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Found 9 papers, 3 papers with code
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion
In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that co-occur in sentences in the corpus (i. e. with mentions entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i. e. without mention entity pairs).
Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion
In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that co-occur in sentences in the corpus (i. e. with mention entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i. e. without mention entity pairs).
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities.
RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities.
RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding
Existing methods for learning KGEs can be seen as a two-stage process where (a) entities and relations in the knowledge graph are represented using some linear algebraic structures (embeddings), and (b) a scoring function is defined that evaluates the strength of a relation that holds between two entities using the corresponding relation and entity embeddings.
Learning Relation Representations from Word Representations
We model relation representation as a supervised learning problem and learn parametrised operators that map pre-trained word embeddings to relation representations.
Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection
We show that, if the word embed- dings are standardised and uncorrelated, such an operator will be independent of bilinear terms, and can be simplified to a linear form, where PairDiff is a special case.
Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection
We show that, if the word embeddings are standardised and uncorrelated, such an operator will be independent of bilinear terms, and can be simplified to a linear form, where \PairDiff is a special case.
Compositional Approaches for Representing Relations Between Words: A Comparative Study
In contrast, a compositional approach for representing relations between words overcomes these issues by using the attributes of each individual word to indirectly compose a representation for the common relations that hold between the two words.
