Discovering relevant concept and context for content node
First Claim
1. A computerized method comprising:
- extracting, by a candidate concept extractor, one or more concept candidates from a content node with a computer based at least in part on;
one or more statistical measures, andmatching concepts in a concept association map against text in the content node, the concept association map representing concepts, concept metadata, and relationships between the concepts, wherein such a map is dynamic and constantly updated, wherein the results on the concept association map are clustered, wherein the content nodes are tagged with labels representing at least one high level category and further wherein the concept association map is augmented by adding links between at least one of a search query and the concepts;
ranking, by a concept filterer, the one or more concept candidates to create a ranked one or more concept candidates based at least in part on a measure of relevance with the computer after extracting the one or more concepts in the content node;
expanding, by a concept expander, the ranked one or more concept candidates according to one or more cost functions, the expanding creating an expanded set of concepts with the computer after ranking the one or more concept candidates; and
storing, in a memory, the expanded set of concepts in association with the content node after expanding the ranked one or more concept candidates.
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Accused Products
Abstract
Discovering relevant concepts and context for content nodes to determine a user'"'"'s intent includes identifying one or more concept candidates in a content node based at least in part on one or more statistical measures, and matching concepts in a concept association map against text in the content node. The concept association map represents concepts, concept metadata, and relationships between the concepts. The one or more concept candidates are ranked to create a ranked one or more concept candidates based at least in part on a measure of relevance. The ranked one or more concept candidates is expanded according to one or more cost functions. The expanded set of concepts is stored in association with the content node.
159 Citations
22 Claims
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1. A computerized method comprising:
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extracting, by a candidate concept extractor, one or more concept candidates from a content node with a computer based at least in part on; one or more statistical measures, and matching concepts in a concept association map against text in the content node, the concept association map representing concepts, concept metadata, and relationships between the concepts, wherein such a map is dynamic and constantly updated, wherein the results on the concept association map are clustered, wherein the content nodes are tagged with labels representing at least one high level category and further wherein the concept association map is augmented by adding links between at least one of a search query and the concepts; ranking, by a concept filterer, the one or more concept candidates to create a ranked one or more concept candidates based at least in part on a measure of relevance with the computer after extracting the one or more concepts in the content node; expanding, by a concept expander, the ranked one or more concept candidates according to one or more cost functions, the expanding creating an expanded set of concepts with the computer after ranking the one or more concept candidates; and storing, in a memory, the expanded set of concepts in association with the content node after expanding the ranked one or more concept candidates. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. An apparatus comprising:
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memory comprising a concept association map representing concepts, concept metadata, and relationships between the concepts, wherein such a map is dynamic and constantly updated, wherein the results on the concept association map are clustered, wherein the content nodes are tagged with labels representing at least one high level category and further wherein the concept association map is augmented by adding links between at least one of a search query and the concepts; and a processor comprising; a candidate concept extractor configured to extract one or more concept candidates from a content node based at least in part on; one or more statistical measures, and matching concepts in a concept association map against text in the content node; a concept filterer configured to rank the one or more concept candidates to create a ranked one or more concept candidates based at least in part on a measure of relevance after the candidate concept extractor extracts the one or more concept candidates in the content node; and a concept expander configured to expand the ranked one or more concept candidates according to one or more cost functions, the expanding creating an expanded set of concepts after the concept filterer ranks the one or more concept candidates, wherein the apparatus is further configured to store the expanded set of concepts in association with the content node in the memory. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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Specification