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
Latest papers
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
This generalized gradient flow enables TFP to harness the multi-view structural information of KGs.
MMEAD: MS MARCO Entity Annotations and Disambiguations
MMEAD, or MS MARCO Entity Annotations and Disambiguations, is a resource for entity links for the MS MARCO datasets.
DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph
In this work, we present a web application named DBLPLink, which performs entity linking over the DBLP scholarly knowledge graph.
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
Category information plays a crucial role in enhancing the quality and personalization of recommender systems.
Context-Aware Composition of Agent Policies by Markov Decision Process Entity Embeddings and Agent Ensembles
Since the environments can be stochastic and complex in terms of the number of states and feasible actions, activities are usually modelled in a simplified way by Markov decision processes so that, e. g., agents with reinforcement learning are able to learn policies, that help to capture the context and act accordingly to optimally perform activities.
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments.
BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs
We train models using a biomedical KG containing approximately 2 million triples, and evaluate the performance of the resulting entity embeddings on the tasks of link prediction, and drug-protein interaction prediction, comparing against methods that do not take attribute data into account.
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
In this paper, we propose an INductive knowledge GRAph eMbedding method, InGram, that can generate embeddings of new relations as well as new entities at inference time.
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains
In this paper, we propose to improve on this process by pre-training an entity encoder such that embeddings of coreferring entities are more similar to each other than to the embeddings of other entities.
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.