Abstract: A method comprises obtaining a hypernym tree for a word selected from each skill name of a plurality of skill names, the obtained hypernym tree including a node for each meaning of the word, each node of an obtained hypernym tree for a meaning of a word including one or more synonyms corresponding to the meaning and a summary description of the meaning, at least one hypernym tree including nodes for meanings of different words; creating an embedding for each skill name and for each synonym and the summary description included in a node of each obtained hypernym tree computing, a distance between the embedding for a skill name and the embedding for each synonym and summary description included in a node of an obtained hypernym tree; assigning to the skill name a meaning of a word associated with a node of any obtained hypernym tree that leads to a lowest distance.

Abstract: A system for document analysis and generation based on graph structures is disclosed. The system is programmed to maintain graph structures for skill names and skill-related categories. The system is further programmed to train one or more machine learning (ML) models based on skill-related documents as training data and the graph structures. The ML models recognize entities in the training data that fall into the skill-related categories as named entities, identify pairs of named entities likely to correspond to skill names and corresponding skill levels, and inferring an overall skill level for each group of related skill names. In addition, the system is programmed to transform an input document into a skill assessment using the trained one or more ML models and access or update specific documents that include specific skills and skill levels.

Abstract: A method comprises obtaining a hypernym tree for a word selected from each skill name of a plurality of skill names, the obtained hypernym tree including a node for each meaning of the word, each node of an obtained hypernym tree for a meaning of a word including one or more synonyms corresponding to the meaning and a summary description of the meaning, at least one hypernym tree including nodes for meanings of different words; creating an embedding for each skill name and for each synonym and the summary description included in a node of each obtained hypernym tree computing, a distance between the embedding for a skill name and the embedding for each synonym and summary description included in a node of an obtained hypernym tree; assigning to the skill name a meaning of a word associated with a node of any obtained hypernym tree that leads to a lowest distance.

Abstract: Techniques are described for automatically generating job descriptions. Training data is used to define job categories, job sections, and selective phrases for each job category and job section. In response to receiving request to generate a job description, a job category or additional information is derived from the request. Such information is used to populate existing templates for a job description based on a skill knowledge graph, which connects skill names based on hypernym trees and enables enhancement of a job description with better or broader coverage.

Abstract: A method comprises obtaining a hypernym tree for a word selected from each skill name of a plurality of skill names, the obtained hypernym tree including a node for each meaning of the word, each node of an obtained hypernym tree for a meaning of a word including one or more synonyms corresponding to the meaning and a summary description of the meaning, at least one hypernym tree including nodes for meanings of different words; creating an embedding for each skill name and for each synonym and the summary description included in a node of each obtained hypernym tree computing, a distance between the embedding for a skill name and the embedding for each synonym and summary description included in a node of an obtained hypernym tree; assigning to the skill name a meaning of a word associated with a node of any obtained hypernym tree that leads to a lowest distance.

Abstract: A data management server computer (“server”) and related methods are disclosed to enhance electronic documents or search processes related to skill data, including skill names, using machine learning techniques. The server is programmed to organize skill names from a dictionary into a knowledge graph. The knowledge graph includes nodes that represent the skill names and words of the skill names and edges that represent syntactic or semantic relationships. The server is programmed to respond to skill-related requests using the knowledge graph and track access patterns of the knowledge graph in responding to the requests. The replies to the request can be generated based on the structure or access patterns of the knowledge graph and used as search results or skill-related data, such as resumes or job descriptions.

Abstract: Techniques are described for automatically generating job descriptions. Training data is used to define job categories, job sections, and selective phrases for each job category and job section. In response to receiving request to generate a job description, a job category or additional information is derived from the request. Such information is used to populate existing templates for a job description based on a skill knowledge graph, which connects skill names based on hypernym trees and enables enhancement of a job description with better or broader coverage.

Abstract: A data management server computer (“server”) and related methods are disclosed to enhance electronic documents or search processes related to skill data, including skill names, using machine learning techniques. The server is programmed to organize skill names from a dictionary into a knowledge graph. The knowledge graph includes nodes that represent the skill names and words of the skill names and edges that represent syntactic or semantic relationships. The server is programmed to respond to skill-related requests using the knowledge graph and track access patterns of the knowledge graph in responding to the requests. The replies to the request can be generated based on the structure or access patterns of the knowledge graph and used as search results or skill-related data, such as resumes or job descriptions.

Abstract: Techniques are described for automatically generating job descriptions. Job categories are identified for the documents in the job description dataset based on clustering the documents. Sections within each document in the job description dataset are automatically identified, and each identified section is automatically classified. Each type of section is analyzed independently of the other section types using natural language processing, which allows automatic generation of each section of a new job description based on the section-specific data derived from the dataset. Section- and category-specific selective phrases are identified within the job description dataset, and embeddings are generated for the identified selective phrases. A request to generate a new job description for a particular category is received.

Abstract: A data management server computer (“server”) and related methods are disclosed to enhance electronic documents or search processes related to skill data, including skill names, using machine learning techniques. The server is programmed to organize skill names from a dictionary into a knowledge graph. The knowledge graph includes nodes that represent the skill names and words of the skill names and edges that represent syntactic or semantic relationships. The server is programmed to respond to skill-related requests using the knowledge graph and track access patterns of the knowledge graph in responding to the requests. The replies to the request can be generated based on the structure or access patterns of the knowledge graph and used as search results or skill-related data, such as resumes or job descriptions.

Abstract: A system operation management server computer (server) and related methods are disclosed. The server is programmed to learn the features of and relations among computer devices from various types of data related to the computer devices and build a knowledge graph (KG) to represent IT infrastructure. The server is also programmed to manage a collection of issue resolution rules each mapping the states of certain computer devices that characterize a known issue and a known resolution of the known issue. In response to receiving a support bundle that contains data related to a target computer system that has encountered an unknown issue the server is programmed to determine which issue resolution rules are applicable to the support bundle based on the KG and transmit a recommendation for resolving the unknown issue.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to provide efficient game access is described. A request to access a first game using a first primary identifier is received. An internal identifier and a game identifier associated with the first primary identifier are accessed. A request to access a second game using a second primary identifier is received. An internal identifier and a game identifier associated with the second primary identifier are accessed. A determination that the first and second primary identifiers are associated with a particular player is made. An association between the internal identifiers is stored.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to provide efficient game access is described. A request to access a first game using a first primary identifier is received. An internal identifier and a game identifier associated with the first primary identifier are accessed. A request to access a second game using a second primary identifier is received. An internal identifier and a game identifier associated with the second primary identifier are accessed. A determination that the first and second primary identifiers are associated with a particular player is made. An association between the internal identifiers is stored.

Abstract: A system operation management server computer (server), agent computer (agent), and related methods are disclosed to configure and manage the operation of systems in a system category. In some embodiments, the server and a plurality of agents are programmed to collaborate on exploring and improving the operation of systems in a system category through reinforcement learning.

Abstract: A system operation management server computer (server) and related methods are disclosed. The server is programmed to learn the features of and relations among computer devices from various types of data related to the computer devices and build a knowledge graph (KG) to represent IT infrastructure. The server is also programmed to manage a collection of issue resolution rules each mapping the states of certain computer devices that characterize a known issue and a known resolution of the known issue. In response to receiving a support bundle that contains data related to a target computer system that has encountered an unknown issue the server is programmed to determine which issue resolution rules are applicable to the support bundle based on the KG and transmit a recommendation for resolving the unknown issue.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to provide efficient game access is described. A request to access a first game using a first primary identifier is received. An internal identifier and a game identifier associated with the first primary identifier are accessed. A request to access a second game using a second primary identifier is received. An internal identifier and a game identifier associated with the second primary identifier are accessed. A determination that the first and second primary identifiers are associated with a particular player is made. An association between the internal identifiers is stored.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to provide efficient game access is described. A request to access a first game using a first primary identifier is received. An internal identifier and a game identifier associated with the first primary identifier are accessed. A request to access a second game using a second primary identifier is received. An internal identifier and a game identifier associated with the second primary identifier are accessed. A determination that the first and second primary identifiers are associated with a particular player is made. An association between the internal identifiers is stored.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to provide efficient game access is described. A request to access a first game using a first primary identifier is received. An internal identifier and a game identifier associated with the first primary identifier are accessed. A request to access a second game using a second primary identifier is received. An internal identifier and a game identifier associated with the second primary identifier are accessed. A determination that the first and second primary identifiers are associated with a particular player is made. An association between the internal identifiers is stored.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to provide efficient game access is described. A request to access a first game using a first primary identifier is received. An internal identifier and a game identifier associated with the first primary identifier are accessed. A request to access a second game using a second primary identifier is received. An internal identifier and a game identifier associated with the second primary identifier are accessed. A determination that the first and second primary identifiers are associated with a particular player is made. An association between the internal identifiers is stored.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to provide efficient game access is described. A request to access a first game using a first primary identifier is received. An internal identifier and a game identifier associated with the first primary identifier are accessed. A request to access a second game using a second primary identifier is received. An internal identifier and a game identifier associated with the second primary identifier are accessed. A determination that the first and second primary identifiers are associated with a particular player is made. An association between the internal identifiers is stored.