Entity Embeddings
70 papers with code • 0 benchmarks • 2 datasets
Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.
Benchmarks
These leaderboards are used to track progress in Entity Embeddings
DAWT
Latest papers
An entity-guided text summarization framework with relational heterogeneous graph neural network
Firstly, entities are leveraged to construct a sentence-entity graph with weighted multi-type edges to model sentence relations, and a relational heterogeneous GNN for summarization is proposed to calculate node encodings.
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
zjukg/earl
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In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities.
Inductive Logical Query Answering in Knowledge Graphs
tgebhart/sheaf_kg_transind
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Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes.
Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering
jumxglhf/grape
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In light of this, we propose a novel knowledge Graph enhanced passage reader, namely Grape, to improve the reader performance for open-domain QA.
A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs
However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations.
Embedding-Based Entity Alignment Using Relation Structural Similarity
pengyanhui/RSimEA
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2020 IEEE International Conference on Knowledge Graph (ICKG) 2022
Then, it iteratively computes the structural similarity between the relations in different knowledge graphs according to the seed alignments and the alignments with high reliability generated during training, which makes the embeddings of relations with high similarity closer to each other.
Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment
qdin4048/CPL-OT
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The key idea is to iteratively pseudo-label alignment pairs empowered with conflict-aware optimal transport (OT) modeling to boost the precision of entity alignment.
Ered: Enhanced Text Representations with Entities and Descriptions
lshowway/ered
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On the one hand, it is implicit and only model weights are paid attention to, the pre-trained entity embeddings are ignored.
StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph
Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood.
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities
To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate.
