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SiGMa - Simple Greedy Matching: a tool for aligning large knowledge-bases
Participant : Simon Lacoste-Julien [correspondant] .
SiGMa - Simple Greedy Matching: a tool for aligning large knowledge-bases
Version 1. Webpage: http://mlg.eng.cam.ac.uk/slacoste/sigma/ .
The tool SiGMa (Simple Greedy Matching) is a knowledge base alignment tool implemented in Python. It takes as input two knowledge bases, each represented as a list of triples of (entity, relationship, entity), in addition to a partial alignment between the relationships from one knowledge base to the other, and gives as output an ordered list of proposed entity matches between the two knowledge base (where the order corresponds heuristically to a notion of certainty about these matches). The matching decisions are made in a greedy fashion, combining information about the relationship graph as well as a pairwise similarity scores defined between the entities. The code handles various sources of information to be used for this score, such as a similarity defined on strings, dates, and other entity properties – and gives a few options to the user.
We also provide two large-scale knowledge base alignment benchmark datasets with tens of thousands of ground truth pairs: YAGO aligned to IMDb as well as Freebase aligned to IMDb.
Participants outside of Sierra: Konstantina Palla, Alex Davies, Zoubin Ghahramani (Machine Learning Group, Department of Engineering, University of Cambridge); Gjergji Kasneci, Thore Graepel (Microsoft Research Cambridge)