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Erschienen in:
Structuring Linked Data Search Results Using Probabilistic Soft Logic Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Unsupervised Entity Resolution on Multi-type Graphs

verfasst von : Linhong Zhu, Majid Ghasemi-Gol, Pedro Szekely, Aram Galstyan, Craig A. Knoblock

Erschienen in: The Semantic Web – ISWC 2016

Verlag: Springer International Publishing

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Abstract

Entity resolution is the task of identifying all mentions that represent the same real-world entity within a knowledge base or across multiple knowledge bases. We address the problem of performing entity resolution on RDF graphs containing multiple types of nodes, using the links between instances of different types to improve the accuracy. For example, in a graph of products and manufacturers the goal is to resolve all the products and all the manufacturers. We formulate this problem as a multi-type graph summarization problem, which involves clustering the nodes in each type that refer to the same entity into one super node and creating weighted links among super nodes that summarize the inter-cluster links in the original graph. Experiments show that the proposed approach outperforms several state-of-the-art generic entity resolution approaches, especially in data sets with missing values and one-to-many, many-to-many relations.

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Vorheriges Kapitel Context-Free Path Queries on RDF Graphs
Fußnoten
1
Note that since the input graphs we focused are undirected, we save the half computation by assuming that types of vertices are ordered and restricting edges from a precedent type t to \(t'\).
 
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Metadaten
Titel
Unsupervised Entity Resolution on Multi-type Graphs
verfasst von
Linhong Zhu
Majid Ghasemi-Gol
Pedro Szekely
Aram Galstyan
Craig A. Knoblock
Copyright-Jahr
2016
Buch
The Semantic Web – ISWC 2016

Print ISBN: 978-3-319-46522-7

Electronic ISBN: 978-3-319-46523-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46523-4_39

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