socher. Seq2sql: Generating structured queries from natural language using reinforcement learning, arXiv preprint arXiv:1709.00103 (2017). (dataset-not good for presentation)

UNIT 4: Semantic equivalence of SQL queries

• a. Hyeonji Kim, Byeong-Hoon So, Wook-Shin Han, Hongrae Lee. Natural language to SQL: Where are we today?, VLDB 2020. (overview/survey-not good for presentation)
• b. S. Chu, B. Murphy, J. Roesch, A. Cheung, and D. Suciu. Axiomatic foundations and algorithms for deciding semantic equivalences of SQL queries. PVLDB, 11(11):1482–1495, 2018.
• c. J. Castelein, M. F. Aniche, M. Soltani, A. Panichella, and A. van Deursen. Search-based test data generation for SQL queries. ICSE 2018. [Mohammadreza]
• d. S. Chu, C. Wang, K. Weitz, and A. Cheung. Cosette: An automated prover for SQL. CIDR 2017. (demo-not good for presentation)
• e. Ruiqi Zhong, Tao Yu, Dan Klein. Semantic Evaluation for Text-to-SQL with Distilled Test Suites. arXiv:2010.02840. 2020. [Maliha]

UNIT 5: Data to text

• a. Suadaa, Lya Hulliyyatus, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura, and Hiroya Takamura. Towards table-to-text generation with numerical reasoning, ACL 2021. [Mohammadreza]
• b. Puduppully, Ratish, Li Dong, and Mirella Lapata. Data-to-text generation with entity modeling, ACL 2019.
• c. Chen, Wenhu, Jianshu Chen, Yu Su, Zhiyu Chen, and William Yang Wang. Logical natural language generation from open-domain tables, ACL 2020. [Maliha]
• d. Parikh, Ankur P., Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. Totto: A controlled table-to-text generation dataset, ACL 2020. (dataset-not good for presentation)
• e. Nan, Linyong, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang et al. DART: Open-Domain Structured Data Record to Text Generation, NAACL 2021. (dataset-not good for presentation)
• f. Nan, Linyong, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński et al. FeTaQA: Free-form Table Question Answering, arXiv preprint arXiv:2104.00369 (2021). (dataset-not good for presentation)

UNIT 6: Querying and integrating data lakes

• a. F. Nargesian, E. Zhu, Ken Pu, R.J. Miller: Table union search on open data, VLDB 2018.
• b. E. Zhu, F. Nargesian, K.Q. Pu, and R.J. Miller: LSH ensemble: Internet-scale domain search, VLDB 2016.
• c. A. Alserafi, A. Abello, O. Romero, T. Calders. Keeping the Data Lake in Form: Proximity Mining for Pre-Filtering Schema Matching, TIS 2020.
• d. O. Lehmberg, C. Bizer: Stitching web tables for improving matching quality, VLDB 2017.
• e. A. Dargahi, D. Rafiei: Efficiently Transforming tables for joinability. CoRR abs/2111.09912 (2021).
• f. A. Hasnat, D. Rafiei: Interactive set discovery. CoRR abs/2111.09917 (2021).

UNIT 7: Query generation from examples

• a. Y.Y. Weiss, S. Cohen. Reverse engineering spj-queries from examples, PODS 2017.
• b. D.V. Kalashnikov, L. Lakshmanan, D. Srivastava. FastQRE: Fast query reverse engineering, SIGMOD 2018.
• c. F. Psallidas,B. Ding, K. Chakrabarti, S. Chaudhuri. S4: Top-k spreadsheet-style search for query discovery, SIGMOD 2015.
• d. A. Bonifati, R. Ciucanu, S. Staworko. Interactive Inference of Join Queries, EDBT 2014: 451-462.

UNIT 8: Fake news detection

• a. Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter 19, no. 1 (2017): 22-36. (overview-not godo for presentation)
• b. Kai Shu, Suhang Wang, and Huan Liu. Beyond news contents: The role of social context for fake news detection. WSDM 2019.
• c. Linmei Ui, Tianchi Yang, Luhao Zhang, Wanjun Zhong, Duyu Tang, Chuan Shi, Nan Duan, and Ming Zhou. Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge. ACL 2021.

UNIT 9: Open world databases

• a. I.I. Ceylan, A. Darwiche, G. Van den Broeck. Open-world probabilistic databases: Semantics, algorithms, complexity, Artificial Intelligence 295 (2021): 103474. (an earlier/shorter version in Description Logics 2016).
• b. L. Orr, S. Ainsworth, W. Cai, K. Jamieson, M. Balazinska, D. Suciu. Mosaic: a sample-based database system for open world query processing. arXiv preprint arXiv:1912.07777 (2019).
• c. E. Ruckhaus, E. Ruiz, M-E. Vidal. Query evaluation and optimization in the semantic web. Theory and Practice of Logic Programming 8, no. 3 (2008): 393-409.
• d. L. Orr, M. Balazinska, D. Suciu. Sample debiasing in the themis open world database system. SIGMOD 2020.

Items marked as ** are given as additional references and are not for weekly discussions.