AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Journal of Computer Science and Technology Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Regular Paper

Context-Aware Semantic Type Identification for Relational Attributes

Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education, Renmin University of China Beijing 100872, China
School of Information, Renmin University of China, Beijing 100872, China
Tencent (Beijing) Technology Company Limited, Beijing 100080, China
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
Tencent (Shenzhen) Technology Company Limited, Shenzhen 518057, China
Show Author Information

Abstract

Identifying semantic types for attributes in relations, known as attribute semantic type (AST) identification, plays an important role in many data analysis tasks, such as data cleaning, schema matching, and keyword search in databases. However, due to a lack of unified naming standards across prevalent information systems (a.k.a. information islands), AST identification still remains as an open problem. To tackle this problem, we propose a context-aware method to figure out the ASTs for relations in this paper. We transform the AST identification into a multi-class classification problem and propose a schema context aware (SCA) model to learn the representation from a collection of relations associated with attribute values and schema context. Based on the learned representation, we predict the AST for a given attribute from an underlying relation, wherein the predicted AST is mapped to one of the labeled ASTs. To improve the performance for AST identification, especially for the case that the predicted semantic types of attributes are not included in the labeled ASTs, we then introduce knowledge base embeddings (a.k.a. KBVec) to enhance the above representation and construct a schema context aware model with knowledge base enhanced (SCA-KB) to get a stable and robust model. Extensive experiments based on real datasets demonstrate that our context-aware method outperforms the state-of-the-art approaches by a large margin, up to 6.14% and 25.17% in terms of macro average F1 score, and up to 0.28% and 9.56% in terms of weighted F1 score over high-quality and low-quality datasets respectively.

Keywords

attribute semantic type (AST) identificationcontext-awaresemantic embeddingknowledge base embedding

Electronic Supplementary Material

Download File(s)
JCST-2010-11048-Highlights.pdf (708.8 KB)

References

[1]
Kandel S, Paepcke A, Hellerstein J, Heer J. Wrangler: Interactive visual specification of data transformation scripts. In Proc. the 2011 SIGCHI Conference on Human Factors in Computing Systems, May 2011, pp.3363–3372. DOI: 10.1145/1978942.1979444.
Crossref
[2]

Rahm E, Bernstein P A. A survey of approaches to automatic schema matching. The VLDB Journal, 2001, 10(4): 334–350. DOI: 10.1007/s007780100057.
Crossref Google Scholar
[3]
Zapilko B, Zloch M, Schaible J. Utilizing regular expressions for instance-based schema matching. In Proc. the 7th International Conference on Ontology Matching, Nov. 2012, pp.240–241. DOI: 10.5555/2887596.2887623.
[4]

Venetis P, Halevy A, Madhavan J, Paşca M, Shen W, Wu F, Miao G X, Wu C. Recovering semantics of tables on the web. Proceedings of the VLDB Endowment, 2011, 4(9): 528–538. DOI: 10.14778/2002938.2002939.
Crossref Google Scholar
[5]

Snipes G. Google data studio. Journal of Librarianship and Scholarly Communication, 2018, 6(1): eP2214. DOI: 10.7710/2162-3309.2214.
Crossref Google Scholar
[6]
Kaelin M. Microsoft power BI: A cheat sheet. Technical Report, Techrepublic, 2019. https://www.techrepublic.com/article/microsoft-power-bi-a-smart-persons-guide, July 2023.
[7]
Black D. Data wrangling ‘decoder ring’ homogenizes polyglot data lakes. Technical Report, Enterprise Tech., 2016. https://www.enterpriseai.news/2016/02/11/trifactas-data-wrangling-decoder-ring-homogenizes-polyglot-data-lakes/, July 2023.
[8]
Zhao C, He Y Y. Auto-EM: End-to-end fuzzy entity-matching using pre-trained deep models and transfer learning. In Proc. the 2019 World Wide Web Conference, May 2019, pp.2413–2424. DOI: 10.1145/3308558.3313578.
Crossref
[9]
Chen J Y, Jiménez-Ruiz E, Horrocks I, Sutton C. Learning semantic annotations for tabular data. In Proc. the 28th International Joint Conference on Artificial Intelligence, Jul. 2019, pp.2088–2094. DOI: 10.24963/ijcai.2019/289.
Crossref
[10]
Chen J Y, Jiménez-Ruiz E, Horrocks I, Sutton C. ColNet: Embedding the semantics of web tables for column type prediction. In Proc. the 33rd AAAI Conference on Artificial Intelligence, Feb. 2019, pp.29–36. DOI: 10.1609/aaai.v33i01.330129.
Crossref
[11]
Ramnandan S K, Mittal A, Knoblock C A, Szekely P. Assigning semantic labels to data sources. In Proc. the 12th European Semantic Web Conference, May 31–June 4, 2015, pp.403–417. DOI: 10.1007/978-3-319-18818-8_25.
Crossref
[12]
Pham M, Alse S, Knoblock C A, Szekely P. Semantic labeling: A domain-independent approach. In Proc. the 15th International Semantic Web Conference, Oct. 2016, pp.446–462. DOI: 10.1007/978-3-319-46523-4_27.
Crossref
[13]
Hulsebos M, Hu K, Bakker M, Zgraggen E, Satyanarayan A, Kraska T, Demiralp Ç, Hidalgo C. Sherlock: A deep learning approach to semantic data type detection. In Proc. the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Jul. 2019, pp.1500–1508. DOI: 10.1145/3292500.3330993.
Crossref
[14]
Krishna S. Introduction to Database and Knowledge-Base Systems. World Scientific Publishing, 1992. DOI: 10.1142/1374.
Crossref
[15]
Gao Y, Liang J, Han B, Yakout M, Mohamed A. Building a large-scale accurate and fresh knowledge graph. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2018. https://kdd2018tutorialt39.azurewebsites.net/, July 2023.
[16]
Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z. DBpedia: A nucleus for a web of open data. In Proc. the 6th International Semantic Web Conference and the 2nd Asian Semantic Web Conference, Nov. 2007, pp.722–735. DOI: 10.1007/978-3-540-76298-0_52.
Crossref
[17]
Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J. Freebase: A collaboratively created graph database for structuring human knowledge. In Proc. the 2008 ACM SIGMOD International Conference on Management of Data, Jun. 2008, pp.1247–1250. DOI: 10.1145/1376616.1376746.
Crossref
[18]
Rebele T, Suchanek F, Hoffart J, Biega J, Kuzey E, Weikum G. YAGO: A multilingual knowledge base from Wikipedia, Wordnet, and Geonames. In Proc. the 15th International Semantic Web Conference, Oct. 2016, pp.177–185. DOI: 10.1007/978-3-319-46547-0_19.
Crossref
[19]
Zwicklbauer S, Einsiedler C, Granitzer, M, Seifert C. Towards disambiguating Web tables. In Proc. the 2013 International Semantic Web Conference, Oct. 2013, pp.205–208.
[20]
Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press, 2016.
[21]

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436–444. DOI: 10.1038/nature14539.
Crossref Google Scholar
[22]

Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks, 2015, 61: 85–117. DOI: 10.1016/j.neunet.2014.09.003.
Crossref Google Scholar
[23]

Wang W, Zhang M H, Chen G, Jagadish H V, Ooi B C, Tan K L. Database meets deep learning: Challenges and opportunities. ACM SIGMOD Record, 2016, 45(2): 17–22. DOI: 10.1145/3003665.3003669.
Crossref Google Scholar
[24]
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł, Polosukhin I. Attention is all you need. In Proc. the 31st International Conference on Neural Information Processing Systems, Dec. 2017, pp.6000–6010. DOI: 10.5555/3295222.3295349.
[25]
He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2016, pp.770–778. DOI: 10.1109/CVPR.2016.90.
Crossref
[26]
Ba J L, Kiros J R, Hinton G E. Layer normalization. arXiv: 1607.06450, 2016. https://arxiv.org/abs/1607.06450, July 2023.
[27]
Devlin J, Chang M W, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv: 1810.04805, 2018. https://arxiv.org/abs/1810.04805, July 2023.
[28]
May C, Wang A, Bordia S, Bowman S R, Rudinger R. On measuring social biases in sentence encoders. arXiv: 1903.10561, 2019. https://arxiv.org/abs/1903.10561, July 2023.
Crossref
[29]
Qiao Y F, Xiong C Y, Liu Z H, Liu Z Y. Understanding the behaviors of BERT in ranking. arXiv: 1904.07531, 2019. https://arxiv.org/abs/1904.07531, July 2023.
[30]

Harris Z S. Distributional structure. Word, 1954, 10(2/3): 146–162. DOI: 10.1080/00437956.1954.11659520.
Crossref Google Scholar
[31]

Erk K. Vector space models of word meaning and phrase meaning: A survey. Language and Linguistics Compass, 2012, 6(10): 635–653. DOI: 10.1002/lnco.362.
Crossref Google Scholar
[32]
Pennington J, Socher R, Manning C. GloVe: Global vectors for word representation. In Proc. the 2014 Conference on Empirical Methods in Natural Language Processing, Oct. 2014, pp.1532–1543. DOI: 10.3115/v1/D14-1162.
Crossref
[33]
Le Q, Mikolov T. Distributed representations of sentences and documents. In Proc. the 31st International Conference on Machine Learning, Jun. 2014, pp.1188–1196. DOI: 10.5555/3044805.3045025.
[34]

Köpcke H, Thor A, Rahm E. Evaluation of entity resolution approaches on real-world match problems. Proceedings of the VLDB Endowment, 2010, 3(1/2): 484–493. DOI: 10.14778/1920841.1920904.
Crossref Google Scholar
[35]

Konda P, Das S, Suganthan G C P, Doan A, Ardalan A, Ballard J R, Li H, Panahi F, Zhang H J, Naughton J, Prasad S, Krishnan G, Deep R, Raghavendra V. Magellan: Toward building entity matching management systems. Proceedings of the VLDB Endowment, 2016, 9(12): 1197–1208. DOI: 10.14778/2994509.2994535.
Crossref Google Scholar
[36]
Eberius J, Braunschweig K, Hentsch M, Thiele M, Ahmadov A, Lehner W. Building the Dresden Web Table Corpus: A classification approach. In Proc. the 2nd International Symposium on Big Data Computing, Dec. 2015, pp.41–50. DOI: 10.1109/BDC.2015.30.
Crossref
[37]
Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv: 1301.3781, 2013. https://arxiv.org/abs/1301.3781, July 2023.
[38]

Matthews B W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 1975, 405(2):442–451. DOI: 10.1016/0005-2795(75)90109-9.
Crossref Google Scholar
[39]

Chicco D. Ten quick tips for machine learning in computational biology. BioData Mining, 2017, 10(1): Article No. 35. DOI: 10.1186/s13040-017-0155-3.
Crossref Google Scholar
[40]
Everitt B S. The Cambridge Dictionary of Statistics (2nd edition). Cambridge University Press, 2002.
[41]
Efthymiou V, Hassanzadeh O, Rodriguez-Muro M, Christophides V. Matching Web tables with knowledge base entities: From entity lookups to entity embeddings. In Proc. the 16th International Semantic Web Conference, Oct. 2017, pp.260–270. DOI: 10.1007/978-3-319-68288-4_16.
Crossref
[42]
Shen S, Dong Z, Ye J Y, Ma L J, Yao Z W, Gholami A, Mahoney M W, Keutzer K. Q-BERT: Hessian based ultra low precision quantization of BERT. In Proc. the 34th AAAI Conference on Artificial Intelligence, Feb. 2020, pp.8815–8821. DOI: 10.1609/aaai.v34i05.6409.
Crossref
[43]
Jiao X Q, Yin Y C, Shang L F, Jiang X, Chen X, Li L L, Wang F, Liu Q. TinyBERT: Distilling BERT for natural language understanding. arXiv: 1909.10351, 2019. https://arxiv.org/abs/1909.10351, July 2023.
[44]
Lan Z Z, Chen M D, Goodman S, Gimpel K, Sharma P, Soricut R. ALBERT: A lite BERT for self-supervised learning of language representations. arXiv: 1909.11942, 2019. https://arxiv.org/abs/1909.11942, July 2023.
Journal of Computer Science and Technology
Volume 38 Issue 4,
July 2023
Pages 927-946
DOI: 10.1007/s11390-021-1048-y
Cite this article:
Ding Y, Guo Y-H, Lu W, et al. Context-Aware Semantic Type Identification for Relational Attributes. Journal of Computer Science and Technology, 2023, 38(4): 927-946. https://doi.org/10.1007/s11390-021-1048-y

303

Views

2

Crossref

0

Web of Science

1

Scopus

0

CSCD

Altmetrics
Received: 05 October 2020
Accepted: 09 June 2021
Published: 06 December 2023
© Institute of Computing Technology, Chinese Academy of Sciences 2023
Return