This disclosure relates to systems, methods, and computer readable media for performing multi-format, multi-protocol message threading in a way that is most beneficial for the individual user. Users desire a system that will provide for ease of message threading by stitching together related communications in a manner that is seamless from the user's perspective. Such stitching together of communications across multiple formats and protocols may occur, e.g., by: 1) direct user action in a centralized communications application (e.g., by a user clicking Reply on a particular message); 2) using semantic matching (or other search-style message association techniques); 3) element-matching (e.g., matching on subject lines or senders/recipients/similar quoted text, etc.); and 4) state-matching (e.g., associating messages if they are specifically tagged as being related to another message, sender, etc. by a third-party service, e.g., a webmail provider or Instant Messaging (IM) service).

In an approach for semantically translating data. Aspects of an embodiment of the present invention include an approach for semantically translating data, wherein the approach includes a processor selecting a first node. A processor identifies a parent node of the first node. A processor determines that a value of the first node is unknown. A processor responsive to determining that the value of the first node is unknown, annotates the first node to indicate that the first node is at least partially unknown. A processor identifies a common table expression of the first node. A processor determines that the common table expression of the first node matches, within a predetermined threshold, a common table expression of the second node. A processor merges information from the common table expression of the second node with the common table expression of the first node.

In an approach for semantically translating data. Aspects of an embodiment of the present invention include an approach for semantically translating data, wherein the approach includes a processor selecting a first node. A processor identifies a parent node of the first node. A processor determines that a value of the first node is unknown. A processor responsive to determining that the value of the first node is unknown, annotates the first node to indicate that the first node is at least partially unknown. A processor identifies a common table expression of the first node. A processor determines that the common table expression of the first node matches, within a predetermined threshold, a common table expression of the second node. A processor merges information from the common table expression of the second node with the common table expression of the first node.

A method of automated domain knowledge structure generation includes crawling learning materials. The method may include extracting structural information from the learning materials. The method may include extracting knowledge points from the learning materials. The method may include inferring dependency relationships between the knowledge points. The method may include aligning one or more of the knowledge points with one or more of the learning materials. The method may also include generating a domain knowledge structure. The domain knowledge structure may include the extracted knowledge points organized at least partially according to the inferred hierarchy and dependency relationships. The extracted knowledge points may include the aligned learning materials.

Embodiments are directed towards previewing results generated from indexing data raw data before the corresponding index data is added to an index store. Raw data may be received from a preview data source. After an initial set of configuration information may be established, the preview data may be submitted to an index processing pipeline. A previewing application may generate preview results based on the preview index data and the configuration information. The preview results may enable previewing how the data is being processed by the indexing application. If the preview results are not acceptable, the configuration information may be modified. The preview application enables modification of the configuration information until the generated preview results may be acceptable. If the configuration information is acceptable, the preview data may be processed and indexed in one or more index stores.

Disclosed herein are systems and methods for populating a table having null values using a predictive query interface including means for receiving a tabular dataset from a user as input, the tabular dataset having data values organized as columns and rows; identifying a plurality of null values within the tabular dataset, the null values being dispersed across multiple rows and multiple columns of the tabular dataset; generating indices from the tabular dataset of columns and rows, the indices representing probabilistic relationships between the rows and the columns of the tabular dataset; displaying the tabular dataset as output to the user, the displayed output including the data values depicted as known values and the null values depicted as unknown values; receiving input from the user to populate at least a portion of the unknown values within the displayed tabular dataset with predicted values; querying the indices for the predicted values; and displaying the predicted values as updated output to the user. Other related embodiments are further disclosed.

A system and method for indexing mobile applications. The method includes crawling through a plurality of data sources to detect applications accessible through a user device; for each detected application, generating metadata characterizing the application; analyzing the generated metadata to classify each detected application to at least one category; and updating an application index to include at least the classified applications and the respective classified categories.

Methods and systems to build and utilize a search infrastructure are described. The system generates index information components in real-time based on a database that is time-stamped. The system updates index information at a plurality of query node servers based on the index information components. A query engine receives a search query from a client machine and identifies search results based on the query and the index information. The system communicates the search results, over the network, to the client machine.

A system for the categorization of interlinked information items, the system comprising: a trust flow module which is configured to receive a seed trust list of one or more first information items, the seed trust list associating the one or more first information items with one or more categories; and a trust flow module configured to: associate a respective trust value with each of the one or more categories for the one or more first information items; and iteratively pass at least part of the or each trust value to one or more further information items to generate, for each of the one or more further information items, at least one accumulated trust value associated with a category of the one or more categories, such that the one or more further information items can be categorized based on the at least one accumulated trust value and associated category.

In an approach to automating multilingual indexing, a computer receives text of a conversation between at least two users. The computer detects at least one language associated with the text. The computer determines whether the language associated with the text is detected with a confidence level that exceeds a threshold. The computer retrieves text from one or more previous conversations between the two users. The computer detects at least one language associated with the text. The computer determines whether the at least one language associated with the text is detected with a confidence level that exceeds a pre-defined threshold. The computer analyzes the text using at least one of the detected languages to create one or more terms. The computer indexes the one or more terms and stores a boost value associated with each of the one or more indexed terms corresponding to confidence level of the detected language.