Abstract: The paper presents a new hybrid schema matching algorithm: Semantic Structure Matching Recommendation Algorithm (SSRMA). SSRMA is able to discover lexical correspondences without breaking the structural ones — it is capable of rejecting trivial lexical similarities, if the structural context suggests that a given matching is inadequate. The algorithm enables achieving results that are comparable to those obtained by means of state-of-the-art schema matching solutions. The presented method involves an adaptable pre-processing and flexible internal data representation, which allows to use a variety of auxiliary data (e.g., textual corpora) and to increase the accuracy of semantic matches accommodated in a given domain. In order to increase the mapping quality, the method allows to extend the input data by auxiliary information that may have the form of ontologies or textual corpora.
Publication Year: 2011
Publication Date: 2011-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot