Abstract: In recent years,a large number of structured tabular data have emerged on the Internet constantly.However,the value of Web tables depends not only on the data itself,but also on the relatedness between the data.Only when the potential relatedness between them is detected,can these structured data be fully utilized.We proposed a new type of relatedness between Web tables called snapshot relationship,and a framework for capturing snapshots that meet a certain matching condition with a given table.The snapshots are beneficial for query optimization,and also helpful for returning partial results rapidly when querying on big data.The relatedness between an original Web table and its snapshot can be computed based on entity consistency and schema consistency.In order to assign high weights on tables which provide more fresh entities,the concept of entity freshness was introduced into our scoring method.Meanwhile,the content consistency of Web tables can be enhanced by applying Bayesian analysis to our relatedness capturing framework.As a consequence,accuracy of finding snapshots is improved.Extensive experiments demonstrate that the algorithms can capture snapshots with high quality,and perform well in query precision and recall.

Key words: Web tables,Relatedness,Snapshot,Data integration,Query optimization