Abstract: A computer receives a search query from a user for finding a resource. The computer obtains a description on a page on a net on which an unknown word extracted from the search query is found. The computer extracts one or more second words from the description using morphological analysis. The computer assigns at least one second category to the one or more second words extracted from the description. The computer finds, among the one or more second words, a particular word to which a predetermined category is assigned, extracts a correlation word having a high correlation with the particular word, and finds, in a dictionary, a search word from among one or more first words extracted from the search query and assigned a first category that is the same as the predetermined category. The computer finds, from a repository, resource data using the correlation word and the search word.

Abstract: A computer receives a search query from a user for finding a resource. The computer obtains a description on a page on a net on which an unknown word extracted from the search query is found. The computer extracts one or more second words from the description using morphological analysis. The computer assigns at least one second category to the one or more second words extracted from the description. The computer finds, among the one or more second words, a particular word to which a predetermined category is assigned, extracts a correlation word having a high correlation with the particular word, and finds, in a dictionary, a search word from among one or more first words extracted from the search query and assigned a first category that is the same as the predetermined category. The computer finds, from a repository, resource data using the correlation word and the search word.

Abstract: A computer receives a search query from a user for finding a resource. The computer extracts one or more words from the search query using morphological analysis. The computer assigns at least one first category to at least one first word of the one or more words using a dictionary. In response to identifying an unknown word not in the dictionary within the one or more words, the computer searches for the unknown word on a net.

Abstract: A computer receives a search query from a user for finding a resource. The computer extracts one or more words from the search query using morphological analysis. The computer assigns at least one first category to at least one first word of the one or more words using a dictionary. In response to identifying an unknown word not in the dictionary within the one or more words, the computer searches for the unknown word on a net.

Abstract: A computer system of an embodiment of the disclosure can be used to automatically create or populate a terms dictionary using a set of computing units. A morphological analysis unit can acquire token sequence data by performing morphological analysis for the text data. A category distinguishing unit can distinguish tokens of the token sequence data by using a category dictionary to extract uncategorized words. An uncategorized-word comparing unit can compare each of the extracted uncategorized words with an uncategorized-word comparison rule to extract an uncategorized word matching the uncategorized-word comparison rule as a registration candidate word. A token-sequence comparing unit can compare a token sequence of the token sequence data with a token-sequence comparison rule to extract a token sequence matching the token-sequence comparison rule as registration candidate words. A permission unit can permit a user to select whether to register the registration candidate words in the category dictionary.

Abstract: Techniques for analyzing keyword data for quality management purposes are provided. One or more keywords are selected. Each of the one or more keywords represent a category of quality management. A keyword time series is prepared for each of the one or more selected keywords. A set of fixed form time series is prepared for each of the one or more selected keywords. The set of fixed form time series comprises one or more fixed form time series representing statistical data related to the one or more selected keywords. One or more correction sets comprising one or more correction parameters are obtained. Each of the one or more correction parameters correspond to one of the one or more fixed form time series within each set of fixed form time series. A set of corrected time series is generated for each of the one or more correction sets.

Abstract: A technique for dynamic integration and semantic analysis of structured data and unstructured textual data including: defining and selecting static attributes and dynamic attribute from structured data, embedding static and dynamic views of the selected corresponding attributes in an annotated document, linking the unstructured textual data with the structured data using the defined static and dynamic attributes, populating an annotated document structure of multiple annotated documents, performing semantic analysis of a query across the unstructured textual data and structured data, querying the annotated document structure to provide query results satisfying static part of the query, processing static and dynamic parts of the query by querying structured data and the annotated document structure, as appropriate, and providing a combined query processing result satisfying the dynamic and static part the query. Other embodiments are also disclosed.

Abstract: A computer system of an embodiment of the disclosure can be used to automatically create or populate a terms dictionary using a set of computing units. A morphological analysis unit can acquire token sequence data by performing morphological analysis for the text data. A category distinguishing unit can distinguish tokens of the token sequence data by using a category dictionary to extract uncategorized words. An uncategorized-word comparing unit can compare each of the extracted uncategorized words with an uncategorized-word comparison rule to extract an uncategorized word matching the uncategorized-word comparison rule as a registration candidate word. A token-sequence comparing unit can compare a token sequence of the token sequence data with a token-sequence comparison rule to extract a token sequence matching the token-sequence comparison rule as registration candidate words. A permission unit can permit a user to select whether to register the registration candidate words in the category dictionary.

Abstract: Techniques for analyzing keyword data for quality management purposes are provided. One or more keywords are selected. Each of the one or more keywords represent a category of quality management. A keyword time series is prepared for each of the one or more selected keywords. A set of fixed form time series is prepared for each of the one or more selected keywords. The set of fixed form time series comprises one or more fixed form time series representing statistical data related to the one or more selected keywords. One or more correction sets comprising one or more correction parameters are obtained. Each of the one or more correction parameters correspond to one of the one or more fixed form time series within each set of fixed form time series. A set of corrected time series is generated for each of the one or more correction sets.