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Abstract:
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Most of the current musicological knowledge is present
in printed books and manuscripts. In the last years greats efforts have
been done in order to digitize and make available these documents in
form of Digital Libraries. However, digital documents are mainly stored
as raw text, with no more structure than indexes and some metadata.
Therefore, implicit knowledge contained in text is not understandable
by computers and cannot be processed like that. Automatic processing
of text documents may help musicologists in several ways, such as
improving navigation through a library, discovering hidden knowledge,
accelerating tedious tasks, etc. To apply these techniques to a Digital
Library, the information contained in documents should be carefully
structured and semantically annotated. Information Extraction is a
discipline of computer science focused on the extraction of structured
information from unstructured text sources. We propose a method to
automatically extract meaningful knowledge from documents present in
Digital Musical Document Libraries, by using Information Extraction
techniques. Our method has two main steps. First, relevant named
entities (e.g. composers, organizations, places, etc.) are identified in
the text. Second, words between these entities are syntactically and
semantically analyzed to understand the relationship between them.
Finally, the extracted knowledge is represented in a machine-readable
format as a knowledge graph, where entities are represented as nodes,
and relations as edges. The resulting knowledge representation is finally
visualized as an interactive graph. With the proposed information
visualization, users may go from one document to another by browsing
the knowledge graph. We tested our method with a subset of artist
biographies present in the Grove Music Online. |
