Concept disambiguation for improved subject access using multiple knowledge sources

We address the problem of mining text forrelevant image metadata. Our work is situ-
ated in the art and architecture domain,
where highly specialized technical vocabu-
lary presents challenges for NLP tech-
niques. To extract high quality metadata,
the problem of word sense disambiguation
must be addressed in order to avoid leading
the searcher to the wrong image as a result
of ambiguous—and thus faulty—meta-
data. In this paper, we present a disam-
biguation algorithm that attempts to select
the correct sense of nouns in textual de-
scriptions of art objects, with respect to a
rich domain-specific thesaurus, the Art and
Architecture Thesaurus (AAT). We per-
formed a series of intrinsic evaluations us-
ing a data set of 600 subject terms ex-
tracted from an online National Gallery of
Art (NGA) collection of images and text.
Our results showed that the use of external
knowledge sources shows an improvement
over a baseline.