Oracle International Corporation

Mohammad Faisal

Yasuhiro Matsuda

Wesley Lin

Garret Frederick Swart

Geeta Arora

Sundeep Abraham

James Conklin

Ravi Palakodety

Rikin Gandhi

Systems, methods, and other embodiments associated with real-time text indexing are described. One example method includes receiving a document for indexing in a search system that includes a mature index and indexing the received document in a staging index. The staging index may be stored in direct access memory associated with query processing that does not degrade query performance even when postings become fragmented. The staging index and the mature text index are accessed to process queries on the search system. The example method may also include periodically merging the staging index into the mature index based on query feedback.

Real-time text indexing

Systems, methods, and other embodiments associated with selectively acquiring and utilizing query statistics are described. One example method includes selectively acquiring statistics associated with search queries submitted to a search process during a sample period. The statistics may be associated with an index and tokens associated with the index and search queries. The method may also include selecting tokens and/or queries to optimize based, at least in part, on the statistics. In one example, whether and/or how the tokens and/or queries are optimized may depend on estimating index fragmentation based, at least in part, on the statistics.

Query statistics

Systems, methodologies, media, and other embodiments associated with efficiently computing document similarity are described. One exemplary system embodiment includes logic to produce a gram from a string and logic to identify candidate documents based on identifying matches between query grams and document grams stored in an inverted index that relates grams to documents. The example system may also include logic to selectively partially reconstruct a candidate document from entries in the inverted index and logic to compute an edit distance between a string associated with a query and a string associated with the partially reconstructed candidate document. The example system may also include a signal logic configured to provide a signal corresponding to the edit distance.

Efficient computation of document similarity

A technique for generating cross-references among categories in a knowledge base extracts a plurality of themes from a corpus of documents. A theme identifies subject matter contained in a corresponding document. A plurality of scores are generated such that each score identifies a relative theme strength among theme pairs of the themes extracted from the documents. In general, a theme strength reflects the amount of subject matter contained in a document for a corresponding theme relative to other themes in the document. Thereafter, the most related theme pairs are selected as indicated by the scores. Category pairs of the knowledge base are then selected by mapping the themes of the selected theme pairs to corresponding categories of the knowledge base. A cross-reference between categories of the category pairs in the knowledge base is generated so as to identify an association between the category pairs.

Automated extension for generation of cross references in a knowledge base

An information retrieval system processes user input queries, and identifies query feedback, including ranking the query feedback, to facilitate the user in re-formatting a new query. A knowledge base, which comprises a plurality of nodes depicting terminological concepts, is arranged to reflect conceptual proximity among the nodes. The information retrieval system processes the queries, identifies topics related to the query as well as query feedback terms, and then links both the topics and feedback terms to nodes of the knowledge base with corresponding terminological concepts. At least one focal node is selected from the knowledge base based on the topics to determine a conceptual proximity between the focal node and the query feedback nodes. The query feedback terms are ranked based on conceptual proximity to the focal node. A content processing system that identifies themes from a corpus of documents for use in query feedback processing is also disclosed.

Ranking of query feedback terms in an information retrieval system

An information retrieval system generates query feedback terminology for an input query based on a corpus of documents and the input query. Potential query feedback terms are identified through the corpus of documents for use as query feedback in the information retrieval system. To process a query, which includes at least one query term, co-occurrence signatures for &ldquo;N&rdquo; query feedback terms are generated. An N&times;N matrix of the co-occurrence signatures depict patterns of semantic distance and conceptual proximity among the &ldquo;N&rdquo; query feedback terms. The N&times;N matrix is processed to reduce the number of the entries in the signature to an N&times;M matrix while preserving the co-occurrence distance characteristics among the signatures. Query feedback terms with co-occurrence signatures, which compare with co-occurrence signatures of the query term in a predetermined manner, are selected as query feedback.

Methods and apparatus for generating query feedback based on co-occurrence patterns

An information retrieval system generates hierarchical query feedback to a user to facilitate the user in reformulating the query. The information retrieval system, which supports both text and theme queries, includes a knowledge base comprising a plurality of nodes of terminology, arranged hierarchically, that reflect associations among the terminology. For the hierarchical query feedback terms, the information retrieval system selects terminology that broadens and narrows the query terms by selecting parent nodes and child nodes, respectively, of the nodes for terminology that corresponds to the terms of the query. The information retrieval system also selects terminology that is generally related to the query terms by selecting nodes of the knowledge base that are cross linked to the nodes for terminology that corresponds to the terms of the query. Normalization processing, which generates canonical forms for query processing, and a content processing system, which generates themes for theme queries, are also disclosed.

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Belmont, CA, United States of America

Mohammad Faisal