Resource-Bounded Information Gathering for Correlation Clustering

Kanani, Pallika; McCallum, Andrew

doi:10.1007/978-3-540-72927-3_46

Cited by 5 publications

(2 citation statements)

References 4 publications

(1 reference statement)

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“…The interdependency within the data set is often conveniently modeled using graphs, but it poses interesting questions about selection of instances to query and propagating uncertainty through the graph [4]. In [5], the test instances are not independent of each other, and the impact of acquisition in the context of graph partitioning is studied.…”

Section: Active Information Acquisitionmentioning

confidence: 99%

Resource-Bounded Information Extraction: Acquiring Missing Feature Values on Demand

Kanani

McCallum

2010

Advances in Knowledge Discovery and Data Mining

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Abstract. We present a general framework for the task of extracting specific information "on demand" from a large corpus such as the Web under resource-constraints. Given a database with missing or uncertain information, the proposed system automatically formulates queries, issues them to a search interface, selects a subset of the documents, extracts the required information from them, and fills the missing values in the original database. We also exploit inherent dependency within the data to obtain useful information with fewer computational resources. We build such a system in the citation database domain that extracts the missing publication years using limited resources from the Web. We discuss a probabilistic approach for this task and present first results. The main contribution of this paper is to propose a general, comprehensive architecture for designing a system adaptable to different domains.

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Section: Active Information Acquisitionmentioning

confidence: 99%

Resource-Bounded Information Extraction: Acquiring Missing Feature Values on Demand

Kanani

McCallum

2010

Advances in Knowledge Discovery and Data Mining

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“…However, in record matching problems, different instances often share common attribute values or have attribute values derived from some common resources, and such works do not consider this aspect. [16] solved a clustering problem expressed as a complete graph, where additional web resources can be acquired and added as additional vertices in the graph. However, they only considered one kind of additional resource (search engine results) and did not account for varying acquisition costs that differing resources may incur.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Cost-Sensitive Web Resource Acquisition for Record Matching

Tan

Kan

2010

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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Abstract-Web information is increasingly used as evidence in solving various problems, including record matching. However, acquiring web-based resources is slow and can incur other access costs. As such, solutions often acquire only a subset of the resources to achieve a balance between acquisition cost and benefit. Unfortunately, existing work has largely ignored the issue of which resources to acquire. They also fail to emphasize on the hierarchical nature of resource acquisitions, e.g., the search engine results for two queries must be obtained before their TF-IDF cosine similarity be computed. In this paper, we propose a framework for performing cost-sensitive acquisition of resources with hierarchical dependencies, and apply it to the web resource context. Our framework is versatile, and we show that a large variety of problems can be formulated using resource dependency graphs. We solve the resource acquisition problem by casting it as a combinatorial search problem. Finally, we demonstrate the effectiveness of our acquisition framework on record matching problems of different domains.

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