1 How to make sense out of unstructured data? Yi Chen Dept. of Computer Science and Engineering Arizona State University.

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Presentation transcript:

1 How to make sense out of unstructured data? Yi Chen Dept. of Computer Science and Engineering Arizona State University

2 Databases Have Been a Great Success  for managing structured data  But, 85% of the World’s Data is Not in Databases!

3 How to Obtain Information from Unstructured Data?  Efforts have been made by other areas  Search engines: Google, Yahoo, MSN, Ask,…  Information extraction (IE) [Avatar, TIES, …]  Natural language processing (NLP) [Treebank, UIMA, …]  What can databases do for unstructured data?  XML provides a good basis for representing semi- structured data,  However, challenges remain!! They produce semi-structured data from texts

4 Querying Data Generated from IE  Information extraction produces data about specific entities and relationships  Data generated from information extraction are error prone  incomplete data [Imieliski, Koch,…]  probabilistic databases [Getoor, Jagadish, Halevy, Subrahmanian, Suciu, Tannen, Widom, …]  malleable schemas [Chang, Halevy, Ives…]  Query posed by naïve users are inaccurate  keywords [Agrawal, Chaudhuri, Das, Doan, Gravano, Papakonstantinou, Shanmugasundaram..]  over- or under-specified queries [Chaudhuri..]  natural language queries [Jagadish..]  QUIC: a system that handles data incompleteness and query imprecision at the same time for autonomous databases [CIDR 07, ICDE 07]  Collaborated with Subbarao Kambhampati, Garrett Wolf, Hemal Khatri, Bhaumik Chokshi, Jianchun Fan, and Ullas Nambiar

5 Querying Data Generated from NLP  Natural language processing generates tree structured data (parse trees)  Understanding the lexical structure of a sentence helps query answering  E.g. find the NP after “Bob” and “with” within an NP  Demands queries similar to but different from XQuery/XPath queries S VP NP V Det Prep NP Bob adogtoday saw Alice with PP NP  LPath: a query language for linguistic annotation data generated from NLP over text documents [ICDE06]  Collaborated with Susan Davidson, Steven Bird, Haejoong Lee, and Yifeng Zheng

6 Challenge  How should we close the loop? Documents Data bases Queries Revised queries Result 1 Result 2