Graph Embeddings
An empirical comparison of knowledge graph embeddings for item recommendation
... knowledge graph, the item recommendation problem can be seen as a specific case of knowledge graph completion problem, where the “feedback” property has to be ...knowledge graph completion algorithms ...
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Improving Neural Entity Disambiguation with Graph Embeddings
... For training, we generate negative samples by filtering this candidate list and limited the num- ber of candidates per positive sample. We em- ploy two techniques to filter the candidate list. First, we shuffle the ...
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Exploring the semantic content of unsupervised graph embeddings : an empirical study.
... why graph embedding approaches have been so success- ...unsupervised graph embedding techniques as we want to explore what features the techniques learn from the topol- ogy alone, without the requirement ...
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Cross Lingual Word Representations via Spectral Graph Embeddings
... In this paper, instead of the skip-gram model, we extend Eigenwords (Dhillon et al., 2015) to cross-lingual settings with sentence-alignment. Our main idea is to replace CCA, which is applica- ble to only two different ...
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KBGAN: Adversarial Learning for Knowledge Graph Embeddings
... ples to train knowledge graph embedding mod- els. More specifically, we consider probability- based, log-loss embedding models as the gener- ator to supply better quality negative examples, and use distance-based, ...
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Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
... We study a symmetric collaborative dia- logue setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this set- ting poses new ...
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Joint Semantic and Distributional Word Representations with Multi Graph Embeddings
... This dual representation becomes even more im- portant when considering graph embeddings. To find a self-supervised optimization function that induces a representation of nodes, two different goals are ...
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Graph embeddings for low-stretch greedy routing
... network graph embeddings in hyperbolic space for purposes other than ob- taining greedy graph embeddings have also been considered in the recent ...Internet graph for distance ...
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Knowledge Graph Embeddings with node2vec for Item Recommendation
... sparsity. Graph embeddings algorithms have shown to be able to automatically learn high quality feature vectors from graph structures, enabling vector-based measures of node related- ...knowledge ...
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Entity search: How to build virtual documents leveraging on graph embeddings
... that graph embeddings aim to represent the topology and structure of a graph through vectors or a set of ...previous graph models based on adjacency matrix which have dimension | V | × | V | , ...
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You CAN teach an old dog new tricks! On training knowledge graph embeddings
... The literature on KGE models is expanding rapidly. We review selected architectures, training methods, and evaluation protocols; see Table 1. The table examplarily indicates that new model ar- chitectures are sometimes ...
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Knowledge Graphs and Knowledge Graph Embeddings
... knowledge graph [6, ...of embeddings are designed to better model some facet of the knowledge ...complicated embeddings are more computationally expensive to use as well as ...
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Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction
... Reading comprehension QA and language mod- elling. Two recently published reading compre- hension question answering datasets—QAngaroo (Welbl et al., 2018) and HotPotQA (Yang et al., 2018)—evaluate multi-hop reasoning ...
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Long tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
... We propose a distance supervised relation ex- traction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate ”few- shot” models for classes existing ...
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Knowledge graph embeddings
... In addition to the information described above, further types of in- formation that can be added to the embedding models include entity attributes, temporal information and graph structures. Nickel et al (2012) ...
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How Much Topological Structure Is Preserved by Graph Embeddings?
... Abstract. Graph embedding aims at learning representations of nodes in a low di- mensional vector ...Good embeddings should preserve the graph topological ...the graph can be reconstructed ...
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RDF2Vec: RDF Graph Embeddings and Their Applications | www.semantic-web-journal.net
... The results for the task of classification and re- gression on the five datasets using the DBpedia and Wikidata entities’ vectors are shown in Tables 3 and 4. We can observe that the latent vectors extracted from DBpedia ...
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Question Answering with Sub Graph Embeddings Analytics and Future Discussionss
... Given a complete inquiry, our framework parses it to its First Order Logic (FOL) representation utilizing a language structure got from interlinked datasets; distinctive interpreters a[r] ...
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Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short
... noise embeddings demonstrate a small net improvement, suggesting that for embedding methods a large, unreliable corpus may be bet- ter than an extremely sparse, high-quality ...
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Towards Understanding the Geometry of Knowledge Graph Embeddings
... Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding meth- ...KG embeddings and correlate it with task ...
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