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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In a paper published in the 2001 VLDB Conference, we proposed treating generic schema matching as an independent problem. We developed a taxonomy of existing techniques, a new schema matching algorithm, and an approach to comparative evaluation. Since then, the field has grown into a major research topic. We briefly summarize the new techniques that have been developed and applications of the techniques in the commercial world. We conclude by discussing future trends and recommendations for further work. Taxonomies are the backbone of many structured, semantic knowledge resources. Recent works for extracting taxonomic relations from text focused on collecting lexical-syntactic patterns to extract the taxonomic relations by matching the patterns to text. These approaches, however, often show low coverage due to the lack of contextual analysis across sentences. To address this issue, we propose a novel approach that collectively utilizes contextual information of terms in syntactic structures such that if the set of contexts of a term includes most of contexts of another term, a subsumption relation between the two terms is inferred. We apply this method to the task of taxonomy construction from scratch, where we introduce another novel graph-based algorithm for taxonomic structure induction. Our experiment results show that the proposed method is well complementary with previous methods of linguistic pattern matching and significantly improves recall and thus F-measure. We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word's coordinate terms to help in determining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (WordNet 2.1). We add 10,000 novel synsets to WordNet 2.1 at 84 precision, a relative error reduction of 70 over a non-joint algorithm using the same component classifiers. Finally, we show that a taxonomy built using our algorithm shows a 23 relative F-score improvement over WordNet 2.1 on an independent testset of hypernym pairs. This paper deals with taxonomy alignment. It presents structural techniques of an alignment method suitable with a dissymmetry in the structure of the mapped taxonomies. The aim is to allow a uniform access to documents belonging to a same application domain, assuming retrieval of documents is supported by taxonomies.