A framework for modeling and evaluating automatic semantic reconciliation

Abstract

Abstract. The introduction of the Semantic Web vision and the shift toward machine understandable Web resources has unearthed the importance of automatic semantic reconciliation. Consequently, new tools for automating the process were proposed. In this work we present a formal model of semantic reconciliation and analyze in a systematic manner the properties of the process outcome, primarily the inherent uncertainty of the matching process and how it reflects on the resulting mappings. An important feature of this research is the identification and analysis of factors that impact the effectiveness of algorithms for automatic semantic reconciliation, leading, it is hoped, to the design of better algorithms by reducing the uncertainty of existing algorithms. Against this background we empirically study the aptitude of two algorithms to correctly match concepts. This research is both timely and practical in light of recent attempts to develop and utilize methods for automatic semantic reconciliation.

Optimizing Ontology Alignments by Using Genetic Algorithms

“…3. Comparison between most outstanding tools [24] [25] and another called ontology meta-matching [26] that tries to optimize automatically the parameters related to matching task. So, our approach could be considered a mechanism for meta-matching.…”

Ten Challenges for Ontology Matching

“…The issue of dealing with uncertainty in ontology matching has been addressed in [8,16,28,29,53,63]. A way of modeling ontology matching as an uncertain process is by using similarity matrices as a measure of certainty.…”

“…A matcher then is measured by the fit of its estimation of a certainty of a correspondence to the real world. In [29], such a formal framework was provided, attempting to answer the question of whether there are good and bad matchers. Uncertainty can also be reduced iteratively.…”

Semantic Matching: Algorithms and Implementation

“…The relatively poor improvement (18%) occurs because our optimizations are implemented in a straightforward way. More precisely, on small trees (e.g., test case #4) a big constant factor 15 dominates the growth of all other components in S-Match computational complexity formula.…”

“…At present, there exists a line of semi-automated schema matching systems, see, for instance [5,10,13,15,32,30,35,39,49,28,46]. A good survey and a classification of matching approaches up to 2001 is provided in [42], an extension of its schema-based part and a user-centric classification of matching systems is provided in [43], while the work in [14] considers both [42,43] as well as some other classifications.…”