Abstract: A method for providing a computer-implemented medical diagnosis includes receiving an input from a user comprising at least one symptom of the user. The method also includes providing the at least one symptom as an input to a medical model, the medical model being retrieved from memory. The medical model includes a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases. The method also includes performing inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease. The method also includes outputting an indication that the user has a disease from the Bayesian inference, wherein the inference is performed using a counterfactual measure.

Abstract: Methods for providing a computer implemented medical diagnosis are provided. In one aspect, a method includes receiving an input from a user comprising at least one symptom of the user, and providing the at least one symptom as an input to a medical model. The method also includes deriving estimates of the probability of the user having a disease from the discriminative model, inputting the estimates to the inference engine, performing approximate inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease, and outputting the probability of the user having the disease for display by a display device.

Abstract: Methods for providing a computer implemented medical diagnosis are provided. In one aspect, a method includes receiving an input from a user comprising at least one symptom of the user, and providing the at least one symptom as an input to a medical model. The method also includes deriving estimates of the probability of the user having a disease from the discriminative model, inputting the estimates to the inference engine, performing approximate inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease, and outputting the probability of the user having the disease for display by a display device.

Abstract: A graphical classification method for classifying graphical structures, said graphical structures comprising nodes defined by feature vectors and having relations between the nodes. The method includes representing the feature vectors and relations as a first graphical representation. The method also includes mapping said first graphical representation into a second graphical representation wherein the mapping comprises using an attention mechanism, said attention mechanism establishes the importance of specific feature vectors dependent on their neighbourhood and the relations between the feature vectors, said mapping transforming the feature vectors of the first graphical representation to transformed feature vectors in the second graphical representation. The method also includes combining the transformed feature vectors to obtain a third combined representation said third combined representation being an indication of the classification of the graphical structure.

Abstract: The present disclosure relates to a computer-implemented method of verifying a semantic triple generated for building a knowledge base including data patterns defining concepts associated with semantic triples derived from unstructured text. The method includes providing the semantic triple to a user interface, the semantic triple including a subject, an object, and a relation. The method also includes receiving, from the user interface, an acceptance or a rejection of the subject, the object, and the relation as relevant or not to the knowledge base. The method also includes transmitting the semantic triple for inclusion as a data pattern in the knowledge base in the event that all of the subject, the object, and the relation, have been accepted.

Abstract: A method for providing a computer implemented medical diagnosis includes receiving an input from a user comprising a symptom of the user. The method also includes providing the symptom as an input to a medical model comprising a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases, and an inference engine configured to perform Bayesian inference on said probabilistic graphical model. The method also includes generating a question for the user to obtain further information concerning the user to allow a diagnosis, and outputting said question to the user. The method also includes outputting said question to the user, wherein generating a question for the user comprises ranking said questions by determining the utility of the possible questions.

Abstract: The subject-matter described herein relates to a computer-implemented method of enabling interoperability between a first knowledge base and a second knowledge base. Each knowledge base is graphically represented and includes a plurality of nodes each defining a concept and a plurality of relations linking the plurality of nodes. The first knowledge base and the second knowledge base are encoded using different coding standards. The method comprises: identifying an entity from the plurality of entities in the second knowledge base; obtaining a mapping between the identified entity from the second knowledge base and a matching entity from the first knowledge base; and creating and storing a link between the identified entity from the second knowledge base and the matching entity from the first knowledge base.

Abstract: A computer implemented method of combining two knowledge bases, each knowledge base comprising concepts that are linked by relations, the method comprising: assigning one of the knowledge bases as a first knowledge base and the other of said knowledge bases as an additional knowledge base; matching concepts between the first knowledge base and the additional knowledge base to define mapping relations between concepts of the first and additional knowledge base; assessing defined mapping relations to determine if they cause a violation with relations already present in the first or second knowledge base; modifying relations within the additional knowledge base to repair violations; and storing an extended first knowledge base comprising the first knowledge base, the defined mapping relations and the additional knowledge base with the modified relations within the additional knowledge base.

Abstract: A computer-implemented method of generating a PGM with causal information, said graphical model containing the causal relationship between a first variable and a second variable, the method comprising: receiving data at a processor, said data showing a correlation between the first variable and a second variable; producing a third variable by reducing the dimensionality of the graphical representation of the two dimensional data defined by the first variable and the second variable, determining determine the causal relationship between the first and third variables and the second and third variable, the causal discovery algorithm being able to determine if the first variable causes the third variable, the third variable causes the first variable, the second variable causes the third variable and the third variable causes the second variable; and outputting a graphical model indicating the direction of edges in a graphical representation of said PGM.

Abstract: A method for providing a computer-implemented medical diagnosis includes receiving an input from a user comprising at least one symptom of the user. The method also includes providing the at least one symptom as an input to a medical model, the medical model being retrieved from memory. The medical model includes a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases. The method also includes performing inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease. The method also includes outputting an indication that the user has a disease from the Bayesian inference, wherein the inference is performed using a counterfactual measure.

Abstract: The subject-matter described herein relates to a computer-implemented method of enabling interoperability between a first knowledge base and a second knowledge base. Each knowledge base is graphically represented and includes a plurality of nodes each defining a concept and a plurality of relations linking the plurality of nodes. The first knowledge base and the second knowledge base are encoded using different coding standards. The method comprises: identifying an entity from the plurality of entities in the second knowledge base; obtaining a mapping between the identified entity from the second knowledge base and a matching entity from the first knowledge base; and creating and storing a link between the identified entity from the second knowledge base and the matching entity from the first knowledge base.

Abstract: A text processing method for improving the accuracy of a response to a query directed to a system comprising concepts and relations defined by a knowledge base, wherein the method comprises: (i) producing a dependency tree from the query, wherein the dependency tree has at least one branch containing nodes and at least one connection between those nodes, wherein each node has a node label which corresponds to a term within the query, and wherein each connection has a label which corresponds to the linguistic relationship between terms within the query; (ii) from the dependency tree, generating a query concept using concepts and relations defined by the knowledge base; (iii) checking if the query concept has a subsumption relationship with a candidate concept retrieved from the system, and if no subsumption relationship is initially identified, optimising the dependency tree by changing the nodes, followed by repeating steps (ii) and (iii); and wherein the query concept and the candidate concept comprise at

Abstract: A method of training a model, said model being adapted to map a first graphical data structure representation to a second graphical data structure representation, the first graphical data structure representation comprising nodes, with at least one of a plurality of relations between said nodes, the second graphical data structure representation comprising nodes, the mapping comprises using an attention mechanism, wherein said attention mechanism establishes the importance of specific nodes dependent on their neighbourhood and the relations between the nodes, wherein the mapping comprising using a projection kernel to map the nodes of the first graphical structure to nodes of an intermediate representation and using an attention kernel to enact the attention mechanism. The method includes receiving a training data set comprising an output layer and a corresponding input layer. The method also includes training the parameters of the projection kernel and the attention kernel using the training data set.

Abstract: A computer implemented method for classifying molecular structures is provided. The method includes representing the elements and atoms in a molecular structure as nodes and the bonds as relations as a first graphical representation. The method also includes mapping said first graphical representation into a second graphical representation wherein the mapping comprises using an attention mechanism, said attention mechanism establishes the importance of specific feature vectors dependent on their neighbourhood and the relations between the feature vectors, said mapping transforming the feature vectors of the first graphical representation to transformed feature vectors in the second graphical representation. The method also includes combining the transformed feature vectors to obtain a third combined representation. The method also includes mapping said third combined representation to a feature vector indicating properties of the molecular structure.

Abstract: Methods for creating a model that is a graphical representation are provided. In one aspect, a method includes including receiving a first dataset including a first variable and a third variable, and a second dataset including a second variable and the third variable. The method also includes creating graphical representations of the first and second datasets by applying conditional independence tests on them, and storing conditional independence information obtained by applying the conditional independence tests on the first and second datasets. The method also includes applying a bivariate causal discovery algorithm. The method further includes modifying the graphical representations of the first and second dataset according to the determined causal relations, and creating a set of candidate graphical representations for a third dataset including the first and second datasets. Each candidate graphical representation is consistent with the conditional independence information.

Abstract: Methods for determining whether two sets of words are similar are provided. In one aspect, a method includes receiving a first set of words and a second set of words, whichare subsets of a vocabulary, and each of the first and second sets of words include word embeddings corresponding to each word. The method also includes determining a word membership function for each word in the vocabulary. Determining the word membership includes determining a set of similarity values, each representing the similarity between the word and a respective word in the vocabulary. The method also includes determining a membership function for the first and second sets of words based on the determined word membership functions, and determining a set-based coefficient for the similarity between the first and second sets of words based on the membership function. Systems and devices are also provided.

Abstract: A method of mapping a first graphical data structure representation to a second graphical data structure representation, the first graphical data structure representation comprising nodes, with at least one of a plurality of relations between said nodes, the second graphical data structure representation comprising nodes, the mapping comprises using an attention mechanism, wherein said attention mechanism establishes the importance of specific nodes dependent on their neighbourhood and the relations between the nodes.

Abstract: Embodiments described herein provide a more flexible, effective, and computationally efficient means for determining multiple intents within a natural language input. Some methods rely on specifically trained machine learning classifiers to determine multiple intents within a natural language input. These classifiers require a large amount of labelled training data in order to work effectively, and are generally only applicable to determining specific types of intents (e.g., a specifically selected set of potential inputs). In contrast, the embodiments described herein avoid the use of specifically trained classifiers by determining inferred clauses from a semantic graph of the input. This allows the methods described herein to function more efficiently and over a wider variety of potential inputs.

Abstract: The disclosed system addresses a technical problem tied to computer technology and arising in the realm of computer memory capacity, namely the technical problem of providing a flexible response dialogue system that can be utilised for a variety of different types of dialogue without requiring the system to be specifically trained for each situation. This therefore avoids the need for large amounts of labelled training data for each type of dialogue (each potential conversation flow or subject area for the conversation). The disclosed system solves this technical problem by using semantic similarity to match a user's input to one of a set of predefined inputs (predefined user responses). Various mechanisms are implemented to provide disambiguation in the event of multiple potential matches for the input. By using semantic similarity, the user's response in unconstrained. This therefore provides a user interface that is more user-friendly.

Abstract: The present disclosure relates to a computer-implemented method of generating a semantic triple for building a knowledge base to include data patterns associated with semantic triples derived from unstructured text. The method includes providing a sentence associated with unstructured text including a main verb and a taxonomic verb, generating a first frame, generating a second frame, identifying a common sub-string in the first subject and either the second subject or the second object, or a common sub-string in the first object and either the second subject or the second object; generating a semantic triple using the first frame and replacing the first subject or the first object having the common sub-string with the second subject or the second object having the common sub-string; and transmitting the semantic triple for inclusion as a data pattern in the knowledge base.