International Business Machines Corporation

Ahmed M M R Salem

Daniel John McCloskey

Mikhail Sogrin

Seamus Ronan McAteer

Seamus R Mac An tSaoir

Seamus R Macan tSaoir

Several aspects are provided for dynamic typeahead suggestions for a text input. A first aspect receives a user input in the form of natural language text formed of a series of characters typed by a user. The first aspect provides a set of typeahead suggestions for the user input as the user input is typed or amended with the set of typeahead suggestions retrieved for a subtext within a current dynamic window formed of a start position and an end position in the user input independent of future user input. The first aspect generates multiple independent sets of typeahead suggestions for the user input, using different dynamic windows as the user input is received until confirmation of a final text input including any selections from the independent sets of typeahead suggestions.

Dynamic typeahead suggestions for a text input

A method and/or system for query expansion may include: providing a set of training data in a given domain in the form of training question texts and training answer texts, identifying disjoint answer words in the training answer text that do not occur in the associated training question text, generating a graph of question word nodes and answer word nodes generated from the set of training data for the given domain in the form of the training question texts and the training answer texts, wherein edges are provided between a disjoint pair of a question word node for a question word in a training question and an answer word node for a disjoint answer word in an associated training answer, and applying spreading activation through the graph to result in a top n most highly activated nodes that are used as candidate words for expansion of a user query input.

Query expansion using a graph of question and answer vocabulary

A method and system are provided for query expansion. The method may include: providing a graph of question and answer word nodes generated from a set of training data for a given domain in the form of training question and answer texts, wherein the answer word nodes are disjoint words that do not occur in both a training question and an associated training answer and wherein edges are provided between a disjoint pair of a training question word and an associated training disjoint answer word, and applying spreading activation through the graph to result in a top n most highly activated nodes that are used as candidate words for expansion of a user query input.

A method and system are provided for query expansion. The method may include: providing a graph of question and answer word nodes generated from a set of training data for a given domain in the form of training question and answer texts, wherein the answer word nodes are disjoint words that do not occur in both a training question and an associated training answer and wherein edges are provided between a disjoint pair of a training question word and an associated training disjoint answer word, including providing weightings for the nodes and edges based on frequency data; and receiving a user query input, activating input nodes in the graph for words in the user query input, and applying spreading activation through the graph using the weightings to result in a top n most highly activated nodes that are used as candidate words for expansion of the user query input.

A processor expands a search expression. The processor determines nodes representing query terms of a search expression. The nodes have associated text for search term expansion, and represent at least one concept in a semantic graph of nodes that represents a domain of semantically related concepts. The processor determines i) a center of focus within the semantic graph for the two or more nodes based, at least in part, on a spreading activation in the graph and ii) a contextual relevance for the two or more nodes with respect to node the center of focus. The processor selects, for a query term, a node based on contextual relevance between that node and the query term and expands the search expression using an associated text of that node.

Growing community of inventors

Dublin, Ireland

Ahmed M M R Salem