Composing Relationships with Translations

García-Durán, Alberto; Bordes, Antoine; Usunier, Nicolas

doi:10.18653/v1/d15-1034

Cited by 78 publications

(45 citation statements)

References 4 publications

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“…In the last years, many embedding-based methods have been proposed to infer missing relations in knowledge bases based on function that computes a likelihood score based on the embeddings of entities and relation types. Due to its simplicity and good performance, there is a large body of work on translation-based scoring functions [5,14,11]. [15] propose an approach to large-scale sequential sales prediction that embeds items into a transition space where user embeddings are modeled as translation vectors operating on item sequences.…”

Section: Related Workmentioning

confidence: 99%

TransRev: Modeling Reviews as Translations from Users to Items

García-Durán

González²,

Oñoro-Rubio³

et al. 2020

Lecture Notes in Computer Science

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The text of a review expresses the sentiment a customer has towards a particular product. This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review. Furthermore, the products costumers have purchased in the past are indicative of the products they will purchase in the future. This is what recommender systems exploit by learning models from purchase information to predict the items a customer might be interested in. We propose TRANSREV, an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective. TRANSREV learns vector representations for users, items, and reviews. The embedding of a review is learned such that (a) it performs well as input feature of a regression model for sentiment prediction; and (b) it always translates the reviewer embedding to the embedding of the reviewed items. This allows TRAN-SREV to approximate a review embedding at test time as the difference of the embedding of each item and the user embedding. The approximated review embedding is then used with the regression model to predict the review score for each item. TRANSREV outperforms state of the art recommender systems on a large number of benchmark data sets. Moreover, it is able to retrieve, for each user and item, the review text from the training set whose embedding is most similar to the approximated review embedding.

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Section: Related Workmentioning

confidence: 99%

TransRev: Modeling Reviews as Translations from Users to Items

García-Durán

González²,

Oñoro-Rubio³

et al. 2020

Lecture Notes in Computer Science

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“…Following TransE, TransH (Wang et al, 2014), TransR (Lin et al, 2015b) and TransD (Ji et al, 2015) were proposed to extend TransE on modeling multimapping relations. PTransE (Lin et al, 2015a) and RTransE (García-Durán et al, 2015) extend TransE on modeling multi-step relation paths.…”

Section: Kg Embeddingmentioning

confidence: 99%

Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment

Shi¹

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP applications. Recently many embedding-based approaches were proposed for cross-lingual entity alignment. However, almost all of them are based on TransE or its variants, which have been demonstrated by many studies to be unsuitable for encoding multi-mapping relations such as 1-N, N-1 and N-N relations, thus these methods obtain low alignment precision. To solve this issue, we propose a new embedding-based framework. Through defining dot productbased functions over embeddings, our model can better capture the semantics of both 1-1 and multi-mapping relations. We calibrate embeddings of different KGs via a small set of pre-aligned seeds. We also propose a weighted negative sampling strategy to generate valuable negative samples during training and we regard prediction as a bidirectional problem in the end. Experimental results (especially with the metric Hits@1) on real-world multilingual datasets show that our approach significantly outperforms many other embedding-based approaches with state-of-the-art performance.

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“…It regards relation paths as translations between entities for representation learning and utilizes a pathconstraint resource allocation algorithm to evaluate the reliability of relation paths. RTransE (García-Durán et al, 2015) and TransE-COMP (Guu et al, 2015) take the sum of the vectors of all relations in a path as the representation for a relation path. For the Bilinear-COMP model (Guu et al, 2015), and the PRUNED-PATHS model (Toutanova et al, 2016), they represent each relation as a diagonal matrix, and evaluate the relation path by matrix multiplication.…”

Section: Incorporating Relation Pathsmentioning

confidence: 99%

“…Predicting Head Entities (Hits@10) Predicting Tail Entities (Hits@10) Relation Category 1-to-1 1-to-N N-to-1 N-to-N 1-to-1 1-to-N N-to-1 N- recorded for comparison. In the link prediction task, several competitive KG completion methods are utilized as baselines, including SE (Bordes et al, 2011), SME (Bordes et al, 2014), TransE (Bordes et al, 2013), TransH (Wang et al, 2014), TransR (Lin et al, 2015b), TranSparse (Ji et al, 2016), STransE (Nguyen et al, 2016, ITransF (Xie et al, 2017), HolE (Nickel et al, 2016), ComplEx (Trouillon et al, 2016), ANALOGY (Liu et al, 2017), ProjE (Shi and Weninger, 2017), RTransE (García-Durán et al, 2015), PTransE (Lin et al, 2015a), PaSKoGE (Jia et al, 2018), RPE (Lin et al, 2018) and RotatE (Sun et al, 2019). Among them, RTransE, PTransE, PaSKoGE and RPE exploit the information of paths between entity pairs.…”

Section: Tasksmentioning

confidence: 99%

Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

Zhu¹,

Liu²,

Wu³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Incompleteness is a common problem for existing knowledge graphs (KGs), and the completion of KG which aims to predict links between entities is challenging. Most existing KG completion methods only consider the direct relation between nodes and ignore the relation paths which contain useful information for link prediction. Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning. In addition, these path-based models always ignore nonlinear contributions of path features for link prediction. To solve these problems, we propose a novel KG completion method named OPTransE. Instead of embedding both entities of a relation into the same latent space as in previous methods, we project the head entity and the tail entity of each relation into different spaces to guarantee the order of relations in the path. Meanwhile, we adopt a pooling strategy to extract nonlinear and complex features of different paths to further improve the performance of link prediction. Experimental results on two benchmark datasets show that the proposed model OPTransE performs better than state-of-the-art methods.

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Composing Relationships with Translations

Cited by 78 publications

References 4 publications

TransRev: Modeling Reviews as Translations from Users to Items

TransRev: Modeling Reviews as Translations from Users to Items

Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment

Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

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