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.

Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding

wyy-code/TFP 23 Jan 2024

This generalized gradient flow enables TFP to harness the multi-view structural information of KGs.

1
23 Jan 2024

MMEAD: MS MARCO Entity Annotations and Disambiguations

informagi/mmead 14 Sep 2023

MMEAD, or MS MARCO Entity Annotations and Disambiguations, is a resource for entity links for the MS MARCO datasets.

8
14 Sep 2023

DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph

uhh-lt/dblplink 14 Sep 2023

In this work, we present a web application named DBLPLink, which performs entity linking over the DBLP scholarly knowledge graph.

0
14 Sep 2023

Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization

jyonn/cove 31 Aug 2023

Category information plays a crucial role in enhancing the quality and personalization of recommender systems.

3
31 Aug 2023

Context-Aware Composition of Agent Policies by Markov Decision Process Entity Embeddings and Agent Ensembles

nmerkle/sw_journal 28 Aug 2023

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.

2
28 Aug 2023

AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models

ruizhang-ai/autoalign 18 Jul 2023

In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments.

1
18 Jul 2023

BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs

elsevier-ai-lab/bioblp 6 Jun 2023

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.

9
06 Jun 2023

InGram: Inductive Knowledge Graph Embedding via Relation Graphs

bdi-lab/ingram 31 May 2023

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.

39
31 May 2023

EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains

fmtumbuka/eacl_encore 22 May 2023

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.

0
22 May 2023

An entity-guided text summarization framework with relational heterogeneous graph neural network

jingqiangchen/kbsumm 7 Feb 2023

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.

1
07 Feb 2023