Author(s): Felix Kuhr, Bjarne Witten, Ralf Moeller
Abstract: Knowledge graph systems produce huge knowledge graphs representing entities and relations.
Annotating documents with parts of these graphs to have symbolic content descriptions representing the semantics of documents ignore the authors’ higher purpose in mind.
Authors often paraphrase words and use synonyms encoding the semantics of text instead of explicitly expressing the textual semantics.
Hence, it is difficult to annotate documents with entities and relations from generic knowledge graphs.
In this paper, we present an unsupervised approach identifying annotations for documents using annotations of related documents representing a symbolic content description including the authors’ higher purpose in mind and introduce an EM-like algorithm iteratively optimizing the document-specific annotations.
Keywords: semantic computation; unsupervised text annotation; annotation database enrichment