  An increasing number of data and knowledge sources are accessible by human
and software agents in the expanding Semantic Web. Sources may differ in
granularity or completeness, and thus be complementary. Consequently, they
should be reconciled in order to unlock the full potential of their conjoint
knowledge. In particular, units should be matched within and across sources,
and their level of relatedness should be classified into equivalent, more
specific, or similar. This task is challenging since knowledge units can be
heterogeneously represented in sources (e.g., in terms of vocabularies). In
this paper, we focus on matching n-ary tuples in a knowledge base with a
rule-based methodology. To alleviate heterogeneity issues, we rely on domain
knowledge expressed by ontologies. We tested our method on the biomedical
domain of pharmacogenomics by searching alignments among 50,435 n-ary tuples
from four different real-world sources. Results highlight noteworthy agreements
and particularities within and across sources.
