Learning Multi-Relational Semantics Using Neural-Embedding Models
In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework. We investigate the different choices of relation operators based on linear and bilinear transformations, and also the effects of entity representations by incorporating unsupervised vectors pre-trained on extra textual resources. Our results show several interesting findings, enabling the design of a simple embedding model that achieves the new state-of-the-art performance on a popular knowledge base completion task evaluated on Freebase.
Code (0)
등록된 구현이 없습니다.
Tasks
Knowledge Base CompletionSimilar Papers 제목 키워드 기반
BERT Meets Relational DB: Contextual Representations of Relational Databases
In this paper, we address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables. Embeddings help to capture semantics encoded in the database and can be us…
Missing ValuesRepresentation LearningOn Embeddings in Relational Databases
We address the problem of learning a distributed representation of entities in a relational database using a low-dimensional embedding. Low-dimensional embeddings aim to encapsulate a concise vector representation for an…
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data
Recent work in learning vector-space embeddings for multi-relational data has focused on combining relational information derived from knowledge bases with distributional information derived from large text corpora. We p…
Entity EmbeddingsMultiplex Word Embeddings for Selectional Preference Acquisition
Conventional word embeddings represent words with fixed vectors, which are usually trained based on co-occurrence patterns among words. In doing so, however, the power of such representations is limited, where the same w…
Word EmbeddingsWord SimilarityRelational Argumentation Semantics
In this paper, we propose a fresh perspective on argumentation semantics, to view them as a relational database. It offers encapsulation of the underlying argumentation graph, and allows us to understand argumentation se…