A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computing system, comprising: a record tokenizer that receives first and second records, each record including an item identifier that identifies an item that is a subject of the record and a set of attributes, the tokenizer normalizing the first and second records to obtain corresponding first and second entity tokens, each entity token representing the item identifier and attributes in the corresponding record in a normalized form; and a record matcher that compares the first entity token to the second entity token to identify a similarity measure and provide a match result indicative of whether the first and second records resolve to a same item as their subject, based on the similarity measure. 2. The computing system of claim 1 and further comprising: a record update component that receives the match result and updates a record of items based on the match result. 3. The computing system of claim 2 wherein the update component is configured to, in response to the match result indicating that the first and second records resolve to the same item, aggregate a superset of the attributes in the first and second records and update the same item in the record of items based on the superset of attributes. 4. The computing system of claim 3 wherein the record matcher accesses a set of previously learned resolutions to identify whether a previously learned resolution indicates that the first and second entity tokens resolve to the same item. 5. The computing system of claim 4 and further comprising: a supervised machine learning system that provides the set of previously learned resolutions. 6. The computing system of claim 5 wherein the record matcher is configured to provide the match result to the supervised machine learning system to update the previously learned resolutions. 7. The computing system of claim 1 wherein the record matcher is configured to select the first and second records from a record block and, in response to the match result indicating that the first and second records do not resolve to the same item, access a wide area network search engine to launch a wide area network search, using the first entity token as a search input, and obtain search results, the search results being added to the record block as records to be tokenized by the tokenizer and compared using the record matcher. 8. The computing system of claim 7 and further comprising: a partition component that receives an input record set and that partitions the input record set into blocks based on partitioning criteria. 9. The computing system of claim 8 wherein the partition component partitions the input record set into the blocks based on geographic location information contained in each record in the input record set. 10. The computing system of claim 7 wherein the record matcher comprises: a vector generator that generates a similarity vector with vector values corresponding to each attribute in the normalized form, the vector values being indicative of whether the corresponding attributes in the first and second entity tokens match one another, the similarity measure being based on the vector values. 11. The computing system of claim 10 wherein the record matcher comprises: a threshold component that identifies whether the similarity measure meets a threshold value and, if so, provides the match result to indicate that the first and second records resolve to the same item. 12. The computing system of claim 11 wherein the record matcher comprises: a weighting component that identifies weights for each vector value and generates the similarity measure based on a weighted combination of the vector values. 13. The computing system of claim 8 and further comprising: a plurality of different business subsystems, each having a record set, the record sets from the plurality of different business subsystems comprising the input record set. 14. A method, comprising: obtaining an input record set from a plurality of different data sources, the record set including a plurality of different records, each record including an item identifier that identifies an item that is described by the record and a set of attributes that relate to the item, the records being in different forms; normalizing the plurality of different records into corresponding normalized forms; comparing the normalized forms to generate a match result indicative of whether the plurality of different records describe a same item; updating a record store based on the match result. 15. The method of claim 14 wherein comparing further comprises: accessing a set of previously learned matches, learned by a supervised machine learning system; determining whether the set of previously learned matches includes a match result for the plurality of different records corresponding to the normalized forms compared. 16. The method of claim 14 and further comprising: if the match result indicates that the plurality of different records do not describe the same item, then launching a web search using at least a part of at least one of the normalized forms; receiving search results; and adding at least some of the search results to the input record set for normalizing and comparing. 17. The method of claim 14 wherein obtaining the input record set comprises: obtaining the input record set from a plurality of different subsystems in a business system. 18. The method of claim 14 wherein comparing comprises: identifying a similarity of the attributes in the normalized forms corresponding to two of the records; generating a similarity vector having vector values corresponding to the attributes, the vector values being indicative of the similarity of the corresponding attributes; generating a similarity measure based on the vector values; and generating the match result based on the similarity measure. 19. An entity resolution system, comprising: a partition component that receives an input record set that includes records from a plurality of different data sources and partitions the input record set into blocks based on partition criteria, each record relating to an entity; and an entity matching component that selects first and second records from a given block, and outputs a match result indicative of whether the first and second records resolve to a same entity, wherein the entity matching component normalizes the records into corresponding normalized forms, determines whether previously learned resolutions are found for the normalized forms corresponding to the first and second records and, if not, compares the normalized forms to determine whether they meet a similarity threshold and, if not, uses at least a portion of at least one normalized form to generate a web search and obtain search results, the entity matching component adding at least some of the search results to the given block for later selection by the entity matching component. 20. The entity resolution system of claim 19 wherein the first and second records contain attributes, and further comprising: a record update component that updates an entity record with the attributes from the first and second records in response to the match result indicating that the first and second records resolve to the same entity.