ABSTRACT
Users often need to gather information about "entities" of interest. Recent efforts try to automate this task by leveraging the vast corpus of HTML tables; this is referred to as "entity augmentation". The accuracy of entity augmentation critically depends on semantic relationships between web tables as well as semantic labels of those tables. Current techniques work well for string-valued and static attributes but perform poorly for numeric and time-varying attributes.
In this paper, we first build a semantic graph that (i) labels columns with unit, scale and timestamp information and (ii) computes semantic matches between columns even when the same numeric attribute is expressed in different units or scales. Second, we develop a novel entity augmentation API suited for numeric and time-varying attributes that leverages the semantic graph. Building the graph is challenging as such label information is often missing from the column headers. Our key insight is to leverage the wealth of tables on the web and infer label information from semantically matching columns of other web tables; this complements "local" extraction from column headers. However, this creates an interdependence between labels and semantic matches; we address this challenge by representing the task as a probabilistic graphical model that jointly discovers labels and semantic matches over all columns. Our experiments on real-life datasets show that (i) our semantic graph contains higher quality labels and semantic matches and (ii) entity augmentation based on the above graph has significantly higher precision and recall compared with the state-of-the-art.
- City Mayors. http://www.citymayors.com/statistics/largest-cities-mayors-1.html.Google Scholar
- Forbes Global 2000. http://www.forbes.com/lists/2012/18/global2000_2011.html.Google Scholar
- List of countries by area. http://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_area.Google Scholar
- Tax Foundation. http://taxfoundation.org/article/oecd-corporate-income-tax-rates-1981-2012.Google Scholar
- M. J. Cafarella, A. Y. Halevy, and N. Khoussainova. Data integration for the relational web. PVLDB, 2(1):1090--1101, 2009. Google ScholarDigital Library
- M. J. Cafarella, A. Y. Halevy, D. Z. Wang, E. Wu, and Y. Zhang. Webtables: exploring the power of tables on the web. PVLDB, 1(1):538--549, 2008. Google ScholarDigital Library
- M. J. Cafarella, A. Y. Halevy, Y. Zhang, D. Z. Wang, and E. Wu. Uncovering the relational web. WebDB, 2008.Google Scholar
- R. Dhamankar, Y. Lee, A. Doan, A. Halevy, and P. Domingos. iMAP: discovering complex semantic matches between database schemas. SIGMOD, pages 383--394, 2004. Google ScholarDigital Library
- A. Doan and A. Y. Halevy. Semantic integration research in the database community: A brief survey. AI Magazine, 26:83--94, 2005. Google ScholarDigital Library
- J. Kleinberg and E. Tardos. Approximation algorithms for classification problems with pairwise relationships: metric labeling and markov random fields. J. ACM, 49(5):616--639, Sept. 2002. Google ScholarDigital Library
- D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009. Google ScholarDigital Library
- G. Limaye, S. Sarawagi, and S. Chakrabarti. Annotating and searching web tables using entities, types and relationships. PVLDB, 3(1):1338--1347, 2010. Google ScholarDigital Library
- G. Malewicz, M. H. Austern, A. J. C. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. Pregel: a system for large-scale graph processing. SIGMOD, pages 135--146, 2010. Google ScholarDigital Library
- R. Pimplikar and S. Sarawagi. Answering table queries on the web using column keywords. PVLDB, 5(10):908--919, 2012. Google ScholarDigital Library
- E. Rahm and P. A. Bernstein. A survey of approaches to automatic schema matching. VLDB J., 10(4):334--350, 2001. Google ScholarDigital Library
- A. D. Sarma, L. Fang, N. Gupta, A. Y. Halevy, H. Lee, F. Wu, R. Xin, and C. Yu. Finding related tables. SIGMOD, pages 817--828, 2012. Google ScholarDigital Library
- B. Shao, H. Wang, and Y. Li. The trinity graph engine.Google Scholar
- P. P. Talukdar, D. Wijaya, and T. Mitchell. Coupled temporal scoping of relational facts. WSDM, pages 73--82, 2012. Google ScholarDigital Library
- P. Venetis, A. Y. Halevy, J. Madhavan, M. Pasca, W. Shen, F. Wu, G. Miao, and C. Wu. Recovering semantics of tables on the web. PVLDB, 4(9):528--538, 2011. Google ScholarDigital Library
- Y. Wang, B. Yang, L. Qu, M. Spaniol, and G. Weikum. Harvesting facts from textual web sources by constrained label propagation. CIKM, pages 837--846, 2011. Google ScholarDigital Library
- M. Yakout, K. Ganjam, K. Chakrabarti, and S. Chaudhuri. Infogather: entity augmentation and attribute discovery by holistic matching with web tables. SIGMOD, pages 97--108, 2012. Google ScholarDigital Library
Index Terms
- InfoGather+: semantic matching and annotation of numeric and time-varying attributes in web tables
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