2022
INTEGRATED GRAPH NEURAL NETWORK FOR SUPERVISED NON-OBVIOUS RELATIONSHIP DETECTION
Phillipp M?ller, Xiao Qin, Balaji Ganesan, Berthold Reinwald, Nasrullah Sheikh
Abstract
A method, a computer program product, and a system for non-obvious relationship detection. The method includes receiving a knowledge and inputting a first node and a second node from the knowledge graph into a twin neural network. The method also includes embedding the first node and the second node, aggregating neighborhood information and position information into the node embeddings. The method further includes concatenating the neighborhood information and the position information of the first node and the second node to produce a first output vector and a second output vector. The method also includes generating a final score by comparing the first output vector with the second output vector. The final score indicates a probability of a non-obvious relationship between the first node and the second node.
Neural-based ontology generation and refinement
Balaji Ganesan, Riddhiman Dasgupta, Akshay Parekh, Hima Patel, Berthold Reinwald, Sameep Mehta
Abstract
Aspects of the present disclosure relate to neural-based ontology generation and refinement. A set of input data can be received. A set of entities can be extracted from the set of input data using a named-entity recognition (NER) process, each entity having a corresponding label, the corresponding labels making up a label set. The label set can be compared to concepts in a set of reference ontologies. Labels that match to concepts in the set of reference ontologies can be selected as a candidate concept set. Relations associated with the candidate concepts within the set of reference ontologies can be identified as a candidate relation set. An ontology can then be generated using the candidate concept set and candidate relation set.
Generation of training data from redacted information
Balaji Ganesan, Kalapriya Kannan, Neeraj Ramkrishna Singh, Shettigar Parkala Srinivas, Hima Patel, Soma Shekar Naganna, Berthold Reinwald, Sameep Mehta
Abstract
One embodiment provides a computer implemented method, including: obtaining an information document corresponding to an entity, wherein the information document includes redacted information spans; identifying an entity type for each of the redacted information spans, wherein the entity type identifies a relationship between a redacted information span and at least one other entity within the information document; replacing the redacted information spans with replacement entities corresponding to the entity type of a given redacted information span, wherein the replacing is performed in view of a frequency distribution of actual information and wherein the replacing includes maintaining relationships of the redacted information spans; and controlling bias within the replacement entities, wherein the controlling includes detecting bias within the replacement entities.
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