Structured query construction via knowledge graph embedding
Sign Up to like & getrecommendations! Published in 2019 at "Knowledge and Information Systems"
DOI: 10.1007/s10115-019-01401-x
Abstract: In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to… read more here.
Keywords: graph embedding; knowledge; query construction; knowledge graph ... See more keywords
Relation correlations-aware graph convolutional network with text-enhanced for knowledge graph embedding
Sign Up to like & getrecommendations! Published in 2024 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-024-02179-3
Abstract: Long-tail distribution is a difficult challenge for knowledge graph embedding. We expect to solve the problem by complementing the information through the neighbor aggregation mechanism of GCN. However, the GCN method and its derivations are… read more here.
Keywords: relation; usepackage; knowledge graph; graph ... See more keywords
A graph embedding based model for fine-grained POI recommendation
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.01.118
Abstract: Abstract Point-of-interest (POI) recommendation is an important technique widely used in self-driving services. While POI recommendation aims to recommend unvisited POIs to self-driving users, users always expect their intended items can be suggested together with… read more here.
Keywords: recommendation; graph embedding; poi recommendation; fine grained ... See more keywords
DensE: An enhanced non-commutative representation for knowledge graph embedding with adaptive semantic hierarchy
Sign Up to like & getrecommendations! Published in 2022 at "Neurocomputing"
DOI: 10.1016/j.neucom.2021.12.079
Abstract: Capturing the composition patterns of relations is a vital task in knowledge graph completion. It also serves as a fundamental step towards multi-hop reasoning over learned knowledge. Previously, rotation-based translational methods, e.g., RotatE, have been… read more here.
Keywords: knowledge; non commutative; graph embedding; knowledge graph ... See more keywords
Relationship Prediction in a Knowledge Graph Embedding Model of the Illicit Antiquities Trade
Sign Up to like & getrecommendations! Published in 2023 at "Advances in Archaeological Practice"
DOI: 10.1017/aap.2023.1
Abstract: ABSTRACT The transnational networks of the illicit and illegal antiquities trade are hard to perceive. We suggest representing the trade as a knowledge graph with multiple kinds of relationships that can be transformed by a… read more here.
Keywords: knowledge graph; knowledge; graph; model ... See more keywords
Advancing edge-based clustering and graph embedding for biological network analysis: a case study in RASopathies
Sign Up to like & getrecommendations! Published in 2025 at "Briefings in Bioinformatics"
DOI: 10.1093/bib/bbaf320
Abstract: Abstract Understanding and predicting biological processes from protein–protein interaction (PPI) networks requires accurate and efficient representations of their structure. However, many existing methods fail to capture the complex, overlapping modular structure of biological systems. To… read more here.
Keywords: edge based; advancing edge; based clustering; clustering graph ... See more keywords
Learning graph representations of biochemical networks and its application to enzymatic link prediction
Sign Up to like & getrecommendations! Published in 2021 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btaa881
Abstract: Abstract Motivation The complete characterization of enzymatic activities between molecules remains incomplete, hindering biological engineering and limiting biological discovery. We develop in this work a technique, enzymatic link prediction (ELP), for predicting the likelihood of… read more here.
Keywords: graph embedding; enzymatic link; elp; link prediction ... See more keywords
Heterogeneous graph embedding model for predicting interactions between TF and target gene.
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac148
Abstract: MOTIVATION Identifying the target genes of transcription factors (TFs) is of great significance for biomedical researches. However, using biological experiments to identify TF-target gene interactions is still time consuming, expensive and limited to small scale.… read more here.
Keywords: heterogeneous graph; target gene; model; graph embedding ... See more keywords
Multi-Manifold Locality Graph Embedding Based on the Maximum Margin Criterion (MLGE/MMC) for Face Recognition
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Access"
DOI: 10.1109/access.2017.2706525
Abstract: Solving problems with small sample sizes during training for feature extraction and the dimensionality reduction method will not produce high face recognition accuracy using the locality graph embedding (LGE) algorithm. Thus, we introduced a new… read more here.
Keywords: maximum margin; graph embedding; locality graph; margin criterion ... See more keywords
GTrans: Generic Knowledge Graph Embedding via Multi-State Entities and Dynamic Relation Spaces
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2797876
Abstract: Knowledge graph embedding aims to construct a low-dimensional and continuous space, which is able to describe the semantics of high-dimensional and sparse knowledge graphs. Among existing solutions, translation models have drawn much attention lately, which… read more here.
Keywords: translation models; graph embedding; knowledge; knowledge graph ... See more keywords
A Knowledge Graph Embedding Framework With Triple Semantics
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2022.3227714
Abstract: The knowledge graph embedding model aims to use low-dimensional real-valued vectors to represent the entities and relations in the triples, where operations such as link prediction and triple classification can be performed based on these… read more here.
Keywords: semantics; knowledge graph; framework; embedding triples ... See more keywords