A method, apparatus and program for completing a knowledge graph from a plurality of predicates and associated entities, the predicates each providing information on a relationship between a pair of entities, the method comprising the steps of: receiving an input comprising the plurality of predicates and associated entities; searching an axiom database and identifying predicates among the plurality of predicates that are equivalent to one another, or inverses of one another; identifying further predicates that are related to one another, using the axiom database and identified predicates; and embedding the identified predicates and associated entities into a vector space to complete the knowledge graph.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for completing a knowledge graph from a plurality of predicates and associated entities, the predicates each providing information on a relationship between a pair of entities, the method comprising the steps of: receiving an input comprising the plurality of predicates and associated entities; searching an axiom database and identifying predicates among the plurality of predicates that are equivalent to one another, or inverses of one another; identifying further predicates that are related to one another, using the axiom database and identified predicates; and embedding the identified predicates and associated entities into a vector space to complete the knowledge graph, wherein the predicates and associated entities are assigned vectors in the embedding such that a penalty function sum, which is the sum of penalty function values for all of the predicates and associated entities, is minimised, the penalty function values being: inversely proportional to the similarity between assigned vectors for two predicates identified as being equivalent to one another; directly proportional to the dissimilarity between assigned vectors for two predicates identified as inverses of one another; and weighted according to the degree of correlation between predicates that are identified as being related. 2. The method of claim 1, wherein: the plurality of predicates and associated entities are in utilized in the form of Resource Description Framework statements, and each Resource Description Framework statement is of the form <subject,predicate,object> the subject and object being entities and the predicate describing the relationship between the subject and object. 3. The method of claim 2, wherein the absence of a given Resource Description Framework statement from the information used to complete the knowledge graph is not automatically interpreted as meaning that the given Resource Description Framework statement is false. 4. The method of claim 3, further comprising: analysing the Resource Description Framework statements that are to be used to complete the knowledge graph and identifying Resource Description Framework statements that are absent; determining which of the identified absent Resource Description Framework statements is likely to be false based on the analysis; and adjusting the embedding of the Resource Description Framework entities and predicates that are to be used to complete the knowledge graph on the basis of the determination. 5. The method of claim 1, wherein the penalty function sum is of the form: RS(Θ)=Σp≡q∈A1D(e(p)∥e(q))+Σp≡q−∈A2D(e(p)∥ϕ(e(q))) wherein: θ is the parameters of the model used to complete the knowledge graph; p and q are predicates; A1 is a set containing all of the schema equivalence axioms; A2 is a set containing all of the schema inverse axioms; e(x) is the embedding vector of x; ϕ is a model dependent transformation function; and D(x∥y) is a measure of the dissimilarity between two given vectors x and y. 6. The method of claim 1, the method further comprising storing the set of assigned vectors found to minimise the penalty function sum. 7. The method of claim 6, further comprising identifying additional statements based on the set of assigned vectors found to minimise the penalty function sum, and updating the knowledge graph (set of triples) using the additional statements. 8. The method of claim 1, wherein the value of the penalty function sum is minimised using stochastic gradient descent optimisation. 9. An apparatus for completing a knowledge graph from a plurality of predicates and associated entities, the predicates each providing information on a relationship between a pair of entities, the apparatus comprising: a data receiver configured to receive an input comprising the plurality of predicates and associated entities; an information retriever configured to search an axiom database and identify predicates among the plurality of predicates that are equivalent to one another, or inverses of one another; a trainer configured to identify further predicates that are related to one another, using the axiom database and identified predicates, and to embed the identified predicates and associated entities into a vector space to complete the knowledge graph, wherein the trainer is further configured to assign vectors to the predicates and associated entities such that a penalty function sum, which is the sum of penalty function values for all of the predicates and associated entities, is minimised, the penalty function values being: inversely proportional to the similarity between assigned vectors for two predicates identified by the information retriever as being equivalent to one another; directly proportional to the dissimilarity between assigned vectors for two predicates identified by the information retriever as inverses of one another; and weighted according to the degree of correlation between predicates that are identified as being related. 10. The apparatus of claim 9, wherein the plurality of predicates and associated entities are input into the data receiver in the form of Resource Description Framework statements, each Resource Description Framework statement being of the form <subject, predicate, object> the subject and object being entities and the predicate describing the relationship between the subject and object, the apparatus further comprising an example generator configured to: analyse the Resource Description Framework statements that are to be used to complete the knowledge graph and identify Resource Description Framework statements that are absent; determine which of the identified absent Resource Description Framework statements is likely to be false based on the analysis, wherein the absence of a given Resource Description Framework statement from the information used to complete the knowledge graph is not automatically interpreted as meaning that the given Resource Description Framework statement is false; and adjust the embedding of the Resource Description Framework entities and predicates that are to be used to complete the knowledge graph on the basis of the determination. 11. The apparatus of claim 9, further comprising a storage unit configured to store the set of assigned vectors found to minimise the penalty function. 12. The apparatus of claim 9, wherein the trainer is configured to minimise the value of the penalty function sum using stochastic gradient descent optimisation. 13. A computer program which, when executed on a computer, causes the computer to perform a method for completing a knowledge graph in accordance with claim 1. 14. A non-transitory computer readable medium comprising a computer program in accordance with claim 13. 15. The apparatus of claim 10, further comprising a storage unit configured to store the set of assigned vectors found to minimise the penalty function. 16. The apparatus of claim 11, wherein the trainer is configured to minimise the value of the penalty function sum using stochastic gradient descent optimisation.