From Field Notes towards a Knowledge Base

Abstract

We describe the process of converting plain text cultural heritage data to elements of a domain-specific knowledge base, using general machine learning techniques. First, digitised expedition field notes are segmented and labelled automatically. In order to obtain perfect records, we create an annotation tool that features selective sampling, allowing domain experts to validate automatically labelled text, which is then stored in a database. Next, the records are enriched with semi-automatically derived secondary metadata. Metadata enable fine-grained querying, the results of which are additionally visualised using maps and photos.

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