Publications

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Hwang, H., Lauw, H., Getoor, L. & Ntoulas, A. Organizing User Search Histories. IEEE Transactions on Knowledge and Data Engineering (2010).
Somasundaran, S., Namata, G. Mark, Getoor, L. & Wiebe, J. Opinion Graphs for Polarity and Discourse Classification. TextGraphs-4: Graph-based Methods for Natural Language Processing (2009).PDF icon somasundaran-textgraphs09.pdf (289.75 KB)
Pujara, J., Miao, H., Getoor, L. & Cohen, W. Ontology-Aware Partitioning for Knowledge Graph Identification. CIKM Workshop on Automatic Knowledge Base Construction (2013).PDF icon pujara_akbc13.pdf (370.62 KB)
Getoor, L. & Fromherz, M. Online Scheduling for Reprographic Machines. Working notes AAAI Workshop on Online Search (1997).
Pujara, J., London, B., Getoor, L. & Cohen, W. Online Inference for Knowledge Graph Construction. Workshop on Statistical Relational AI (2015).PDF icon pujara-starai15.pdf (340.95 KB)
Bhattacharya, I. & Getoor, L. Online Collective Entity Resolution. The 22nd National Conference on Artificial Intelligence (NECTAR Track) (AAAI Press, 2007).PDF icon nectar07.pdf (395.24 KB)
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Rekatsinas, T., Deshpande, A. & Getoor, L. Local Structure and Determinism in Probabilistic Databases. ACM SIGMOD Conference (2012).PDF icon sigmod_AAC2012.pdf (490.28 KB)
Lu, Q. & Getoor, L. Link-based Text Classification. IJCAI Workshop on "Text Mining and Link Analysis" (2003).PDF icon ijcai03-ws.pdf (97.25 KB)
Lu, Q. & Getoor, L. Link-based Classification Using Labeled and Unlabeled Data. ICML Workshop on "The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining (2003).PDF icon icml03-ws.pdf (274.65 KB)
Sen, P. & Getoor, L. Link-based Classification. (2007).PDF icon senum-tr07.pdf (511.11 KB)
Getoor, L. Advanced Methods for Knowledge Discovery from Complex Data (Maulik, U., Holder, L. & Cook, D.) (Springer-Verlag, 2005).
Lu, Q. & Getoor, L. Link-based Classification. Proceedings of the International Conference on Machine Learning (ICML) (2003).PDF icon lu-icml03.pdf (195.81 KB)
Bilgic, M. & Getoor, L. Link-based Active Learning. NIPS Workshop on Analyzing Networks and Learning with Graphs (2009).PDF icon mbilgic-nips09wkshp.pdf (116.35 KB)
Namata, G. Mark & Getoor, L. Link Prediction. Encyclopedia of Machine Learning (2010).
Getoor, L. & Diehl, C. Link Mining: A Survey. SigKDD Explorations Special Issue on Link Mining 7, (2005).
Getoor, L. Link Mining: A New Data Mining Challenge. SIGKDD Explorations, volume 5, 85- -89 (2003).
Kimmig, A., Mihalkova, L. & Getoor, L. Lifted graphical models: a survey. Machine Learning 1-45 (2014).
Kimmig, A., Mihalkova, L. & Getoor, L. Lifted graphical models: a survey. Machine Learning Journal 99, 1–45 (2015).PDF icon kimmig-mlj15.pdf (785.58 KB)
Srinivasan, S., Babaki, B., Farnadi, G. & Getoor, L. Lifted Hinge-Loss Markov Random Fields. 33rd AAAI Conference on Artificial Intelligence (2019).PDF icon srinivasan-aaai19.pdf (417.5 KB)
Mihalkova, L. & Getoor, L. Lifted Graphical Models: A Survey. (2011).PDF icon 1107.4966v2.pdf (446.54 KB)
Smith, M., Barash, V., Getoor, L. & Lauw, H. Leveraging Social Context for Searching Social Media. CIKM Workshop on Search in Social Media (2008).
Udrea, O., Getoor, L. & Miller, R. Leveraging Data and Structure in Ontology Integration. Proceedings of ACM-SIGMOD 2007 International Conference on Management 449–460 (2007).PDF icon p449.pdf (509.48 KB)
Mihalkova, L., Moustafa, W. Eldin & Getoor, L. Learning to Predict Web Collaborations. WSDM Workshop on User Modeling for Web Applications (2011).PDF icon mihalkova-wikiCollabs.pdf (353.9 KB)
Getoor, L., Friedman, N. & Koller, D. Learning Structured Statistical Models from Relational Data. Electronic Transactions on Artificial Intelligence 6, (2002).
Getoor, L. Learning Statistical Models from Relational Data. (2001).PDF icon getoor-thesis.pdf (3.39 MB)
Getoor, L., Koller, D., Taskar, B. & Friedman, N. Learning Probabilistic Relational Models with Structural Uncertainty. Proceedings of the AAAI Workshop on Learning Statistical Models from Relational Data (2000).
Getoor, L., Friedman, N., Koller, D. & Pfeffer, A. Relational Data Mining (Dzeroski, S. & Lavrac, N.) (Springer-Verlag, 2001).
Friedman, N., Getoor, L., Koller, D. & Pfeffer, A. Learning Probabilistic Relational Models. International Joint Conference on Arti cial Intelligence (1999).PDF icon icjai99.pdf (156.94 KB)
Getoor, L., Friedman, N., Koller, D. & Pfeffer, A. Learning Probabilistic Relational Models. Relational Data Mining (Springer-Verlag, 2001).PDF icon lprm-ch.pdf (376 KB)
Getoor, L., Friedman, N., Koller, D. & Taskar, B. Learning Probabilistic Models of Relational Structure. Proceedings of International Conference on Machine Learning (ICML) (2001).PDF icon icml01.pdf (157.91 KB)
Getoor, L., Friedman, N., Koller, D. & Taskar, B. Learning Probabilistic Models of Link Structure. Journal of Machine Learning Research 3, 679- -707 (2002).PDF icon jmlr02.pdf (502.22 KB)
Bach, S. H., Huang, B. & Getoor, L. Learning Latent Groups with Hinge-loss Markov Random Fields. ICML Workshop on Inferning: Interactions between Inference and Learning (2013).PDF icon bach-inferning13.pdf (348.79 KB)
Ramesh, A., Goldwasser, D., Huang, B., III, H. Daume & Getoor, L. Learning Latent Engagement Patterns of Students in Online Courses. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI Press, 2014).PDF icon ramesh-aaai14.pdf (505.47 KB)
Doppa, J., Yu, J., Tadepalli, P. & Getoor, L. Learning Algorithms for Link Prediction based on Chance Constraints. European Conference on Machine Learning (ECML) (2010).PDF icon doppa-ecml10.pdf (203 KB)
Foulds, J., Kumar, S. & Getoor, L. Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models. International Conference on Machine Learning (ICML) (2015).PDF icon Foulds2015LatentTopicNetworks.pdf (382.53 KB)

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