Knowledge-Based Matching of $n$-ary Tuples
This addresses the challenge of reconciling complementary but heterogeneously represented knowledge sources for applications like the Semantic Web, though it appears incremental as it applies existing rule-based methods to a specific domain.
The paper tackles the problem of matching n-ary tuples across heterogeneous knowledge sources in the Semantic Web, using a rule-based method with ontologies to handle vocabulary differences, and tests it on 50,435 tuples from four biomedical sources, highlighting agreements and particularities.
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.