Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings (2009.07659v2)

Published 16 Sep 2020 in cs.AI and cs.CL

Abstract: Knowledge graph embedding approaches represent nodes and edges of graphs as mathematical vectors. Current approaches focus on embedding complete knowledge graphs, i.e. all nodes and edges. This leads to very high computational requirements on large graphs such as DBpedia or Wikidata. However, for most downstream application scenarios, only a small subset of concepts is of actual interest. In this paper, we present RDF2Vec Light, a lightweight embedding approach based on RDF2Vec which generates vectors for only a subset of entities. To that end, RDF2Vec Light only traverses and processes a subgraph of the knowledge graph. Our method allows the application of embeddings of very large knowledge graphs in scenarios where such embeddings were not possible before due to a significantly lower runtime and significantly reduced hardware requirements.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Jan Portisch (15 papers)
  2. Michael Hladik (5 papers)
  3. Heiko Paulheim (65 papers)
Citations (26)

Summary

We haven't generated a summary for this paper yet.