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.
The problem of aligning taxonomies have received much attention by the research community since various knowledge based applications, including clustering algorithms, browsing support interfaces, and recommendation systems, perform more effectively when they are supported with domain describing taxonomies, which help to resolve ambiguities and provide context @cite_2 . Furthermore, this problem is of great interest on a number of application areas, especially in scientific @cite_13 , business @cite_14 @cite_9 , and web data integration @cite_16 @cite_6 .
@cite_14 @cite_9 @cite_6 @cite_2 @cite_16 @cite_13
61729836 2100365109 1572864191 131296644 1979891362 1566392645
This paper proposes SCHEMA, an algorithm for automated mapping between heterogeneous product taxonomies in the e-commerce domain. SCHEMA utilises word sense disambiguation techniques, based on the ideas from the algorithm proposed by Lesk, in combination with the semantic lexicon WordNet. For finding candidate map categories and determining the path-similarity we propose a node matching function that is based on the Levenshtein distance. The final mapping quality score is calculated using the Damerau-Levenshtein distance and a node-dissimilarity penalty. The performance of SCHEMA was tested on three real-life datasets and compared with PROMPT and the algorithm proposed by Park & Kim. It is shown that SCHEMA improves considerably on both recall and F @math -score, while maintaining similar precision. We address the problem of unsupervised matching of schema information from a large number of data sources into the schema of a data warehouse. The matching process is the first step of a framework to integrate data feeds from third-party data providers into a structured-search engine's data warehouse. Our experiments show that traditional schema-based and instance-based schema matching methods fall short. We propose a new technique based on the search engine's clicklogs. Two schema elements are matched if the distribution of keyword queries that cause click-throughs on their instances are similar. We present experiments on large commercial datasets that show the new technique has much better accuracy than traditional techniques. We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category disambiguation and taxonomy restructuring perform with high accuracy. Besides, we assess these methods on automatically generated datasets and show that we are able to effectively enrich WordNet with a large number of instances from Wikipedia. Our approach produces an integrated resource, thus bringing together the fine-grained classification of instances in Wikipedia and a well-structured top-level taxonomy from WordNet. A valve unit for a flowing medium is combined with an electrical measuring system to provide measurement of the rate of flow of the medium. The valve unit is connected to a propeller-type rotor disposed in a conduit which communicates with the valve. The rotor incorporates a permanent magnet in at least one vane thereof and the measuring system includes a magnetic pulse generator which senses the magnetic field produced by the magnet and generates a corresponding train of pulses during the rotation of the rotor. An amplifier-differentiator connected to the pulse generator produces an output in accordance with the derivative of the pulses so as to provide an indication of the rate of flow. To operate effectively, the Semantic Web must be able to make explicit the semantics of Web resources via ontologies, which software agents use to automatically process these resources. The Web's natural semantic heterogeneity presents problems, however - namely, redundancy and ambiguity. The authors' ontology matching, clustering, and disambiguation techniques aim to bridge the gap between syntax and semantics for Semantic Web construction. Their approach discovers and represents the intended meaning of words in Web applications in a nonredundant way, while considering the context in which those words appear. Resources located in digital libraries are labeled (or classified) based on taxonomies. On multiple digital libraries, however, heterogeneity between taxonomies is a serious problem for efficient interoperation processes (e.g., information sharing and query transformation). In order to overcome this problem, we propose a novel framework based on aligning taxonomies of digital libraries. Thereby, the best mapping between concepts has to be discovered to maximize the summation of a set of partial similarities. For experimentation, three digital libraries were built based on different taxonomies. Taxonomy alignment-based resource retrieval was evaluated by human experts, and we measured recall and precision measures retrieved by concept replacement strategy.