Abstract: A method, system and computer program product for assessing similarity between two job description documents. Job description documents consist of sentences framed in a particular manner, where the sentences are represented as a set of actions, an object corresponding to each action and a set of attributes corresponding to the object. The two job description documents are parsed to generate a first and a second set of an action-object-attribute triplet representation, where the first set of the action-object-attribute triplet representation is associated with the first job description document and the second set of the action-object-attribute triplet representation is associated with the second job description document. A similarity score between the first and second sets of action-object-attribute triplet representations is then calculated by hierarchically matching the first and second sets of action-object-attribute triplet representations across the job description documents.

Abstract: A method, system and computer program product for assessing similarity between two job description documents. Job description documents consist of sentences framed in a particular manner, where the sentences are represented as a set of actions, an object corresponding to each action and a set of attributes corresponding to the object. The two job description documents are parsed to generate a first and a second set of an action-object-attribute triplet representation, where the first set of the action-object-attribute triplet representation is associated with the first job description document and the second set of the action-object-attribute triplet representation is associated with the second job description document. A similarity score between the first and second sets of action-object-attribute triplet representations is then calculated by hierarchically matching the first and second sets of action-object-attribute triplet representations across the job description documents.

Abstract: A method, system and computer program product for assessing similarity between two job description documents. Job description documents consist of sentences framed in a particular manner, where the sentences are represented as a set of actions, an object corresponding to each action and a set of attributes corresponding to the object. The two job description documents are parsed to generate a first and a second set of an action-object-attribute triplet representation, where the first set of the action-object-attribute triplet representation is associated with the first job description document and the second set of the action-object-attribute triplet representation is associated with the second job description document. A similarity score between the first and second sets of action-object-attribute triplet representations is then calculated by hierarchically matching the first and second sets of action-object-attribute triplet representations across the job description documents.

Abstract: Matching resources to an opportunity in a customer relationship management (CRM) system includes obtaining, from a CRM system, an opportunity, the opportunity representing a complex record structure in the CRM system, in which the opportunity captures a number of fields of metadata, determining, from the metadata, a number of opportunity attributes associated with the opportunity, determining a number of resource attributes for a number of resources, ranking the resource attributes with the opportunity attributes to determine a score for each of the resources, and presenting, based on the score, a list of the resources that are recommended for the opportunity.

Abstract: Matching resources to an opportunity in a customer relationship management (CRM) system includes obtaining, from a CRM system, an opportunity, the opportunity representing a complex record structure in the CRM system, in which the opportunity captures a number of fields of metadata, determining, from the metadata, a number of opportunity attributes associated with the opportunity, determining a number of resource attributes for a number of resources, ranking the resource attributes with the opportunity attributes to determine a score for each of the resources, and presenting, based on the score, a list of the resources that are recommended for the opportunity.

Abstract: In some embodiments, a method comprises receiving a tag cloud including tags that hyperlink to web content. The method can also comprise separating the tags into different linguistic categories, assigning a weight to each tag, and grouping the tags into clusters, wherein tags in a cluster are associated with a context. The method can also include determining one or more domains for the tag clusters, wherein a domain is a broadest class that defines one or more of the tags in a linguistic category, determining a hierarchy for the tags based on the weights of the tags, and identifying linguistic relationships between the tags. The method can also comprise determining properties associated with one or more of the tags and one or more of the domains, wherein the tag's properties are determined using linguistic analysis and storing the tags, the hierarchies, the linguistic relationships, and the properties.

Abstract: In some embodiments, a method comprises receiving a tag cloud including tags that hyperlink to web content. The method can also comprise separating the tags into different linguistic categories, assigning a weight to each tag, and grouping the tags into clusters, wherein tags in a cluster are associated with a context. The method can also include determining one or more domains for the tag clusters, wherein a domain is a broadest class that defines one or more of the tags in a linguistic category, determining a hierarchy for the tags based on the weights of the tags, and identifying linguistic relationships between the tags. The method can also comprise determining properties associated with one or more of the tags and one or more of the domains, wherein the tag's properties are determined using linguistic analysis and storing the tags, the hierarchies, the linguistic relationships, and the properties.