Abstract: A method and system for labeling object information of an information page is provided. A labeling system identifies an object record of an information page based on the labeling of object elements within an object record and labels object elements based on the identification of an object record that contains the object elements. To identify the records and label the elements, the labeling system generates a hierarchical representation of blocks of an information page. The labeling system identifies records and elements within the records by propagating probability-related information of record labels and element labels through the hierarchy of the blocks. The labeling system generates a feature vector for each block to represent the block and calculates a probability of a label for a block being correct based on a score derived from the feature vectors associated with related blocks. The labeling system searches for the labeling of records and elements that has the highest probability of being correct.

Abstract: A method and system is provided for determining relevance of an object to a term based on a language model. The relevance system provides records extracted from web pages that relate to the object. To determine the relevance of the object to a term, the relevance system first determines, for each record of the object, a probability of generating that term using a language model of the record of that object. The relevance system then calculates the relevance of the object to the term by combining the probabilities. The relevance system may also weight the probabilities based on the accuracy or reliability of the extracted information for each data source.

Abstract: A method and system for determining relatedness of images of pages based on link and page layout analysis. A link analysis system determines relatedness between images by first identifying blocks within web pages, and then analyzing the importance of the blocks to web pages, web pages to blocks, and images to blocks. Based on this analysis, the link analysis system determines the degree to which each image is related to each other image. The link analysis system may also use the relatedness of images to generate a ranking of the images. The link analysis system may also generate a vector representation of the images based on their relatedness and apply a clustering algorithm to the vector representations to identify clusters of related images.

Abstract: Systems and methods for query expansion are described. In one aspect, new terms are extracted from a newly submitted query. Terms to expand the new terms are identified to a relevant document list. The expansion term are identified at least in part on the new terms and probabilistic correlations from information in a query log. The query log information includes one or more query terms and a corresponding set of document identifiers (IDs). The query terms were previously submitted to a search engine. The document IDs represent each document selected from a list generated by the search engine in response to searching for information relevant to corresponding ones of the query terms.

Abstract: A method and system for identifying schemas of web databases is provided. A schema matching system generates a mapping between an interface schema and a result schema of a web database, which is used to represent the underlying database schema. The schema matching system also generates a mapping of the interface attributes and the result attributes of the web database to global attributes of a global schema whose semantics are known. Using these mappings, a search engine service can formulate queries using the global attributes, map those queries to the corresponding interface attributes, submit the query, and retrieve the values from the result attributes that correspond to the desired global attributes.

Abstract: A content object indexing process including creating a content object knowledge index, calculating a description vector of a target content object, and indexing the target content object by searching for the description vector in the content object knowledge database. It may be difficult to search for an exact content object such as a music file or academic researcher as a conventional search index may not include related hierarchical information. A content object indexing process may add hierarchical information taken from a content object knowledge index and incorporate the hierarchical information to the index entry for a specific content object. An application of such a content object indexing process may be a world wide web search engine.

Abstract: A labeling system uses a two-dimensional conditional random fields technique to label the object elements. The labeling system represents transition features and state features that depend on object elements that are adjacent in two dimensions. The labeling system represents the grid as a graph of vertices and edges with a vertex representing an object element and an edge representing a relationship between the object elements. The labeling system represents each diagonal of the graph as a sequence of states. The labeling system selects a labeling for the vertices of the diagonals that has the highest probability based on transition probabilities between vertices of adjacent diagonals and on the state probabilities of a position within a diagonal.

Abstract: Features extracted from network browser pages and/or network search queries are leveraged to facilitate in detecting a user's browsing and/or searching intent. Machine learning classifiers constructed from these features automatically detect a user's online commercial intention (OCI). A user's intention can be commercial or non-commercial, with commercial intentions being informational or transactional. In one instance, an OCI ranking mechanism is employed with a search engine to facilitate in providing search results that are ranked according to a user's intention. This also provides a means to match purchasing advertisements with potential customers who are more than likely ready to make a purchase (transactional stage). Additionally, informational advertisements can be matched to users who are researching a potential purchase (informational stage).

Abstract: A mechanism to classify source documents into one of two categories, either likely to contain desired information or unlikely to contain desired information. Generally some form of rules based classification in conjunction with deeper analysis using advanced techniques on difficult cases is utilized. The rules based classification is generally good for eliminating cases from further consideration and for identifying documents of interest based on generally discernable relationships between data or based on the presence or absence of data. The deeper analysis is used to uncover more complex relationships between data that may identify documents of interest. Portions of the process may use the entire document while other portions of the process may use only a portion of the document.

Abstract: A method and system for determining relevance of a document to a query based on identifier match distance is provided. The relevance system analyzes a training set of queries and documents to determine the relationship between identifier match distance and relevance of a document to a query. The identifier match distance indicates the distance from the end of an identifier of a document to an identifier term that matches a query term. The relevance system generates a prior relevance probability that a document with a certain identifier match distance is relevant to a query. The relevance system uses the prior relevance probabilities to determine relevance of documents to queries based on identifier match distance.

Abstract: A relevance system determines the relevance of a query term to a document based on spans within the document that contain the query term. The relevance system aggregates the relevance of the query terms into an overall relevance for the document. For each query term, the relevance system calculates a span relevance for each span that contains that query term. The relevance system then aggregates the span relevances for a query term into a query term relevance for that document. The relevance system may aggregate the query term relevances into a document relevance.

Abstract: Data classification using stochastic key feature generation includes obtaining a set of training data having associated summaries. The set of training data and associated summaries are used to generate a key feature generation model. Another set of training data having associated categories is also obtained, and the key feature generation model is used to map this other set of training data to a set of vectors. A data classifier is then trained based on the set of vectors and the associated categories.

Abstract: A system and method for delivering detailed product information to a user in response to a request for a product is provided. The delivered product information can include products identified by crawling web sites and extracting product information. The detailed information can include the name of the product, a picture of the product, the price of the product, a description of the product, and/or other information specifying a product for sale.

Abstract: Categorizing page block functionality to improve document layout for browsing is described. In one aspect, document content is analyzed with respect to multiple block function criteria. Results of this analysis are used to assign a respective block function to blocks of the document content. These block function assignments are used to generate one or more customized document layouts for browsing by a user.

Abstract: System events preceding occurrence of a problem are likely to be similar to events preceding occurrence of the same problem at other times or on other systems. Thus, the cause of a problem may be identified by comparing a trace of events preceding occurrence of the problem with previously diagnosed traces. Traces of events preceding occurrences of a problem arising from a known cause are reduced to a series of descriptive elements. These elements are aligned to correlate differently timed but otherwise similar traces of events, converted into symbolic representations, and archived. A trace of events leading to an undiagnosed a problem similarly is converted to a symbolic representation. The representation of the undiagnosed trace is then compared to the archived representations to identify a similar archived representation. The cause of the similar archived representation is presented as a diagnosis of the problem.

Abstract: System events preceding occurrence of a problem are likely to be similar to events preceding occurrence of the same problem at other times or on other systems. Thus, the cause of a problem may be identified by comparing a trace of events preceding occurrence of the problem with previously diagnosed traces. Traces of events preceding occurrences of a problem arising from a known cause are reduced to a series of descriptive elements. These elements are aligned to correlate differently timed but otherwise similar traces of events, converted into symbolic representations, and archived. A trace of events leading to an undiagnosed a problem similarly is converted to a symbolic representation. The representation of the undiagnosed trace is then compared to the archived representations to identify a similar archived representation. The cause of the similar archived representation is presented as a diagnosis of the problem.

Abstract: System events preceding occurrence of a problem are likely to be similar to events preceding occurrence of the same problem at other times or on other systems. Thus, the cause of a problem may be identified by comparing a trace of events preceding occurrence of the problem with previously diagnosed traces. Traces of events preceding occurrences of a problem arising from a known cause are reduced to a series of descriptive elements. These elements are aligned to correlate differently timed but otherwise similar traces of events, converted into symbolic representations, and archived. A trace of events leading to an undiagnosed a problem similarly is converted to a symbolic representation. The representation of the undiagnosed trace is then compared to the archived representations to identify a similar archived representation. The cause of the similar archived representation is presented as a diagnosis of the problem.

Abstract: The described subject matter provides systems and procedures to make query similarity determinations, wherein the queries are used in information retrieval operations. A same document and/or multiple similar documents are identified that have been selected by a user in response to multiple queries. Responsive to identifying the same document and/or the similar documents, a query cluster is generated that indicates that the queries used to obtain the same and/or similar documents. This is accomplished in a manner that is independent of whether individual ones of the queries are compositionally similar with respect to other ones of the queries.

Abstract: A search system generates an index for databases by generatively sampling the databases and uses that index to identify and formulate queries for searching the databases. The generated index is referred to as a domain-attribute index and contains a domain-level index and site-level indexes. A site-level index for a database maps site attributes to distinct attribute values within the database. The domain-level index for a domain maps attribute values to database and site attribute pairs that contain those attribute values. To generate a site-level index for a database within a certain domain, the search system starts out with an initial set of the sample data for that domain. The search system generates sampling queries based on the sample data and submits the sampling queries to a database. The search system updates the site-level index based on the sampling results and uses the results to generate more sampling queries.

Abstract: A method and system for identifying object information of an information page is provided. An information extraction system identifies the object blocks of an information page. The extraction system classifies the object blocks into object types. Each object type has associated attributes that define a schema for the information of the object type. The extraction system identifies object elements within an object block that may represent an attribute value for the object. After the object elements are identified, the extraction system attempts to identify which object elements correspond to which attributes of the object type in a process referred to as “labeling.” The extraction system uses an algorithm to determine the confidence that a certain object element corresponds to a certain attribute. The extraction system then selects the set of labels with the highest confidence as being the labels for the object elements.