Lise Getoor
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Lise Getoor is a person.
- See: Record Coreference Resolution Methods, Probabilistic Relational Learning, Link-based Classification Methods, Probabilistic Soft Logic, LINQS Research Group.
References
- Personal Homepage: https://getoor.soe.ucsc.edu/ (OLD http://cs.umd.edu/~getoor/)
- DBLP Author Page: http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/g/Getoor:Lise.html
- Google Scholar Author Page: http://scholar.google.com/citations?user=Y8-xGncAAAAJ
- http://GM-RKB.com/Special:SearchByProperty/Author/Lise-20Getoor
2015
- (Bach et al., 2015) ⇒ Stephen H Bach, Matthias Broecheler, Bert Huang, and Lise Getoor. (2015). “Hinge-loss Markov Random Fields and Probabilistic Soft Logic.” In: arXiv preprint arXiv:1505.04406.
2013
- (Pujara et al., 2013) ⇒ Jay Pujara, Hui Miao, Lise Getoor, and William Cohen. (2013). “Knowledge Graph Identification.” In: Proceedings of the 12th International Semantic Web Conference - Part I. ISBN:978-3-642-41334-6 doi:10.1007/978-3-642-41335-3_34
2012
- (Getoor, 2012) ⇒ Lise Getoor. (2012). “Representation, Inference and Learning in Structured Statistical Models." Tutorial at NIPS 2012.
- (Kimmig et al., 2012) ⇒ Angelika Kimmig, Stephen Bach, Matthias Broecheler, Bert Huang, and Lise Getoor. (2012). “A Short Introduction to Probabilistic Soft Logic.” In: Proceedings of the NIPS Workshop on Probabilistic Programming: Foundations and Applications.
2009
- (Bilgic & Getoor, 2009) ⇒ Mustafa Bilgic, and Lise Getoor. (2009). “Reflect and Correct: A misclassification prediction approach to active inference.” In: ACM Transactions on Knowledge Discovery from Data (TKDD), 3(4). doi:10.1145/1631162.1631168
- Link-based Classification http://www.cs.umd.edu/projects/linqs/projects/lbc/index.html
- Traditional machine learning classification algorithms aim to label entities on the basis of their attribute values. Many real-world datasets, however, contain interlinked entities and exhibit correlations among labels of the interlinked entities. Link-based classification aims to improve classification accuracy by exploiting such correlations in the link structure besides utilizing the attribute values of each entity. ...
2008
- (Kang et al., 2008) ⇒ Hyunmo Kang, Lise Getoor, Ben Shneiderman, Mustafa Bilgic, and Louis Licamele. (2008). “Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation.” In: IEEE Transactions on Visualization and Computer Graphics, 14,(5) (TVCG 2008).
- (Sen, Namata et al., 2008) ⇒ Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. "Collective classification in network data." AI magazine 29, no. 3 (2008): 93.
2007
- (Bhattacharya & Getoor, 2007) ⇒ Indrajit Bhattacharya, and Lise Getoor. (2007). “Collective Entity Resolution In Relational Data.” In: ACM Transactions on Knowledge Discovery from Data, 1(1) (TKDD). doi:10.1145/1217299.1217304.
- (Getoor & Taskar, 2007) ⇒ Lise Getoor, and Ben Taskar, editors. (2007). “Introduction to Statistical Relational Learning." MIT Press. ISBN:0262072882.
2006
- (Bhattacharya & Getoor, 2006) ⇒ Indrajit Bhattacharya, and Lise Getoor. (2006). “A Latent Dirichlet Model for Unsupervised Entity Resolution.” In: Proceedings of the Sixth SIAM International Conference on Data Mining (SIAM 2006).
- (Bhattacharya et al., 2006) ⇒ Indrajit Bhattacharya, Louis Licamele, Lise Getoor. (2006). “Relational Clustering for Entity Resolution Queries.” In: Proceedings of the ICML 2006 Workshop on Statistical Relational Learning (SRL).
- ABSTRACT: The goal of entity resolution is to reconcile database references corresponding to the same real-world entities. Given the abundance of publicly available databases where entities are not resolved, we motivate the problem of quickly processing queries that require resolved entities from such ‘unclean’ databases. We first propose a cut-based relational clustering formulation for collective entity resolution. We then show how it can be performed on-the-fly by adaptively extracting and resolving those database references that are the most helpful for resolving the query. We validate our approach on two large real-world publication databases, where we show the usefulness of collective resolution and at the same time demonstrate the need for adaptive strategies for query processing. We then show how the same queries can be answered in real time using our adaptive approach while preserving the gains of collective resolution. 1.
- Presentation: http://www.cs.umd.edu/projects/srl2006/Slides/bhattacharya-srl06-poster.ppt
- (Getoor, 2006) ⇒ Lise Getoor. (2006). “Entity Resolution in Relational Data." Presentation at Second International Workshop on Exchange and Integration of Data.
2005
- (Getoor & Diehl, 2005) ⇒ Lise Getoor, and Christopher P. Diehl. (2005). “Link Mining: A survey.” In: SIGKDD Explorations, 7(2). doi:10.1145/1117454.1117456
2004
- (Bhattacharya & Getoor, 2004a) ⇒ Indrajit Bhattacharya, and Lise Getoor. (2004). “Iterative Record Linkage for Cleaning and Integration.” In: Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. doi:10.1145/1008694.1008697
- (Bhattacharya & Getoor, 2004b) ⇒ Indrajit Bhattacharya, and Lise Getoor. (2004). “Deduplication and Group Detection Using Links.” In: Proceedings of 10th ACM SIGKDD Workshop on Link Analysis and Group Detection.
- (Bhattacharya et al., 2004) ⇒ Indrajit Bhattacharya, Lise Getoor, and Yoshua Bengio. (2004). “Word Sense Disambiguation using Probabilistic Models.” In: Proceedings of ACL 2004.
2002
- (Getoor et al., 2002) ⇒ Lise Getoor, Nir Fridman, Daphne Koller, and Benjamin Taskar. (2002). “Learning Probabilistic Models of Link Structure.” In: Journal Machine Learning Research, 3.
2000
- (Getoor, 2000) ⇒ Lise Getoor. (2000). “Learning Probabilistic Relational Models.” In: 4th International Symposium on Abstraction, Reformulation, and Approximation (SARA 2000). doi:10.1007/3-540-44914-0.
- ABSTRACT: My work is on learning Probabilistic Relational Models (PRMs) from structured data (e.g., data in a relational database, an object-oriented database or a frame-based system). This work has as a starting point the framework of Probabilistic Relational Models, introduced in [5, 7]. We adapt and extend the machinery that has been developed over the years for learning Bayesian networks from data [1, 4, 6] to the task of learning PRMs from structured data. At the heart of this work is a search algorithm that explores the space of legal models using search operators that abstract or refine the model.
1999
- (Friedman et al., 1999) ⇒ Nir Friedman, Lise Getoor, Daphne Koller, and A. Pfeffer. (1999). “Learning Probabilistic Relational Models.” In: IJCAI 1999.