S. Tejada, C. A. Knoblock, and S. Minton. Learning object identification rules for information integration. Information Systems, 26(8), 2001. Home/Search Document Details and Download
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On Evaluation and Training-Set Construction for Duplicate.. - Bilenko, Mooney (2003) (3 citations) (Correct)
....will not be equivalent, selecting only pairs that are fairly similar accordingly to some default, static metric, may be a good strategy. More sophisticated active learning strategies that dynamically select the next best pair of examples based on the current results of learning can be very useful [15, 16]. We present results on various simple static sample selection strategies and make several methodological suggestions based on the results. Finally, we present an unsupervised strategy for obtaining negative training examples. Since in a typical database the vast majority of randomly selected ....
S. Tejada, C. A. Knoblock, and S. Minton. Learning object identification rules for information integration. Information Systems Journal, 26(8):635--656, 2001.
Record Linkage: Current Practice and Future Directions - Gu, Baxter, Vickers.. (Correct)
....and attribute weights) have recently been proposed. Adequate training data is needed to train these models [109, 64] Proposed models have included: Logistic regression [87] although this was found to not work for census data in [64] Support vector machines [10] Decision trees [103]. Active learning techniques have also been proposed to optimise e#ciency in selection of training records [103] Bayesian Decision Cost Model Verykios et al. 104] propose a Bayesian decision model for cost optimal record matching. Conventional models for record matching rely on decision rules ....
.... [109, 64] Proposed models have included: Logistic regression [87] although this was found to not work for census data in [64] Support vector machines [10] Decision trees [103] Active learning techniques have also been proposed to optimise e#ciency in selection of training records [103]. Bayesian Decision Cost Model Verykios et al. 104] propose a Bayesian decision model for cost optimal record matching. Conventional models for record matching rely on decision rules that minimise the probability of error, i.e. the probability that a sample record pair is assigned to the wrong ....
S. Tejada, C.A. Knoblock, and S. Minton. Learning object identification rules for information integration. Information Systems, 26:607--633, 2001.
Exploiting Secondary Sources for Unsupervised Record.. - Michalowski, Thakkar.. (2004) Self-citation (Knoblock) (Correct)
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S. Tejada, C. A. Knoblock, and S. Minton. Learning object identification rules for information integration. Information Systems, 26(8), 2001.
Deploying Information Agents on the Web - Craig Knoblock University Self-citation (Knoblock) (Correct)
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Sheila Tejada, Craig A. Knoblock, and Steven Minton. Learning object identification rules for information integration. Information Systems, 26(8), 2001.
Deploying Information Agents on the Web - Knoblock Self-citation (Knoblock) (Correct)
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Sheila Tejada, Craig A. Knoblock, and Steven Minton. Learning object identification rules for information integration. Information Systems, 26(8), 2001.
A View Integration Approach to Dynamic Composition of Web.. - Thakkar, Knoblock, Ambite (2003) (4 citations) Self-citation (Knoblock) (Correct)
....cVahe 1 Input LocValue 1, LocValue2 Output: QueryResult Figure 4 Modified Theseus Plan example, one of the key issues when integrating data from various web services is to consolidate information extracted from various data sources. We plan to incorporate object consolidation techniques from [16] as an intelligent join operator in the mediator. The object consolidation techniques allow soft matching the records extracted from various web services. ....
Tejada, S., C.A. Knoblock, and S. Minton, Learning Object Identification Rules for Information Integration. Information Systems, 2001.26(8).
Learning Domain-Independent String Transformation Weights.. - Tejada, Knoblock (2002) (7 citations) Self-citation (Tejada Knoblock Minton) (Correct)
....manual construction of domain specific string transformations or manual setting of general transformation parameter weights for recognizing format inconsistencies. This manual process can be time consuming and error prone. We have developed an object identification system called Active Atlas [18], which applies a set of domain independent string transformations to compare the objects shared attributes in order to identify matching objects. In this paper, we discuss extensions to the Active Atlas system, which allow it to learn to tailor the weights of a set of general transformations to ....
....and Phone attributes ( Les Celebrites ) This type of critical attribute information is captured in the form of object identification rules (mapping rules) which are then used to determine the mappings between the objects. We have developed an object identification system called Active Atlas [18], which applies a set of general string transformations in order to propose possible mappings between the objects and then employs an active learning technique that learns the necessary mapping rules to classify the mappings with high accuracy. Previously, Active Atlas, as many other object ....
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S. Tejada, C. A. Knoblock, and S. Minton. Learning object identification rules for information integration. Special Issue on Data Extraction, Cleaning, and Reconciliation, Information Systems Journal, 26(8), 2001.
A Comparison of String Metrics for Matching Names and Records - William Cohen Pradeep (4 citations) (Correct)
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Tejada, S.; Knoblock, C. A.; and Minton, S. 2001. Learning object identification rules for information integration. Information Systems 26(8):607--633.
Schema Matching using Duplicates - Alexander Bilke Technische (1 citation) (Correct)
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S. Tejada, C. A. Knoblock, and S. Minton. Learning object identification rules for information integration. Information Systems, 26(8):607--633, 2001.
Shared Lexicon for Distributed Annotations on the Web - Paolo Avesani Via (2005) (Correct)
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S. Tejada, C. A. Knoblock, and S. Minton. Learning Object Identification Rules for Information Integration. Information System, 26(8):607--633, 2001.
Shared Lexicon for Distributed Annotations on the Web - Avesani, Cova (2005) (Correct)
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S. Tejada, C. A. Knoblock, and S. Minton. Learning Object Identification Rules for Information Integration. Information System, 26(8):607--633, 2001.
The Object Identification Framework - Neiling, Jurk (2003) (1 citation) (Correct)
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S. Tejada, C. Knoblock, and S. Minton. Learning object identification rules for information integration. Information Systems, 26(8), 2001.
A Hierarchical Graphical Model for Record Linkage - Pradeep Ravikumar William (2004) (8 citations) (Correct)
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S. Tejada, C. A. Knoblock, and S. Minton. Learning object identification rules for information integration. Information Systems, 26(8):607--633, 2001.
An Ontology Approach to Data Integration - Buccella, Cechich, Brisaboa (Correct)
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Tejada, S., Knoblock, C.A., Minton, S. Learning object identification rules for Information Integration. Information Systems Vol. 26, N 8, pp. 607-633, 2001.
Record Linkage: A Machine Learning Approach, A.. - Elfeky, Verykios, .. (2003) (Correct)
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S. Tejada, C. Knoblock, and S. Minton. Learning Object Identification Rules for Information Integration. Information Systems Journal, 26(8), 2001.
Piazza: Mediation and Integration Infrastructure for.. - Ives, Halevy, Mork.. (2004) (1 citation) (Correct)
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S. Tejada, C. A. Knoblock, and S. Minton. Learning object identification rules for information integration. Information Systems Journal Special Issue on Data Extraction, Cleaning, and Reconciliation, December 2001.
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