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While the potential of Open Information Extraction (Open IE) for KnowledgeGraph Construction (KGC) may seem promising, we find that the alignment of OpenIE extraction results with existing knowledge graphs to be inadequate. Theadvent of Large Language Models (LLMs), especially the commercially availableOpenAI models, have reset expectations for what is possible with deep learningmodels and have created a new field called prompt engineering. We investigatethe use of GPT models and prompt engineering for knowledge graph constructionwith the Wikidata knowledge graph to address a similar problem to Open IE,which we call Open Knowledge Extraction (OKE) using an approach we call theLinked Open Knowledge Extractor (LOKE, pronounced like "Loki"). We consider theentity linking task essential to construction of real world knowledge graphs.We merge the CaRB benchmark scoring approach with data from the TekGen datasetfor the LOKE task. We then show that a well engineered prompt, paired with anaive entity linking approach (which we call LOKE-GPT), outperforms AllenAI'sOpenIE 4 implementation on the OKE task, although it over-generates triplescompared to the reference set due to overall triple scarcity in the TekGen set.Through an analysis of entity linkability in the CaRB dataset, as well asoutputs from OpenIE 4 and LOKE-GPT, we see that LOKE-GPT and the "silver"TekGen triples show that the task is significantly different in content fromOIE, if not structure. Through this analysis and a qualitative analysis ofsentence extractions via all methods, we found that LOKE-GPT extractions are ofhigh utility for the KGC task and suitable for use in semi-automated extractionsettings.

LOKE: Linked Open Knowledge Extraction for Automated Knowledge Graph Construction