33 results about "Concept network" patented technology

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.

In a search processing system, a concept network 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 that have some similar characteristic(s) are grouped into superunits. For each superunit, there is a corresponding signature that defines the similar characteristic of the group. A query is processed 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.

Information retrieval systems face challenging problems with delivering highly relevant and highly inclusive search results in response to a user's query. Contextual personalized information retrieval uses a set of integrated methodologies that can combine automatic concept extraction / matching from text, a powerful fuzzy search engine, and a collaborative user preference learning engine to provide accurate and personalized search results. The system can include constructing a search query to execute a search of a database. The system can parse an input query from a user conducting the search of the database into sub-strings, and can match the sub-strings to concepts in a semantic concept network of a knowledge base. The system can further map the matched concepts to criteria and criteria values that specify a set of constraints on and scoring parameters for the matched concepts.

Techniques are provided for reranking and increasing the relevance of the results Internet searches. A search query is initially parsed into individual units. Each unit corresponds to one or more words that represents a natural concept. A concept network is analyzed to locate concepts that are related to the units in the search query. Particular concepts are selected from the concept network. Independent Internet searches are performed for each of the selected concepts. The search results from these searches are then compared to units in the original search query and ranked according to their relevance to the original search query.

A concept network that can be generated in response to a user query. Various embodiments include analysis of structure information, for example, where such information is based at least in part on Universal Resource Locators (URLs) of Web sites or data storage locations. A concept network may be used with a search tool where the search tool searches a plurality of sites (e.g., Web sites, data storage locations, etc.). In such an example, each site location is arranged with a node. Certain ones of the nodes are connected by at least one link. The concept network selects a portion of certain ones of the nodes based on the link, wherein the at least one link is used for content purposes.

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.

In a computerized search system, a document corpus is searched according to a query and results of the search are presentable as an ordered search result set comprising one or more search hits, a search hit being a document of the document corpus or reference thereto. Based on content of the search result set, a number of suggested queries and / or parameter options are presented to a querier, thereby allowing the querier to select a suggested query and / or provide values for the parameters and those selections can be used to reorder the hits of the search result set. The particular suggested queries and / or parameters presented depend on the page elements of the search result set's documents and their categorization. The categorization might be done using concept networks.

The invention discloses a visual question-answering problem-solving method based on a complex network analysis method, including semantic concept network construction, non-random depth walk, image and text feature fusion, and a classifier. The semantic concept network construction aims to mine the co-occurrence of concepts Mode to enhance semantic expression, non-random deep walk realizes the mapping of complex network relations to low-dimensional features, on the basis of constructing image semantic concept network, apply deep walk algorithm to learn the potential relationship of nodes in semantic concept network, and combine complex The nodes in the network are mapped into a low-dimensional feature vector, and multinomial logistic regression fuses image and text features to solve visual question answering problems. The invention digs deep into the conceptual co-occurrence mode and the hierarchical structure of the cluster concept, effectively integrates the visual and semantic features of the image, and the natural language features, and provides a feasible way to solve the visual question answering problem.

Described is a system for understanding machine-learning decisions. In an unsupervised learning phase, the system extracts, from input data, concepts represented by a machine-learning (ML) model in an unsupervised manner by clustering patterns of activity of latent variables of the concepts, where the latent variables are hidden variables of the ML model. The extracted concepts are organized into a concept network by learning functional semantics among the extracted concepts. In an operational phase, a subnetwork of the concept network is generated. Nodes of the subnetwork are displayed as a set of visual images that are annotated by weights and labels, and the ML model per the weights and labels.