A Survey of Current Link Discovery Frameworks

Tracking #: 1117-2329

Authors: 
Markus Nentwig
Michael Hartung
Axel-Cyrille Ngonga Ngomo
Erhard Rahm

Responsible editor: 
Natasha Noy

Submission type: 
Survey Article
Abstract: 
Links build the backbone of the Linked Data Cloud. With the steady growth in size of datasets comes an increased need for end users to know which frameworks to use for deriving links between datasets. In this survey, we comparatively evaluate current Link Discovery tools and frameworks. For this purpose, we outline general requirements and derive a generic architecture of Link Discovery frameworks. Based on this generic architecture, we study and compare the features of state-of-the-art linking frameworks. We also analyze reported performance evaluations for the different frameworks. Finally, we derive insights pertaining to possible future developments in the domain of Link Discovery.
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Reviewed

Decision/Status: 
Accept

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Review #1
By Arnab Dutta submitted on 03/Oct/2015
Suggestion:
Accept
Review Comment:

The area of LD is challenging enough and this was a survey paper on the different approaches undertaken. The authors compare and contrast different state of the art LD frameworks. However, it is absolutely correct that users requirement from a LD framework may vary, easy UI over scalability for instance. But, the authors refrain from re-sketching the common algorithm for any LD framework. This would have improved the contents of the paper beyond just benchmark results but enhanced with a recipe for a "good" LD framework.

Review #2
By Miriam Fernandez submitted on 22/Oct/2015
Suggestion:
Accept
Review Comment:

This manuscript was submitted as 'Survey Article' and should be reviewed along the following dimensions: (1) Suitability as introductory text, targeted at researchers, PhD students, or practitioners, to get started on the covered topic. (2) How comprehensive and how balanced is the presentation and coverage. (3) Readability and clarity of the presentation. (4) Importance of the covered material to the broader Semantic Web community.

Review #3
By Kavitha Srinivas submitted on 29/Oct/2015
Suggestion:
Accept
Review Comment:

This revision is good. It serves as a very good introduction to the space. Its quite comprehensive now, and makes it clear why it does not consider the database literature. Its very clear and well written. The space is clearly important to the community. A minor nit:
This workflow is a gen- eralization of the architecture given by analysing the later on compared LD frameworks (starting in section 4).
The word 'later' should be latter.


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