Results 1 - 10 of 159

M.J.: InstanceBased Matching of Large Ontologies Using Locality-Sensitive Hashing

by Songyun Duan, Achille Fokoue, Oktie Hassanzadeh, Anastasios Kementsietsidis, Kavitha Srinivas, Michael J. Ward - In: Proceedings of the 11th International Semantic Web Conference, ISWC , 2012
"... Abstract. In this paper, we describe a mechanism for ontology align-ment using instance based matching of types (or classes). Instance-based matching is known to be a useful technique for matching ontologies that have different names and different structures. A key problem in instance matching of ty ..."
Abstract - Cited by 6 (2 self) - Add to MetaCart
of types, however, is scaling the matching algorithm to (a) handle types with a large number of instances, and (b) efficiently match a large number of type pairs. We propose the use of state-of-the art locality-sensitive hashing (LSH) techniques to vastly improve the scala-bility of instance matching

Kernelized locality-sensitive hashing for scalable image search

by Brian Kulis, Kristen Grauman - IEEE International Conference on Computer Vision (ICCV , 2009
"... Fast retrieval methods are critical for large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched. However, existing methods do not apply ..."
Abstract - Cited by 163 (5 self) - Add to MetaCart
not apply for high-dimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm’s sub-linear time similarity search guarantees for a

Instance-based matching of large life science ontologies

by Toralf Kirsten, Andreas Thor, Erhard Rahm
"... Abstract. Ontologies are heavily used in life sciences so that there is increasing value to match different ontologies in order to determine related conceptual categories. We propose a simple yet powerful methodology for instance-based ontology matching which utilizes the associations between molecu ..."
Abstract - Cited by 20 (6 self) - Add to MetaCart
Abstract. Ontologies are heavily used in life sciences so that there is increasing value to match different ontologies in order to determine related conceptual categories. We propose a simple yet powerful methodology for instance-based ontology matching which utilizes the associations between

Efficient Large-Scale Sequence Comparison by Locality-Sensitive Hashing

by Jeremy Buhler - Bioinformatics , 2001
"... Motivation: Comparison of multimegabase genomic DNA sequences is a popular technique for finding and annotating conserved genome features. Performing such comparisons entails finding many short local alignments between sequences up to tens of megabases in length. To process such long sequences e#cie ..."
Abstract - Cited by 91 (6 self) - Add to MetaCart
be chosen so that they appear frequently in significant similarities yet still remain rare in the background sequence. The algorithm finds ungapped alignments e#ciently using a randomized search technique, locality-sensitive hashing. We have found lsh-all-pairs to be both e#cient and sensitive for finding

Locality-Sensitive Hashing for Massive String-Based Ontology Matching

by Michael Cochez
"... Abstract-This paper reports initial research results related to the use of locality-sensitive hashing (LSH) for string-based matching of big ontologies. Two ways of transforming the matching problem into a LSH problem are proposed and experimental results are reported. The performed experiments sho ..."
Abstract - Add to MetaCart
Abstract-This paper reports initial research results related to the use of locality-sensitive hashing (LSH) for string-based matching of big ontologies. Two ways of transforming the matching problem into a LSH problem are proposed and experimental results are reported. The performed experiments

An Instance-based Learning Approach for Ontology Matching

by Pedro H R De Assis , Alberto H F Laender
"... Abstract. This paper proposes an instance-based learning approach for the ontology matching problem. This approach is applicable to scenarios where instances of the ontologies to be matched are exchanged between sources. An initial population of instances is used as a training set of a non-supervis ..."
Abstract - Add to MetaCart
Abstract. This paper proposes an instance-based learning approach for the ontology matching problem. This approach is applicable to scenarios where instances of the ontologies to be matched are exchanged between sources. An initial population of instances is used as a training set of a non

G.: Constructing Virtual Documents for Ontology Matching

by Hang Zhang, Wei Hu, Yuzhong Qu - In: 15th International World Wide Web Conference , 2006
"... Abstract. Ontology matching is a crucial task for data integration and management on the Semantic Web. The ontology matching techniques today can solve many problems from heterogeneity of ontologies to some extent. However, for matching large ontologies, most ontology match-ers take too long run tim ..."
Abstract - Cited by 79 (9 self) - Add to MetaCart
time and have strong requirements on running environment. Based on the MapReduce framework and the virtual doc-ument technique, in this paper, we propose a 3-stage MapReduce-based approach called V-Doc+ for matching large ontologies, which signifi-cantly reduces the run time while keeping good

Pyramid match hashing: Sub-linear time indexing over partial correspondences

by Kristen Grauman - In CVPR , 2007
"... Matching local features across images is often useful when comparing or recognizing objects or scenes, and efficient techniques for obtaining image-to-image correspondences have been developed [6, 4, 11]. However, given a query image, searching a very large image database with such measures remains ..."
Abstract - Cited by 38 (6 self) - Add to MetaCart
Matching local features across images is often useful when comparing or recognizing objects or scenes, and efficient techniques for obtaining image-to-image correspondences have been developed [6, 4, 11]. However, given a query image, searching a very large image database with such measures remains

HARRA: Fast Iterative Hashed Record Linkage for Large-Scale Data Collections

by Hung-sik Kim, Dongwon Lee
"... We study the performance issue of the “iterative ” record linkage (RL) problem, where match and merge operations may occur together in iterations until convergence emerges. We first propose the Iterative Locality-Sensitive Hashing (I-LSH) that dynamically merges LSH-based hash tables for quick and a ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
We study the performance issue of the “iterative ” record linkage (RL) problem, where match and merge operations may occur together in iterations until convergence emerges. We first propose the Iterative Locality-Sensitive Hashing (I-LSH) that dynamically merges LSH-based hash tables for quick

Fast contour matching using approximate earth mover’s distance

by Kristen Grauman, Trevor Darrell , 2004
"... Weighted graph matching is a good way to align a pair of shapes represented by a set of descriptive local features; the set of correspondences produced by the minimum cost matching between two shapes ’ features often reveals how similar the shapes are. However, due to the complexity of computing the ..."
Abstract - Cited by 90 (9 self) - Add to MetaCart
with Locality-Sensitive Hashing (LSH). We demonstrate our shape matching method on a database of 136,500 images of human figures. Our method achieves a speedup of four orders of magnitude over the exact method, at the cost of only a 4 % reduction in accuracy. 1.
Results 1 - 10 of 159