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1.发明公开公开(公告)号：US20230401461A1
公开(公告)日：2023-12-14
申请号：US18333200
申请日：2023-06-12
发明人： Christophe Gueret , Sumit Pai , Luca Costabello , Rory McGrath
IPC分类号： G06N5/025
CPC分类号： G06N5/025
摘要： This application relates generally to intelligent and explainable link prediction in knowledge graph systems that automatically incorporate user feedback. In one aspect, this application discloses an iterative process for predicting a link set as a group of links in a knowledge graph in an embedding space by expanding the knowledge graph with predicted and validated single links in each iteration such that a final set of links are predicted with each one being added to the set depending on previously added predicted links. In another aspect, this application also discloses automatically extracting rules from user feedback of link predictions and generating a user feedback knowledge graph from the extracted rules, which in combination with an original knowledge graph are used for the generation of the link predictions.
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公开(公告)号：US10949718B2
公开(公告)日：2021-03-16
申请号：US16406380
申请日：2019-05-08
发明人： Luca Costabello , Nicholas McCarthy , Rory McGrath , Sumit Pai
IPC分类号： G06K9/72 , G06F3/0484 , G06K9/62 , G06F40/30
摘要： The systems and methods described herein may generate multi-modal embeddings with sub-symbolic features and symbolic features. The sub-symbolic embeddings may be generated with computer vision processing. The symbolic features may include mathematical representations of image content, which are enriched with information from background knowledge sources. The system may aggregate the sub-symbolic and symbolic features using aggregation techniques such as concatenation, averaging, summing, and/or maxing. The multi-modal embeddings may be included in a multi-modal embedding model and trained via supervised learning. Once the multi-modal embeddings are trained, the system may generate inferences based on linear algebra operations involving the multi-modal embeddings that are relevant to an inference response to the natural language question and input image.
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公开(公告)号：US20220318651A1
公开(公告)日：2022-10-06
申请号：US17218308
申请日：2021-03-31
摘要： Implementations for selectively enabling override of an inference result provided by an artificial intelligence (AI) system can include receiving an input case, outputting a first inference result by processing the input case through a machine learning (ML) model, and determining that a confidence score associated with the first inference result fails to meet a threshold, and in response: providing an adapted ML model based on a set of additional cases, outputting a second inference result by processing a current case through the adapted ML model, the current case including the input case, and selectively transmitting instructions to display an override element with the first inference result in a user interface.
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公开(公告)号：US10157226B1
公开(公告)日：2018-12-18
申请号：US15872227
申请日：2018-01-16
发明人： Luca Costabello , Freddy Lecue
摘要： A device receives training data and an ontology for the training data, where the training data includes information associated with a subject of the ontology. The device generates a knowledge graph based on the training data and the ontology, and converts the knowledge graph into knowledge graph embeddings, where the knowledge graph embeddings include points in a k-dimensional metric space. The device receives a new entity that is not present in the knowledge graph embeddings, and generates a new embedding of the new entity. The device adds the new embedding to the knowledge graph embeddings, and utilizes the knowledge graph embeddings, with the new embedding, to perform an action.
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5.发明公开公开(公告)号：US20230401258A1
公开(公告)日：2023-12-14
申请号：US18333093
申请日：2023-06-12
发明人： Christophe Gueret , Sumit Pai , Luca Costabello , Rory McGrath
IPC分类号： G06F16/901
CPC分类号： G06F16/9024
摘要： This application relates generally to intelligent and explainable link prediction in knowledge graph systems that automatically incorporate user feedback. In one aspect, this application discloses an iterative process for predicting a link set as a group of links in a knowledge graph in an embedding space by expanding the knowledge graph with predicted and validated single links in each iteration such that a final set of links are predicted with each one being added to the set depending on previously added predicted links. In another aspect, this application also discloses automatically extracting rules from user feedback of link predictions and generating a user feedback knowledge graph from the extracted rules, which in combination with an original knowledge graph are used for the generation of the link predictions.
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公开(公告)号：US10803394B2
公开(公告)日：2020-10-13
申请号：US15923738
申请日：2018-03-16
IPC分类号： G06F16/25 , G06F16/901 , G06F16/9038 , G06F16/532 , G06F16/632 , G06F16/2452 , G06N5/04 , H04L29/08
摘要： A system for providing integrated monitoring and communications of diagnostic equipment is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive heterogeneous data from a plurality of machine and sensor equipment associated with performance of a system or product. The data access interface may also to receive a user inquiry pertaining to the system and product. The processor may generate a knowledge graph based on the data associated with the system or product, as well as convert the user inquiry into a knowledge graph query by: extracting entities from the user inquiry; extracting relations from the user inquiry to identify relationships between entities; expanding the user inquiry using the knowledge graph and the entities and relations; and translating the inquiry into knowledge graph triples. The processor may then identify relevant nodes and edges based on the knowledge graph query and the knowledge graph, and determine an answer to the user inquiry.
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公开(公告)号：US10262079B1
公开(公告)日：2019-04-16
申请号：US16141636
申请日：2018-09-25
发明人： Luca Costabello , Christophe Dominique Marie Gueret , Freddy Lecue , Jeremiah Hayes , Nicholas McCarthy
摘要： A device may receive individual information associated with individual activities of an individual, and may aggregate the individual information, based on a time period, to generate aggregated individual information. The device may identify patterns in the aggregated individual information, and may determine states for the patterns based on state information associated with activities capable of being performed by individuals. The device may generate a sequential knowledge graph based on modifying a knowledge graph with the states and adding a sequence of activities to the knowledge graph, and may determine embeddings for the individual activities based on the sequential knowledge graph. The device may determine anonymized activity signatures for the individual activities based on the embeddings, and may combine the anonymized activity signatures to generate a time-based anonymized activity signature for the individual, wherein the time-based anonymized activity signature providing information that may be utilized without divulging the individual information.
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公开(公告)号：US20240256917A1
公开(公告)日：2024-08-01
申请号：US18161260
申请日：2023-01-30
发明人： Eda Bayram , Luca Costabello
摘要： Methods, systems and apparatus, including computer programs encoded on computer storage medium, for training a neural link predictor. In one aspect a method includes obtaining triples that represent a knowledge graph, where each triple comprises specifies a subject and object node in the knowledge graph and a relation type between the subject node and object node; obtaining data that specifies entity types of nodes in the knowledge graph; and for each triple: retrieving, from an ontology lookup table, ontology embeddings for the knowledge graph, the ontology embeddings comprising embeddings for each entity type in the set of entity types, generating, using the retrieved ontology embeddings for the knowledge graph, node embeddings for the subject node and the object node included in the triple, scoring the triple using the generated node embeddings, and updating, using a loss of the scored triple, the ontology embeddings stored in the ontology lookup table.
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公开(公告)号：US12051010B2
公开(公告)日：2024-07-30
申请号：US16560499
申请日：2019-09-04
发明人： Luca Costabello , Mykhaylo Zayats , Jeremiah Hayes
摘要： Complex computer system architectures are described for analyzing data elements of a knowledge graph, and predicting new surprising or unforeseen facts from relational learning applied to the knowledge graph. This discovery process takes advantage of the knowledge graph structure to improve the computing capabilities of a device executing a discovery calculation by applying both training and inference analysis techniques on the knowledge graph within an embedding space, and generating a scoring strategy for predicting surprising facts that may be discoverable from the knowledge graph.
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公开(公告)号：US20210216881A1
公开(公告)日：2021-07-15
申请号：US16899365
申请日：2020-06-11
发明人： Nicholas McCarthy , Sumit Pai , Luca Costabello
摘要： This disclosure provides methods and systems for predicting missing links and previously unknown numerals in a knowledge graph. A jointly trained multi-task machine learning model is disclosed for integrating a symbolic pipeline for predicting missing links and a regression numerical pipeline for predicting numerals with prediction uncertainty. The two prediction pipelines share a jointly trained embedding space of entities and relationships of the knowledge graph. The numerical pipeline additionally includes a second-layer multi-task regression neural network containing multiple regression neural networks for parallel numerical prediction tasks with a cross stich network allowing for information/model parameter sharing between the various parallel numerical prediction tasks.
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