NDLI: Factorizing YAGO: scalable machine learning for linked data

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Factorizing YAGO: scalable machine learning for linked data

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Factorizing YAGO: scalable machine learning for linked data

Content Provider	ACM Digital Library
Author	Nickel, Maximilian Tresp, Volker Kriegel, Hans-Peter
Abstract	Vast amounts of structured information have been published in the Semantic Web's Linked Open Data (LOD) cloud and their size is still growing rapidly. Yet, access to this information via reasoning and querying is sometimes difficult, due to LOD's size, partial data inconsistencies and inherent noisiness. Machine Learning offers an alternative approach to exploiting LOD's data with the advantages that Machine Learning algorithms are typically robust to both noise and data inconsistencies and are able to efficiently utilize non-deterministic dependencies in the data. From a Machine Learning point of view, LOD is challenging due to its relational nature and its scale. Here, we present an efficient approach to relational learning on LOD data, based on the factorization of a sparse tensor that scales to data consisting of millions of entities, hundreds of relations and billions of known facts. Furthermore, we show how ontological knowledge can be incorporated in the factorization to improve learning results and how computation can be distributed across multiple nodes. We demonstrate that our approach is able to factorize the YAGO~2 core ontology and globally predict statements for this large knowledge base using a single dual-core desktop computer. Furthermore, we show experimentally that our approach achieves good results in several relational learning tasks that are relevant to Linked Data. Once a factorization has been computed, our model is able to predict efficiently, and without any additional training, the likelihood of any of the 4.3 ⋅ $10^{14}$ possible triples in the YAGO~2 core ontology.
Starting Page	271
Ending Page	280
Page Count	10
File Format	PDF
ISBN	9781450312295
DOI	10.1145/2187836.2187874
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semantic web Tensor factorization Relational learning Linked open data Large-scale machine learning
Content Type	Text
Resource Type	Article

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