SLINT: A Schema-Independent Linked Data Interlinking System

SLINT: A Schema-Independent Linked Data Interlinking System
SLINT: A Schema-Independent Linked Data Interlinking System
Bibliographical Metadata
Subject:	Link Discovery
Keywords:	linked data, schema-independent, blocking, interlinking
Year:	2012
Authors:	Khai Nguyen, Ryutaro Ichise, Bac Le
Venue	OM
Content Metadata
Problem:	Link Discovery
Approach:	Weighted co-occurrence and adaptive filtering in blocking and instance matching
Implementation:	SLINT
Evaluation:	Accuracy Evaluation

Abstract

Linked data interlinking is the discovery of all instances that represent the same real-world object and locate in different data sources. Since different data publishers frequently use different schemas for storing resources, we aim at developing a schema-independent interlinking system. Our system automatically selects important predicates and useful predicate alignments, which are used as the key for blocking and instance matching. The key distinction of our system is the use of weighted co-occurrence and adaptive filtering in blocking and instance matching. Experimental results show that the system highly improves the precision and recall over some recent ones. The performance of the system and the efficiency of main steps are also discussed.

Conclusion

In this paper, we present SLINT, an efficient schema-independent linked data interlinking system. We select important predicates by predicate’s coverage and discriminability. The predicate alignments are constructed and filtered for obtaining key alignments.We implement an adaptive filtering technique to produce candidates and identities. Compare with the most recent systems, SLINT highly outperforms the precision and recall in interlinking. The performance of SLINT is also very high when it takes around 1 minute to detect more than 13,000 identity pairs.

Future work

Although SLINT has good result on tested datasets, it is not sufficient to evaluate the scalability of our system, which we consider as the current limiting point because of the used of weighted co-occurrence matrix. We will investigate about a solution for this issue in our next work. Besides, we also interested in automatic configuration for every threshold used in SLINT and improving SLINT into a novel cross-domain interlinking system.

Approach

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Implementations

Download-page: http://ri-www.nii.ac.jp/SLINT/index.html

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Evaluation

Experiment Setup: 2.66Ghz quad-core CPU and 4GB of memory

Evaluation Method : Compare the system with AgreementMaker, SERIMI, and Zhishi.Links

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Dimensions: Accuracy

Benchmark used: LinkedMDB, GeoNames

Results: SLINT system totally outperforms the others on both precision and recall. AgreementMaker has a competitive precision with SLINT on dataset D3 but this system is much lower in recall. Zhishi.Links results on dataset D3 are very high, but the F1 score of SLINT is still 0.05 higher in overall.