Towards Efficient and Effective Semantic Table Interpretation

  • Ziqi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8796)


This paper describes TableMiner, the first semantic Table Interpretation method that adopts an incremental, mutually recursive and bootstrapping learning approach seeded by automatically selected ‘partial’ data from a table. TableMiner labels columns containing named entity mentions with semantic concepts that best describe data in columns, and disambiguates entity content cells in these columns. TableMiner is able to use various types of contextual information outside tables for Table Interpretation, including semantic markups (e.g., RDFa/microdata annotations) that to the best of our knowledge, have never been used in Natural Language Processing tasks. Evaluation on two datasets shows that compared to two baselines, TableMiner consistently obtains the best performance. In the classification task, it achieves significant improvements of between 0.08 and 0.38 F1 depending on different baseline methods; in the disambiguation task, it outperforms both baselines by between 0.19 and 0.37 in Precision on one dataset, and between 0.02 and 0.03 F1 on the other dataset. Observation also shows that the bootstrapping learning approach adopted by TableMiner can potentially deliver computational savings of between 24 and 60% against classic methods that ‘exhaustively’ processes the entire table content to build features for interpretation.


Name Entity Recognition Computational Linguistics Link Open Data Candidate Concept Entity Annotation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ziqi Zhang
    • 1
  1. 1.Department of Computer ScienceUniversity of SheffieldUK

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