Embedding Knowledge Graph in Function Spaces
We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques. Initially employing polynomial functions to compute embeddings, we progress to more intricate representations using neural networks with varying layer complexities. We argue that employing functions for embedding computation enhances expressiveness and allows for more degrees of freedom, enabling operations such as composition, derivatives and primitive of entities representation. Additionally, we meticulously outline the step-by-step construction of our approach and provide code for reproducibility, thereby facilitating further exploration and application in the field.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph EmbeddingKnowledge Graph EmbeddingSimilar Papers 제목 키워드 기반
On The Expressive Power of Knowledge Graph Embedding Methods
Knowledge Graph Embedding (KGE) is a popular approach, which aims to represent entities and relations of a knowledge graph in latent spaces. Their representations are known as embeddings. To measure the plausibility of t…
Graph EmbeddingKnowledge Graph EmbeddingComposing Knowledge Graph Embeddings via Word Embeddings
Learning knowledge graph embedding from an existing knowledge graph is very important to knowledge graph completion. For a fact $(h,r,t)$ with the head entity $h$ having a relation $r$ with the tail entity $t$, the curre…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+2Improving Graph Embeddings in Machine Learning Using Knowledge Completion with Validation in a Case Study on COVID-19 Spread
The rise of graph-structured data has driven major advances in Graph Machine Learning (GML), where graph embeddings (GEs) map features from Knowledge Graphs (KGs) into vector spaces, enabling tasks like node classificati…
Node ClassificationKnowledge GraphsLink PredictionIs Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods
Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum. In order to make use of multiple KG em…
Entity DisambiguationKnowledge Graph CompletionKnowledge GraphsQuestion Answering+1Analogical Inference Enhanced Knowledge Graph Embedding
Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink Prediction