Abstract: A flexible and extensible architecture allows for secure searching across an enterprise. Such an architecture can provide a simple Internet-like search experience to users searching secure content inside (and outside) the enterprise. The architecture allows for the crawling and searching of a variety of sources across an enterprise, regardless of whether any of these sources conform to a conventional user role model. The architecture further allows for security, recency, or other attributes to be submitted at query time, for example, in order to re-rank query results from enterprise resources. The user query also can be transformed to provide for dynamic querying that provides for a more current result list than can be obtained for static queries.

Abstract: A flexible and extensible architecture allows for secure searching across an enterprise. Such an architecture can provide a simple Internet-like search experience to users searching secure content inside (and outside) the enterprise. The architecture allows for the crawling and searching of a variety of sources across an enterprise, regardless of whether any of these sources conform to a conventional user role model. The architecture further allows for security, recency, or other attributes to be submitted at query time, for example, in order to re-rank query results from enterprise resources. The user query also can be transformed to provide for dynamic querying that provides for a more current result list than can be obtained for static queries.

Abstract: Search term ranking algorithms can be generated and updated based on customer settings, such as where a ranking algorithm is modeled as a combination function of different ranking factors. An end user of a search system provides personalized preferences for weighted attributes, generally or for a single instance of the query. The user also can indicate the relative importance of one or more ranking factors by specifying different weights to the factors. Ranking factors can specify document attributes, such as document title, document body, document page rank, etc. Based on the attribute weights and the received user query, a ranking algorithm function will produce the relevant value for each document corresponding to the user preferences and personalization configurations.

Abstract: Systems, methods, and other embodiments associated with automatically summarizing a document are described. One method embodiment includes computing term scores for members of a set of terms in a document to be summarized and computing sentence scores for sentences in a set of sentences in the document. The method embodiment also includes computing a set of entries for a term-sentence matrix that relates terms to sentences. The method embodiment also includes computing a dominant topic for the document and simultaneously ranking the set of terms and the set of sentences based on the dominant topic. The method embodiment provides a summarization item(s) selected from the set of terms and/or the set of sentences.

Abstract: In one embodiment, access one or more query chains, wherein each one of the query chains comprises two or more search queries, {q1, . . . , qn}, which are recency-sensitive, are related to the same subject matter, and are issued to a search engine sequentially, and actual click-through information associated with each one of the query chains; and smooth each one of the query chains using the actual click-through information associated with the query chain. To smooth one of the query chains comprises, for each one of search queries, qj, in the query chain, where 2?j?n, if one of the network resources identified for qj has actually been clicked in connection with qj by the corresponding one network user, then presume that the one network resource has been clicked in connection with one or more search queries, qk, in the query chain, where 1?k<j.

Abstract: In one embodiment, access one or more query-resource pairs, wherein for each one of the query-resource pairs comprising one of one or more search queries and one of one or more network resources, the one search query is recency-sensitive with respect to a particular time period, and the one network resource is identified for the one search query, and a resource-view count and a resource-click count associated with each one of the query-resource pairs; and construct one or more first click features using the resource-view counts and the resource-click counts associated with the query-resource pairs. To construct one of the first click features in connection with one of the query-resource pairs comprises determine a only-resource-click count associated with the one query-resource pair; and calculate a ratio between the only-resource-click count and the resource-view count associated with the one query-resource pair as the one first click feature.

Abstract: Search term ranking algorithms can be generated and updated based on customer settings, such as where a ranking algorithm is modeled as a combination function of different ranking factors. An end user of a search system provides personalized preferences for weighted attributes, generally or for a single instance of the query. The user also can indicate the relative importance of one or more ranking factors by specifying different weights to the factors. Ranking factors can specify document attributes, such as document title, document body, document page rank, etc. Based on the attribute weights and the received user query, a ranking algorithm function will produce the relevant value for each document corresponding to the user preferences and personalization configurations.

Abstract: One embodiment accesses a search query and one or more sets of clicked network resources corresponding to the search query; determines a classifier model that represents the sets of clicked network resources that each satisfy the information need of one of the users and one or more subsets of the sets of clicked network resources that each do not satisfy the information need of one of the users; computes a probability value for each clicked network resource from each of the sets of clicked network resources using the classier model, wherein the probability value represents a likelihood that, after clicking on the corresponding network resource, the particular one of the users conducting the corresponding particular one of the search sessions ends the search session; and forms a set of features comprising the probability values computed for network resources from the search sessions.

Abstract: A flexible and extensible architecture allows for secure searching across an enterprise. Such an architecture can provide a simple Internet-like search experience to users searching secure content inside (and outside) the enterprise. The architecture allows for the crawling and searching of a variety of sources across an enterprise, regardless of whether any of these sources conform to a conventional user role model. The architecture further allows for security, recency, or other attributes to be submitted at query time, for example, in order to re-rank query results from enterprise resources. The user query also can be transformed to provide for dynamic querying that provides for a more current result list than can be obtained for static queries.

Abstract: In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features.

Abstract: Systems, methods, and other embodiments associated with extracting knowledge from application data and maintaining an ontology based on the extracted knowledge are described. One example system includes a mapping logic to store mappings between application objects and ontology classes and an information extraction (IE) logic that accesses the mapping logic to identify application data to process based on the mappings. The application data may be stored in application data repositories belonging to an enterprise and may be characterized by the application object. Having identified application data to process, the IE logic may locate data in the application data repositories and selectively manipulate an ontology based on selected application data elements.

Abstract: To estimate, or predict, the relevance of items, or documents, in a set of search results, relevance information is extracted from user click data, and relational information among the documents as manifested by an aggregation of user clicks is determined from the click data. A supervised approach uses judgment information, such as human judgment information, as part of the training data used to generate a relevance predictor model, which minimizes the inherent noisiness of the click data collected from a commercial search engine.

Abstract: Systems, methods, media, and other embodiments associated with (non)contiguous n-gram based spell correction are described. One exemplary system embodiment includes logic for creating contiguous and non-contiguous trigrams, logic for creating an inverted index relating trigrams and the words from which they were generated, and logic for comparing trigrams associated with a word to spell check to trigrams associated with the words selected using the inverted index.

Abstract: Systems, methods, media, and other embodiments associated with ranking documents by providing a search engine with a series of sub-queries generated from an original query are described. One example system includes input logic for receiving a query. The example system may include a relaxation logic configured to produce sub-queries from the query. The sub-queries may describe metadata string matching, content string matching, and/or metadata numerical attribute analysis. The sub-queries may be provided by an output logic to a search engine in an order that facilitates defining document relevance without requiring post-retrieval relevance ranking.

Abstract: Systems, methods, and other embodiments associated with automatically summarizing a document are described. One method embodiment includes computing term scores for members of a set of terms in a document to be summarized and computing sentence scores for sentences in a set of sentences in the document. The method embodiment also includes computing a set of entries for a term-sentence matrix that relates terms to sentences. The method embodiment also includes computing a dominant topic for the document and simultaneously ranking the set of terms and the set of sentences based on the dominant topic. The method embodiment provides a summarization item(s) selected from the set of terms and/or the set of sentences.

Abstract: Systems, methods, and other embodiments associated with automatically summarizing a document are described. One method embodiment includes computing term scores for members of a set of terms in a document to be summarized and computing sentence scores for sentences in a set of sentences in the document. The method embodiment also includes computing a set of entries for a term-sentence matrix that relates terms to sentences. The method embodiment also includes computing a dominant topic for the document and simultaneously ranking the set of terms and the set of sentences based on the dominant topic. The method embodiment provides a summarization item(s) selected from the set of terms and/or the set of sentences.

Abstract: A flexible and extensible architecture allows for secure searching across an enterprise. Such an architecture can provide a simple Internet-like search experience to users searching secure content inside (and outside) the enterprise. The architecture allows for the crawling and searching of a variety of sources across an enterprise, regardless of whether any of these sources conform to a conventional user role model. The architecture further allows for security, recency, or other attributes to be submitted at query time, for example, in order to re-rank query results from enterprise resources. The user query also can be transformed to provide for dynamic querying that provides for a more current result list than can be obtained for static queries.

Abstract: Systems, methods, and other embodiments associated with identifying and selectively deleting duplicate search results are described. One example system embodiment includes logic to receive an identity indicator from a search logic. The identity indicator is associated with a search item that the search logic determines to be relevant to a search request. The example system may also include logic to determine whether the search result associated with the identity indicator is a duplicate result based on comparing the identity indicator to another identity indicator associated with another search result.

Abstract: A flexible and extensible architecture allows for secure searching across an enterprise. Such an architecture can provide a simple Internet-like search experience to users searching secure content inside (and outside) the enterprise. The architecture allows for the crawling and searching of a variety of sources across an enterprise, regardless of whether any of these sources conform to a conventional user role model. The architecture further allows for security, recency, or other attributes to be submitted at query time, for example, in order to re-rank query results from enterprise resources. The user query also can be transformed to provide for dynamic querying that provides for a more current result list than can be obtained for static queries.

Abstract: Acronym and synonym pairs can be identified and retrieved automatically in a corpus and/or across an enterprise based on customer settings globally or for a single instance. Possible acronym and synonym term pairs can be identified using a rule such as a heuristic, user-defined rule. Rules selected by the user can be used to rank acronym and synonym pairs using factors such as occurrence frequency and maximum term length. A rule interpreter engine executes the user defined rule set to properly identify and retrieve the user selected acronym and synonym pairs through the utilization of a shallow pause read step. Finally, the user selected acronym and synonym pairs are ranked according to the user preferences, and can be displayed or held for subsequent use in searching.