Knowledge graph embeddingDocument similarityBERTBi-LSTM encoderRecently, similar entity searching over knowledge graph (KG) has gained much attentions by researchers. However, in rich-semantic KGs with multi-typed entities and relations, also known as heterogeneous information network, relevant entity ...
An evaluation of this approach uses a benchmark dataset of simulated videos for causal reasoning, CLEVRER-Humans, and compares the performance of multiple knowledge graph embedding algorithms. Two distinct dataset splitting approaches are used for evaluation: (1) random-based split, which is the ...
35model to generate BERT embeddings. Instead of providing a fixed embedding for each word, BERT produces contextualized word embeddings, so that the representation of each word is influenced by its surrounding words in a given sentence
1. GR proceeds by defining non-overlapping consecutive blocks, or sectors, along the similarity circle of the network embedding. Each sector contains r consecutive nodes, independently of whether these nodes are connected. Given the distribution of nodes across the similarity space, sectors could ...
To visualize the distribution of drug pairs’ feature vectors, we apply the t-distributed stochastic neighbor embedding (t-SNE) algorithm [63] to three distinct types of drug pair features. In the t-SNE plot, tight clustering of positive (or negative) set data indicates that the extracted ...
Integrating Entity Attributes for Error-Aware Knowledge Graph Embedding We leverage confidence scores to adaptively update the weighted aggregation in the multi-view graph learning framework and margin loss in KG embedding, such... Q Zhang,J Dong,Q Tan,... - 《IEEE Transactions on Knowledge & Da...
In this section, we formulate the problem of heterogeneous information network embedding and give the definitions and symbols that are used in this paper. [Weighted Sign Heterogeneous Information Network] A weighted sign heterogeneous information network is a directed graphG={V,E,W}with an object ...
line Alberi Morel, “Low-complexity single-image super-resolution based on nonnegative neighbor embedding,” in Proc. British Machine Vision Conf. BMVA Press, Surrey, Australia, 2012, pp. 135.1–135.10.. Google Scholar [54] R. Zeyde, M. Elad, and M. Protter, “On single image scale-...
S. et al. Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLoS Computational Biology 6, e1000748 (2010). 34. Klimm, F., Bassett, D. S., Carlson, J. M. & Mucha, P. J. Resolving structural variability in network models ...
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