Entity Augmentation Service from Latent Relational Data
First Claim
1. In a computing environment, a method performed at least in part on at least one processor, comprising, processing an augmentation task, including accessing relationship-based data corresponding to relationships, including at least one indirect relationship, between entities and attributes mined from at least one corpus, and using the relationship-based data to find data that completes the augmentation task.
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Abstract
The subject disclosure is directed towards providing data for augmenting an entity-attribute-related task. Pre-processing is preformed on entity-attribute tables extracted from the web, e.g., to provide indexes that are accessible to find data that completes augmentation tasks. The indexes are based on both direct mappings and indirect mappings between tables. Example augmentation tasks include queries for augmented data based on an attribute name or examples, or finding synonyms for augmentation. An online query is efficiently processed by accessing the indexes to return augmented data related to the task.
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Citations
21 Claims
- 1. In a computing environment, a method performed at least in part on at least one processor, comprising, processing an augmentation task, including accessing relationship-based data corresponding to relationships, including at least one indirect relationship, between entities and attributes mined from at least one corpus, and using the relationship-based data to find data that completes the augmentation task.
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15. A system comprising, a service configured to process domain-independent entity augmentation tasks, including to preprocess relational tables obtained from at least one corpus into a plurality of indexes, in which the indexes include data based upon indirect mappings between at least some of the tables, and to respond to a query corresponding to an entity augmentation task with data obtained via the indexes, including to identify seed tables via the index, compute scores for each seed table, compute a preference vector based upon vectors of the seed tables, compute prediction scores based upon the preference vectors and vectors associated with at least some of the relational tables, aggregate the scores, and return a final prediction based upon aggregation of the scores to complete an entity augmentation task to respond to the query.
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19. One or more computer-readable media having computer-executable instructions, which when executed perform steps, comprising:
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pre-processing entity attribute relation tables extracted from a corpus into indexes used for entity augmentation, including performing holistic matching between tables that includes computing values for direct relationships and indirect relationships between at least some of the tables; and accessing the indexes to process an entity augmentation task.
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Specification