Abstract: A schema for a dataset is identified by identifying a dataset comprising data and relationships between data pairs. An original schema is identified for the dataset. This original schema comprises an organizational structure. An initial fit between the dataset and the original schema is determined. The initial fit quantifying a conformity of the data in the dataset to the organizational structure of the original schema. A plurality of additional schemas are identified. Each additional schema is a distinct organizational schema. The dataset is partitioned into a plurality of subsets. Each subset comprises a modified fit quantifying a modified conformity of subset data in each subset to one of the original schema and the additional schemas. The modified fit is greater than the original fit.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate encoding a tree data structure into a vector based on a set of constraints are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a constraint former that can form a set of constraints based on a first tree data structure and a vector encoder that can encode the first tree data structure into a vector based on the set of constraints.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate encoding a tree data structure into a vector based on a set of constraints are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a constraint former that can form a set of constraints based on a first tree data structure and a vector encoder that can encode the first tree data structure into a vector based on the set of constraints.

Abstract: Methods, systems, and computer program products for linkage prediction through similarity analysis are provided herein. A computer-implemented method includes extracting multiple features from (i) one or more attributes of a set of source nodes within a knowledge graph and (ii) one or more attributes of a set of target nodes within the knowledge graph, wherein at least one extracted feature satisfies a designated complexity level; performing a similarity analysis across the at least one extracted feature by applying one or more similarity measures to the at least one extracted feature; predicting one or more sets of links between the source nodes and the target nodes based on the similarity analysis, wherein one or more sets of predicted links satisfy a pre-determined accuracy threshold; and outputting the one or more sets of predicted links to a user.

Abstract: Methods, systems, and computer program products for linkage prediction through similarity analysis are provided herein. A computer-implemented method includes extracting multiple features from (i) one or more attributes of a set of source nodes within a knowledge graph and (ii) one or more attributes of a set of target nodes within the knowledge graph, wherein at least one extracted feature satisfies a designated complexity level; performing a similarity analysis across the at least one extracted feature by applying one or more similarity measures to the at least one extracted feature; predicting one or more sets of links between the source nodes and the target nodes based on the similarity analysis, wherein one or more sets of predicted links satisfy a pre-determined accuracy threshold; and outputting the one or more sets of predicted links to a user.

Abstract: A schema for a dataset is identified by identifying a dataset comprising data and relationships between data pairs. An original schema is identified for the dataset. This original schema comprises an organizational structure. An initial fit between the dataset and the original schema is determined. The initial fit quantifying a conformity of the data in the dataset to the organizational structure of the original schema. A plurality of additional schemas are identified. Each additional schema is a distinct organizational schema. The dataset is partitioned into a plurality of subsets. Each subset comprises a modified fit quantifying a modified conformity of subset data in each subset to one of the original schema and the additional schemas. The modified fit is greater than the original fit.

Abstract: A schema for a dataset is identified by identifying a dataset comprising data and relationships between data pairs. An original schema is identified for the dataset. This original schema comprises an organizational structure. An initial fit between the dataset and the original schema is determined. The initial fit quantifying a conformity of the data in the dataset to the organizational structure of the original schema. A plurality of additional schemas are identified. Each additional schema is a distinct organizational schema. The dataset is partitioned into a plurality of subsets. Each subset comprises a modified fit quantifying a modified conformity of subset data in each subset to one of the original schema and the additional schemas. The modified fit is greater than the original fit.

Abstract: A computer-implemented method, system, and article of manufacture for querying and integrating structured and unstructured data. The method includes: receiving entity information that is extracted from a first set of unstructured data using an open domain information extraction system, wherein the entity in-formation comprises relationship information between a first entity and a second entity of the first set of unstructured data; recognizing a pattern based on the relationship information and creating a schema for the first set of unstructured data based on the pattern; and associating an element of the created schema with (i) an entity of a second set of unstructured data or (ii) a schema element of an existing set of structured data if there is sufficient overall similarity between the created schema element and either the second unstructured data entity or the schema element of the existing structured data.

Abstract: Techniques for assessing trust in information are provided. The techniques include collecting information from one or more information sources, detecting one or more inconsistencies in one or more statements from the information collected from the one or more sources, assigning a penalty to each information source involved in an inconsistency and a reward for each statement not involved in any inconsistencies, and using each assigned penalty and reward to perform an aggregation to assess trust in the collected information.

Abstract: A computer-implemented method, system, and article of manufacture for querying and integrating structured and unstructured data. The method includes: receiving entity information that is extracted from a first set of unstructured data using an open domain information extraction system, wherein the entity information comprises relationship information between a first entity and a second entity of the first set of unstructured data; recognizing a pattern based on the relationship information and creating a schema for the first set of unstructured data based on the pattern; and associating an element of the created schema with (i) an entity of a second set of unstructured data or (ii) a schema element of an existing set of structured data if there is sufficient overall similarity between the created schema element and either the second unstructured data entity or the schema element of the existing structured data.

Abstract: Techniques for facilitating learning of one or more ontological rules of a resource description framework database are provided. The techniques include obtaining ontology vocabulary from a resource description framework database, generating a rule hypothesis by incrementally building upon a previously learnt rule from the database by adding one or more predicates to the previously learnt rule, performing a constraint check on the generated rule hypothesis by determining compatibility with each previously learnt rule to ensure that a complete rule set including each previously learnt rule and the generated rule hypothesis is consistent, validating the rule hypothesis as a rule using one or more association rule mining techniques to determine validity of the rule hypothesis against the database, and applying the rule to the database to infer one or more facts from the database to facilitate learning of one or more additional ontological rules.

Abstract: A system and computer program product for facilitating learning of one or more ontological rules of a resource description framework database include obtaining ontology vocabulary from a resource description framework database, generating a rule hypothesis by incrementally building upon a previously learnt rule from the database by adding one or more predicates to the previously learnt rule, performing a constraint check on the generated rule hypothesis by determining compatibility with each previously learnt rule to ensure that a complete rule set including each previously learnt rule and the generated rule hypothesis is consistent, validating the rule hypothesis as a rule using one or more association rule mining techniques to determine validity of the rule hypothesis against the database, and applying the rule to the database to infer one or more facts from the database to facilitate learning of one or more additional ontological rules.

Abstract: Computer-implemented methods for mapping an element of a source information model to an element of a target information model, forming a cluster of elements for mapping across information models, and evaluating a mapping of elements across information models, and a system and computer program product thereof. The method of mapping an element of a source information model to an element of a target information model includes: receiving information for mapping a first element in a source cluster to an element in the target information model; mapping the first element to the target element using the received information for mapping the first element to the target element; and mapping all other elements in the source cluster to the target element.

Abstract: Techniques for facilitating learning of one or more ontological rules of a resource description framework database are provided. The techniques include obtaining ontology vocabulary from a resource description framework database, generating a rule hypothesis by incrementally building upon a previously learnt rule from the database by adding one or more predicates to the previously learnt rule, performing a constraint check on the generated rule hypothesis by determining compatibility with each previously learnt rule to ensure that a complete rule set including each previously learnt rule and the generated rule hypothesis is consistent, validating the rule hypothesis as a rule using one or more association rule mining techniques to determine validity of the rule hypothesis against the database, and applying the rule to the database to infer one or more facts from the database to facilitate learning of one or more additional ontological rules.

Abstract: Computer-implemented methods for mapping an element of a source information model to an element of a target information model, forming a cluster of elements for mapping across information models, and evaluating a mapping of elements across information models, and a system and computer program product thereof. The method of mapping an element of a source information model to an element of a target information model includes: receiving information for mapping a first element in a source cluster to an element in the target information model; mapping the first element to the target element using the received information for mapping the first element to the target element; and mapping all other elements in the source cluster to the target element.

Abstract: Techniques for assessing trust in information are provided. The techniques include collecting information from one or more information sources, detecting one or more inconsistencies in one or more statements from the information collected from the one or more sources, assigning a penalty to each information source involved in an inconsistency and a reward for each statement not involved in any inconsistencies, and using each assigned penalty and reward to perform an aggregation to assess trust in the collected information.

Abstract: The present invention is a method and apparatus for safe ontology reasoning. In one embodiment, a method for building safe sub-ontology that includes one or more elements of a given ontology includes designating at least one the elements as a sensitive element, where a sensitive element is an element not to be revealed. The method then designates a safe sub-ontology such that the safe sub-ontology does not include any elements that, alone or in combination, allow inference of a sensitive element, in accordance with one or more given inference rules. In another embodiment, a method for building a potential sub-ontology includes designating at least one of the elements as a sensitive element and including a maximal number of the elements in the potential sub-ontology, wherein the maximal number includes the greatest number of elements that can be revealed, cumulatively, without allowing inference of a sensitive element, in accordance with one or more given inference rules.

Abstract: The present invention is a method and apparatus for safe ontology reasoning. In one embodiment, a method for building safe sub-ontology that includes one or more elements of a given ontology includes designating at least one the elements as a sensitive element, where a sensitive element is an element not to be revealed. The method then designates a safe sub-ontology such that the safe sub-ontology does not include any elements that, alone or in combination, allow inference of a sensitive element, in accordance with one or more given inference rules. In another embodiment, a method for building a potential sub-ontology includes designating at least one of the elements as a sensitive element and including a maximal number of the elements in the potential sub-ontology, wherein the maximal number includes the greatest number of elements that can be revealed, cumulatively, without allowing inference of a sensitive element, in accordance with one or more given inference rules.

Abstract: Methods and apparatus, including computer program products, for scalable ontology reasoning. A method of generating a summarized ontology includes loading an ontology from a store, eliminating relationships in the ontology, the eliminating relationships including an insertion of new relationships that simplify the ontology, eliminating individuals in the ontology, the eliminating individuals including insertion of new individuals to simplify the ontology, eliminating concepts in the ontology including insertion of new concepts to simplify the ontology, and generating the summarized ontology from the eliminating relationships, eliminating individuals and eliminating concepts.

Abstract: The present invention is a method and apparatus for safe ontology reasoning. In one embodiment, a method for building safe sub-ontology that includes one or more elements of a given ontology includes designating at least one the elements as a sensitive element, where a sensitive element is an element not to be revealed. The method then designates a safe sub-ontology such that the safe sub-ontology does not include any elements that, alone or in combination, allow inference of a sensitive element, in accordance with one or more given inference rules. In another embodiment, a method for building a potential sub-ontology includes designating at least one of the elements as a sensitive element and including a maximal number of the elements in the potential sub-ontology, wherein the maximal number includes the greatest number of elements that can be revealed, cumulatively, without allowing inference of a sensitive element, in accordance with one or more given inference rules.