Abstract:
This position paper introduces a new way for large language models (LLMs) to interact with Knowledge Graphs (KGs), especially for information extraction. Most relation extraction approaches focus on finding the corresponding textual spans for subject, relation, and object in a text but ignore the actual state of the KG and the schema defined therein. In this work, the function-calling possibility of LLMs is explored to search for already defined entities in the KG and add information in the form of triples to the KG. A huge improvement over previous techniques is that it can also deal with various large ontologies / KGs and that the added information fits to a provided KG on a schema and instance level. Further validation checks can be added to increase the quality of the extracted statements. A small playground is provided to analyze the effectiveness of the approach.