A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Semantic-integration research in the database community
AI Magazine - Special issue on semantic integration
WebTables: exploring the power of tables on the web
Proceedings of the VLDB Endowment
Data integration for the relational web
Proceedings of the VLDB Endowment
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Annotating and searching web tables using entities, types and relationships
Proceedings of the VLDB Endowment
Recovering semantics of tables on the web
Proceedings of the VLDB Endowment
Harvesting facts from textual web sources by constrained label propagation
Proceedings of the 20th ACM international conference on Information and knowledge management
Coupled temporal scoping of relational facts
Proceedings of the fifth ACM international conference on Web search and data mining
InfoGather: entity augmentation and attribute discovery by holistic matching with web tables
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Answering table queries on the web using column keywords
Proceedings of the VLDB Endowment
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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.