In this work we present SIFT, a 3-step algorithm for the analysis of the structural information represented by means of a taxonomy. The major advantage of this algorithm is the capability to leverage the information inherent to the hierarchical structures of taxonomies to infer correspondences which can allow to merge them in a later step. This method is particular relevant in scenarios where taxonomy alignment techniques exploiting textual information from taxonomy nodes cannot operate successfully.
Existing taxonomy alignment techniques focus on different dimensions of the problem, including whether data instances are used for matching @cite_8 , whether linguistic information and other auxiliary information are available @cite_5 , and whether the match is performed for complex structures @cite_15 . Our algorithm fits in this last category.
@cite_5 @cite_15 @cite_8
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Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is an important challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do this, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries. Matching elements of two data schemas or two data instances plays a key role in data warehousing, e-business, or even biochemical applications. In this paper we present a matching algorithm based on a fixpoint computation that is usable across different scenarios. The algorithm takes two graphs (schemas, catalogs, or other data structures) as input, and produces as output a mapping between corresponding nodes of the graphs. Depending on the matching goal, a subset of the mapping is chosen using filters. After our algorithm runs, we expect a human to check and if necessary adjust the results. As a matter of fact, we evaluate the 'accuracy' of the algorithm by counting the number of needed adjustments. We conducted a user study, in which our accuracy metric was used to estimate the labor savings that the users could obtain by utilizing our algorithm to obtain an initial matching. Finally, we illustrate how our matching algorithm is deployed as one of several high-level operators in an implemented testbed for managing information models and mappings. Ontology Matching aims to find the semantic correspondences between ontologies that belong to a single domain but that have been developed separately. However, there are still some problem areas to be solved, because experts are still needed to supervise the matching processes and an efficient way to reuse the alignments has not yet been found. We propose a novel technique named Reverse Ontology Matching, which aims to find the matching functions that were used in the original process. The use of these functions is very useful for aspects such as modeling behavior from experts, performing matching-by-example, reverse engineering existing ontology matching tools or compressing ontology alignment repositories. Moreover, the results obtained from a widely used benchmark dataset provide evidence of the effectiveness of this approach.