Matt Michelson

Matthew Michelson

Fetch Technologies, Inc.
841 Apollo Street, Suite 400
El Segundo, CA 90245

Email:

Voice: (310) 414-9849 x270
Publications | Data and Software | Videos of Talks

Welcome to my website. My name is Matt Michelson. I received my Ph.D. from the Computer Science Department at the University of Southern California. I was a member of the Information Integration research group led by Prof. Craig Knoblock, which is part of the Information Sciences Institute. Graduate school was super fun and I learend a lot. I am now very fortunate to work as a senior scientist at Fetch Technologies, in the labs division.

Research Interests

My research interest lies in the areas of information integration and machine learning. I am especially interested in information extraction and record linkage (sometimes called entity resolution).

Education

Ph.D., Computer Science, 2009, University of Southern California
M.S., Computer Science, 2005, University of Southern California
B.S., Computer Science, 2002, The Johns Hopkins University

Publications

2009

Matthew Michelson and Craig A. Knoblock, Exploiting Background Knowledge to Build Reference Sets for Information Extraction, In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI), Pasadena, CA, 2009 [PDF, bibtex]
Matthew Michelson and Sofus A. Macskassy, Layered, Multivariate Anomaly Explanations: A First Look, In Proceedings of the International Workshop on Statistical Relational Learning (SRL), Leuven, Belgium, 2009 [PDF, bibtex]
Matthew Michelson and Sofus A. Macskassy, Judging the Performance of Cascading Models: A First Look, In Proceedings of the 4th workshop on Evaluation Methods for Machine Learning, Montreal, Canada, 2009 [PDF, bibtex]
Matthew Michelson and Sofus A. Macskassy, Record Linkage Measures in an Entity Centric World, In Proceedings of the 4th workshop on Evaluation Methods for Machine Learning, Montreal, Canada, 2009 [PDF, bibtex]
Matthew Michelson, Sofus A. Macskassy, and Steven N. Minton, Flexible Query Formulation for Federated Search, In Proceedings of the 7th workshop on Information Integration on the Web (IIWeb), Pasadena, CA, 2009 [PDF, bibtex]

2008

Matthew Michelson and Craig A. Knoblock, Creating Relational Data from Unstructured and Ungrammatical Data Sources, Journal of Artificial Intelligence Research (JAIR), 31, p.543-590, 2008 [PDF, Abstract, bibtex]
Matthew Michelson, Aman Goel, and Craig A. Knoblock, Identifyinig Maps on the World Wide Web, In Proceedings of the 5th International Conference on Geographic Information Science (GIScience), Park City, Utah, 2008 [PDF, SpringerLink, bibtex]
Craig A. Knoblock, Jose Luis Ambite, Mark Carman, Matthew Michelson, Pedro Szekely, and Rattapoom Tuchinda, Beyond the Elves: Making Intelligent Agents Intelligent, AI Magazine, 29 (2), p.33-42, 2008 [AAAI link, bibtex]

2007

Matthew Michelson and Craig A. Knoblock, Unsupervised Information Extraction from Unstructured, Ungrammatical Data Sources on the World Wide Web, International Journal of Document Analysis and Recognition (IJDAR), Special Issue on Noisy Text Analytics, 10, p.211-226, 2007 [PDF, SpringerLink, bibtex]
Matthew Michelson and Craig A. Knoblock, Mining Heterogeneous Transformations for Record Linkage, In Proceedings of the 6th International Workshop on Information Integration on the Web (IIWEB-07), Vancouver, Canada, 2007 [PDF, bibtex]
Matthew Michelson and Craig A. Knoblock, Beginning to Understand Unstructured, Ungrammatical Text: An Information Integration Approach, In Proceedings of the AAAI Spring Symposium on Machine Reading, Stanford, CA, 2007 [PDF, bibtex]
Matthew Michelson and Craig A. Knoblock, An Automatic Approach to Semantic Annotation of Unstructured, Ungrammatical Sources: A First Look, In Proceedings of the 1st IJCAI Workshop on Analytics for Noisy Unstructured Text Data (AND-07), Hyderabad, India, 2007 [PDF, bibtex]

2006

Matthew Michelson and Craig A. Knoblock, Learning Blocking Schemes for Record Linkage, In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI-06), Boston, MA, 2006 [PDF, bibtex]
Matthew Michelson and Craig A. Knoblock, Phoebus: A System for Extracting and Integrating Data from Unstructured and Ungrammatical Sources, In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI-06), Intelligent Systems Demonstrations, Boston, MA, 2006 [PDF, bibtex]

2005

Matthew Michelson and Craig A. Knoblock, Semantic Annotation of Unstructured and Ungrammatical Text, In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-05), Edinburgh, Scotland, 2005 [PDF, bibtex]
Steven N. Minton, Claude Nanjo, Craig A. Knoblock, Martin Michalowski, and Matthew Michelson, A Heterogeneous Field Matching Method for Record Linkage, In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM-05), Houston, TX, 2005 [PDF, bibtex]
Matthew Michelson and Craig A. Knoblock. Beyond keyword search for data sources on the World Wide Web Software Developer's Journal Extra, 1/2006: 44-48. [Check it out in French or Spanish!]
Matthew Michelson, Building Queryable Datasets from Ungrammatical and Unstructured Sources, Master's thesis, University of Southern California, 2005 [PDF]

Data and Software

Data sets

Bidding For Travel data set [Record Linkage and Info. Extraction]

Software

Information Extraction from Unstructured, Ungrammatical Data using Reference Sets
This code allows for information extraction from unstructured, ungrammatical data sources (such as classified ads and auction listings) by exploiting reference sets. This implementation includes both our machine learning approach (JAIR 2008 paper) and our automatic approach (IJDAR 2007 paper). The code freely available.
BSL: Blocking Scheme Learner
This code implements the algorithm presented in the Learning Blocking Schemes for Record Linkage paper. Blocking schemes are rules to efficiently generate candidate matches in record linkage, without pruning too many true positives from the set. For more on this, please read the paper.

Videos of Talks

A Reference-Set Approach to Information Extraction from Unstructured, Ungrammatical Data Sources, USC Information Sciences Institute, AI Seminars Series, 2009 [Description] [Webcast]