TripleRank: Ranking Semantic Web Data by Tensor Decomposition

By Thomas Franz, Antje Schultz, Sergej Sizov, Steffen Staab. 2009

228 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

A semantic matching energy function for learning with multi-relational data (2013, background): “A relation is denoted by a triplet ( lhs , rel , rhs ), where lhs is the left entity, rhs the right one and rel the type of relation between them.”
A Review of Relational Machine Learning for Knowledge Graphs (2015, methodology): “[77] factorized adjacency tensors using the CP tensor decomposition to analyze the link structure of web pages and semantic web data, respectively.”
Knowledge vault: a web-scale approach to probabilistic knowledge fusion (2014, methodology): “If we ignore the sigmoid transform (needed to produce binary responses), this is equivalent to the PARAFAC method of tensor decomposition [14, 5, 11].”
Typed Tensor Decomposition of Knowledge Bases for Relation Extraction (2014, background): “For instance, Speer et al. (2008) aimed to create vectors of latent components for representing concepts in a common sense knowledge base us-ing SVD. Franz et al. (2009) proposed TripleRank to model the subject - predicate - object RDF triples in a tensor, and then applied the CP decomposition to…”
Unsupervised generation of data mining features from linked open data (2012, background): “The implementation and evaluation of more sophisticated entity recognition algorithms is an important item for future work, but beyond the scope of this paper.”
SSE: Semantically Smooth Embedding for Knowledge Graphs (2017, background): “This section reviews two lines of embedding strategies: 1) strategies based on latent distance (LD) and 2) strategies based on tensor factorization.”
Linked open data to support content-based recommender systems (2012, background): “Moreover, in [9] the authors consider only the objects of the RDF triples, while we look also at subject of the statements to compute similarities.”
Knowledge Graph Embedding: A Survey of Approaches and Applications (2017, methodology): “have also been applied on KGs to model multi-relational data [83], [84], [85].”
Knowledge Graph Completion via Complex Tensor Factorization (2017, methodology): “al., 2016). With the rst embedding models, asymmetry of relations was quickly seen as a problem and asymmetric extensions of tensors were studied, mostly by either considering independent embeddings (Franz et al., 2009) or considering relations as matrices instead of vectors in the RESCAL model (Nic”
A Three-Way Model for Collective Learning on Multi-Relational Data (2011, methodology): “(Franz et al., 2009) use CP to rank relational data that is given in form of RDF triples.”
CORE: Context-Aware Open Relation Extraction with Factorization Machines (2015, methodology): “Examples of such tensor factorization models are TripleRank (Franz et al., 2009), RESCAL (Nickel et al.”
Factorizing YAGO: scalable machine learning for linked data (2012, methodology): “Probably most similar to our approach is TripleRank [12], which applies the CP [8] tensor decomposition to RDF graphs for faceted browsing.”
Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train (2016, background): “Employing tensors and their decompositions to process data has become increasingly popular since [2]–[4].”