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Towards Ontologically Grounded and Language-Agnostic Knowledge Graphs

arXiv 2307.11206 ↗First posted 20 Jul 2023cs.AIcited by 0

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Abstract (arXiv)

Knowledge graphs (KGs) have become the standard technology for the representation of factual information in applications such as recommendation engines, search, and question-answering systems. However, the continual updating of KGs, as well as the integration of KGs from different domains and KGs in different languages, remains to be a major challenge. What we suggest here is that by a reification of abstract objects and by acknowledging the ontological distinction between concepts and types, we arrive at an ontologically grounded and language-agnostic representation that can alleviate the difficulties in KG integration.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 20 Jul 2023.

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References · 4

  1. 2021Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Conceptscited by 37
  2. 2021A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learningcited by 31
  3. 2021RAGA: Relation-aware Graph Attention Networks for Global Entity Alignmentcited by 29
  4. 2022Facing Changes: Continual Entity Alignment for Growing Knowledge Graphscited by 8

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