A Survey of Web Information Extraction Systems
IEEE Transactions on Knowledge and Data Engineering
WebTables: exploring the power of tables on the web
Proceedings of the VLDB Endowment
Acquisition of instance attributes via labeled and related instances
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Attribute retrieval from relational web tables
SPIRE'11 Proceedings of the 18th international conference on String processing and information retrieval
Towards a framework for attribute retrieval
Proceedings of the 20th ACM international conference on Information and knowledge management
Aggregated search: A new information retrieval paradigm
ACM Computing Surveys (CSUR)
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In this paper we propose an attribute retrieval approach which extracts and ranks attributes from Web tables. We combine simple heuristics to filter out improbable attributes and we rank attributes based on frequencies and a table match score. Ranking is reinforced with external evidence from Web search, DBPedia and Wikipedia. Our approach can be applied to whatever instance (e.g. Canada) to retrieve its attributes (capital, GDP). It is shown it has a much higher recall than DBPedia and Wikipedia and that it works better than lexico-syntactic rules for the same purpose.