The invention is a method for textual searching of text-based databases including databases of compiled internet content, scientific literature, abstracts for books and articles, newspapers, journals, and the like. Specifically, the algorithm supports searches using full-text or webpage as query and keyword searches allowing multiple entries and an information-content based ranking system (Shannon Information score) that uses p-values to represent the likelihood that a hit is due to random matches. Additionally, users can specify the parameters that determine hits and their ranking with scoring based on phrase matches and sentence similarities.

Full text query and search systems and methods of use

Methods and apparatus for a new approach to the problem of finding communities in complex networks relating to a social definition of communities and percolation are disclosed. Instead of partitioning the graph into separate subgraphs from top to bottom a local algorithm (communities of each vertex) allows overlapping of communities. The performance of an algorithm on synthetic, randomly-generated graphs and real-world networks is used to benchmark this method against others. An heuristic is provided to generate a list of communities for networks using a local community finding algorithm. Unlike diffusion based algorithms, The provided algorithm finds overlapping communities and provides a means to measure confidence in community structure. It features locality and low complexity for exploring the communities for a subset of network nodes, without the need for exploring the whole graph.

Method and apparatus for distributed community finding

A system and method for collecting and analyzing electronic discussion messages to categorize the message communications and the identify trends and patterns in pre-determined markets. The system comprises an electronic data discussion system wherein electronic messages are collected and analyzed according to characteristics and data inherent in the messages. The system further comprises a data store for storing the message information and results of any analyses performed. Objective data is collected by the system for use in analyzing the electronic discussion data against real-world events to facilitate trend analysis and event forecasting based on the volume, nature and content of messages posted to electronic discussion forums.

System and method for scoring electronic messages

In one embodiment, a method includes maintaining access to information comprising nodes and edges; receiving a request from a first user corresponding to a first user node for a structured document corresponding to a first concept node; determining a first data set that identifies concept nodes connected by edges with user nodes that are each connected by edges with both the first user node and the first concept node; determining a second data set that identifies concept nodes connected by edges with the first concept node and user nodes that are each connected to the first user node; generating a score for each concept node in the data sets; selecting one or more concept nodes based on their scores as recommended nodes; and transmitting to the client device the structured document and code executable by a client application to render node names or identifiers of the recommended nodes for display.

Dynamically generating recommendations based on social graph information

In one embodiment, a method includes maintaining a data store of nodes and edges and for each of one or more users: scanning items of content associated with the corresponding user node; identifying a candidate item of content; searching for matches between the candidate item of content and existing nodes; determining whether or not a match between the candidate item of content and an existing node exists; and when it is determined that at least one match exists, generating an edge from the user node to the existing node for which the best match is determined; and when it is determined that no match exists, generating a new node based on the candidate item of content, and generating an edge from the user node to the new node.

Automatically Generating Nodes and Edges in an Integrated Social Graph

Systems and method for enhancing search functionality provided to a user. In certain aspects, a query processing engine automatically decomposes queries into constituent units that are related to concepts in which a user may be interested. The query processing engine decomposes queries into one or more constituent units per query using statistical methods. In certain aspects, no real world knowledge is used in determining units. In other aspects, aspects of world and content knowledge are introduced to enhance and optimize performance, for example, manually using a team of one or more information engineers.

Systems and methods for generating concept units from search queries

Search results are processed using search requests, including analyzing received queries in order to provide a more sophisticated understanding of the information being sought. A concept network is generated from a set of queries by parsing the queries into units and defining various relationships between the units. From these concept networks, queries can be automatically categorized into categories, or more generally, can be associated with one or more nodes of a taxonomy. The categorization can be used to alter the search results or the presentation of the results to the user. As an example of alterations of search results or presentation, the presentation might include a list of “suggestions” for related search query terms. As other examples, the corpus searched might vary depending on the category or the ordering or selection of the results to present to the user might vary depending on the category. Categorization might be done using a learned set of query-node pairs where a pair maps a particular query to a particular node in the taxonomy. The learned set might be initialized from a manual indication of which queries go with which nodes and enhanced has more searches are performed. One method of enhancement involves tracking post-query click activity to identify how a category estimate of a query might have varied from an actual category for the query as evidenced by the category of the post-query click activity, e.g., a particular hits of the search results that the user selected following the query. Another method involved determining relationships between units in the form of clusters and using clustering to modify the query-node pairs.

Search processing with automatic categorization of queries

A system is described for discovering query intent based on search queries and concept networks. The system may construct frequency vectors from log data corresponding to a submitted query and at least one related query submitted to one or more search engines. The system may also construct a query intent vector based on the frequency vectors. The query intent vector may include frequency scores that represent the intent of the query.

Discovering query intent from search queries and concept networks

In a search processing system, a concept network (408) is generated from a set of queries by parsing the queries into units and defining various relationships between the units based in part on patterns of units that appear together in queries. Units in the concept network (408) that have some similar characteristic(s) are grouped into superunits (414). For each superunit, there is a corresponding signature that defines the similar characteristic of the group. A query is processed (404) by identifying constituent units, determining the superunit membership of some or all of the constituent units , and using that information to formulate a response to the query.

Systems and methods for search processing using superunits

Multiple concept networks are generated from subsets of received queries. These concept networks can be used in various ways to enhance response to subsequent queries. In one embodiment, concept networks can be merged into a larger concept network that can be used to infer a user's likely intent given a query. In another embodiment, suggestions for related searches obtained using different concept networks can be merged or aggregated. Other users for concept networks in query processing, including assisted search, are also described.

Deepa Joshi

Papers

Systems and methods for generating concept units from search queries

Search processing with automatic categorization of queries

Discovering query intent from search queries and concept networks

Systems and methods for search processing using superunits

Systems and methods for managing and using multiple concept networks for assisted search processing