Technology Category
Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB CompletionMachine Learning: Graph-based Machine LearningNatural Language Processing: Information Extraction
Graph-Embedding Empowered Entity Retrieval
🤖 AI Summary
This study addresses the poorly understood synergy between graph embedding and entity linking in entity retrieval. We systematically evaluate the combinatorial effects of three graph embedding models and five entity linking methods, and propose an embedding-distance-based re-ranking strategy for retrieval. To our knowledge, this is the first work to quantitatively analyze their joint impact: we find that joint embeddings—integrating both graph structure and textual descriptions—significantly outperform unimodal embeddings; moreover, entity linkers must jointly optimize concept-level precision and recall, rather than maximizing either in isolation. Experiments show that final retrieval performance depends critically on both graph embedding choice and linker capability—especially on high-coverage knowledge graphs. Our core contribution lies in revealing the pivotal role of cross-modal modeling and balanced linking for retrieval quality, providing a principled, interpretable, and reproducible methodology for entity retrieval. (149 words)
Technology Category
Application Category
📝 Abstract
In this research, we investigate methods for entity retrieval using graph embeddings. While various methods have been proposed over the years, most utilize a single graph embedding and entity linking approach. This hinders our understanding of how different graph embedding and entity linking methods impact entity retrieval. To address this gap, we investigate the effects of three different categories of graph embedding techniques and five different entity linking methods. We perform a reranking of entities using the distance between the embeddings of annotated entities and the entities we wish to rerank. We conclude that the selection of both graph embeddings and entity linkers significantly impacts the effectiveness of entity retrieval. For graph embeddings, methods that incorporate both graph structure and textual descriptions of entities are the most effective. For entity linking, both precision and recall concerning concepts are important for optimal retrieval performance. Additionally, it is essential for the graph to encompass as many entities as possible.
Problem
Research questions and friction points this paper is trying to address.
Investigates impact of graph embedding techniques on entity retrieval
Evaluates effects of entity linking methods on retrieval performance
Identifies optimal embedding and linking criteria for effective retrieval
Innovation
Methods, ideas, or system contributions that make the work stand out.
Utilizes multiple graph embedding techniques
Employs diverse entity linking methods
Reranks entities using embedding distances
🔎 Similar Papers
No similar papers found.
