Knowledge workers' personal and work related concepts (e.g. persons, projects, topics) are usually not sufficiently covered by knowledge graphs.
Yet, already handmade classification schemes, prominently folder structures, naturally mention several of their concepts in file names.
Thus, such data could be a promising source for constructing personal knowledge graphs.
However, this idea poses several challenges:
file names are usually noisy non-grammatical text snippets, while folder structures do not clearly define how concepts relate to each other.
To cope with this semantic gap, we include knowledge workers as humans-in-the-loop to guide the building process with their feedback.
Our semi-automatic personal knowledge graph construction approach consists of four major stages: domain term extraction, ontology population, taxonomic and non-taxonomic relation learning.
We conduct a case study with four expert interviews from different domains in an industrial scenario.
Results indicate that file systems are promising sources and, combined with our approach, already yield useful personal knowledge graphs with moderate effort spent.
Authors
