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Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/www2012/paper/707> ?p ?o. }

• 707 creator maximilian-nickel.
• 707 creator volker-tresp.
• 707 type InProceedings.
• 707 label "Factorizing YAGO: Scalable Machine Learning for Linked Data".
• 707 sameAs 707.
• 707 abstract "Vast amounts of structured information have already been published in the Semantic Web's Linked Open Data (LOD) cloud and its size is still growing rapidly. Yet, access to this information via reasoning and querying is often 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 noise and data inconsistencies. 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 a sparse tensor factorization 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 its computation can be distributed across multiple nodes. We demonstrate that our approach is able to factorize the YAGO~2 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 a variety of relational learning tasks that are relevant to Linked Data, such as predicting the existence of unknown triples.".
• 707 hasAuthorList authorList.
• 707 isPartOf proceedings.
• 707 keyword "Large-Scale Machine Learning".
• 707 keyword "Linked Open Data".
• 707 keyword "Relational Learning".
• 707 keyword "Semantic Web".
• 707 keyword "Tensor Factorization".
• 707 title "Factorizing YAGO: Scalable Machine Learning for Linked Data".