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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
ER Retrieval Pathway01:45

ER Retrieval Pathway

In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...

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Related Experiment Videos

JointRel: Joint semantic embedding with relational message passing for knowledge graph completion.

Yunong Zhang1, Jiashuang Huang1, Weiping Ding2

  • 1School of Artificial Intelligence and Computer Science, Nantong University, Nantong, 226019, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces JointRel, a novel method for knowledge graph completion (KGC) that balances entity and relation semantics. JointRel enhances KGC performance by explicitly learning both node and edge features for improved accuracy.

Keywords:
Entity embedding learningGraph augmentation networkKnowledge graph completionRelational embedding learningRelational message passing

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Data Science

Background:

  • Knowledge graph completion (KGC) traditionally focuses on entity semantics, often overlooking crucial edge features.
  • This imbalance limits the semantic expressiveness and practical applications of knowledge graphs.

Purpose of the Study:

  • To develop a method that explicitly learns both node and edge features for a more balanced semantic representation in KGC.
  • To improve the accuracy, stability, and robustness of knowledge graph completion.

Main Methods:

  • Proposed JointRel, a dual-channel graph augmentation network for joint semantic embedding with relational message passing.
  • Employed node-level and edge-level graph learning to capture neighbor information for entity embeddings.
  • Utilized graph attention and relational context to enhance edge feature representations.

Main Results:

  • JointRel demonstrated superiority on four KGC datasets, achieving significant Mean Reciprocal Rank (MRR) improvements over state-of-the-art methods.
  • Observed MRR gains of 4.2%, 0.2%, 3.4%, and 24.8% across different datasets.
  • The method provides a more balanced semantic framework for entities and relations.

Conclusions:

  • JointRel offers a robust framework for KGC, enhancing semantic representation for both entities and relations.
  • The improved stability and robustness benefit downstream applications such as question answering and recommendation systems.