Induction of a Large-Scale Knowledge Graph from the Regesta Imperii
We induce and visualize a Knowledge Graph over the Regesta Imperii (RI), an important large-scale resource for medieval history research. The RI comprise more than 150,000 digitized abstracts of medieval charters issued by the Roman-German kings and popes distributed over many European locations and a time span of more than 700 years. Our goal is to provide a resource for historians to visualize and query the RI, possibly aiding medieval history research. The resulting medieval graph and visualization tools are shared publicly.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
μgat: Improving Single-Page Document Parsing by Providing Multi-Page Context
Regesta are catalogs of summaries of other documents and, in some cases, are the only source of information about the content of such full-length documents. For this reason, they are of great interest to scholars in many…
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously extract knowledge tripl…
graph constructionKnowledge GraphsDeriving Players \& Themes in the Regesta Imperii using SVMs and Neural Networks
Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification
Event schemas are a form of world knowledge about the typical progression of events. Recent methods for event schema induction use information extraction systems to construct a large number of event graph instances from …
Event ExpansionWorld KnowledgeKnowledge-Enriched Event Causality Identification via Latent Structure Induction Networks
Identifying causal relations of events is an important task in natural language processing area. However, the task is very challenging, because event causality is usually expressed in diverse forms that often lack explic…
DescriptiveEvent Causality Identification