International Business Machines Corporation

Freddy Lorge

Tom Odd Rojahn

Matthew A Pantland

Aruna Yedavilli

Patrick Haim

Vinod A Valecha

Frederik Frank Flöther

Jared Andrew Limberg

Mehmet Cuneyt Goksu

Ahsan A Asghar

David Brian Callies

Apparatuses, system, and computer-implemented methods to facilitate physical representation of quantum entanglement are provided. According to an embodiment, an apparatus can comprise two or more qubit representation devices that interact with one another in a defined manner, and a display component operatively coupled to at least one of the two or more qubit representation devices, wherein the display component outputs a visual indicator of quantum entanglement between the two or more qubit representation devices.

Physical representation of quantum entanglement

Measuring risk intensity level of a scene captured within media is provided. A score is generated, using a computational scoring model, for each risk type, cause, and evidence taxon element described in a risk taxonomy corresponding to a scene class by matching one or more taxon elements with each attribute of each detected object of a set of detected objects within the scene captured by the media. The media of the scene is tagged with risk type, cause, and evidence scores of each taxon element of the risk taxonomy corresponding to the scene class. A risk intensity level of the scene is calculated based on the risk type, cause, and evidence scores tagged to the media. An action of a set of defined actions is performed based on the risk intensity level of the scene.

Measuring risk within a media scene

A method, system, and program product for testing adaptive software is provided. The method includes receiving a selection for a software testing model associated with a portion of a software product requiring operational testing. The software testing model is selected from a database of saturated models. Input data for executing the software testing model is received and loaded within a model execution module. The software testing model is executed with respect to the input data and an output verifying results is generated. Input values associated with operational issues of the software product are stored. Machine learning software code is generated. The machine learning software code is configured for executing future processes associated with generating and selecting additional input values for future executions of the software testing model.

Software testing with machine learning models

A method, and associated computer system and computer program product, for computing a value between two concepts in a schema containing concepts which are linked to each other through associations. In a schema S of n concepts C1, C2 . . . Ci . . . Cn, the concepts are linked by associations, each association having a semantic distance set in a range between a minimum and a maximum indicating the concepts are completely similar or dissimilar respectively. An information value is determined between concepts from their semantic distance and informational distance. For dissimilar concepts, the informational distance is computed according to a closeness of the concepts. For similar concepts, the informational distance is computed according to a remoteness of the concepts. Both the first and second functions increase with a number of links between C1 and another concept. The number of links is a topological distance between C1 and the other concept.

Computing the value of information between concepts

An approach for building an ontology is provided. Syntactically transformed complex triples whose core terms are identified are semantically transformed into simplified triples referring to new conceptualized core terms formed by user selections from a lexical database or by adding a suffix to core terms. The core terms are assigned to respective definitions and keys in a reference ontology, thereby retaining the semantics of the complex triples. Based on the reference ontology, an enrichment transformation of the simplified triples is performed to create simplified and enriched triples by adding relations derived from a correspondence each term in the simplified triples has with the reference ontology and by adding representations of semantics of reference ontology definitions of the terms. The simplified and enriched triples are stored as an ontology representing knowledge in an application providing free-form text from which the complex triples were extracted.

Building an ontology by transforming complex triples

A system and related method for automating alert decision-making in a computer network are disclosed. The system and method act to receive an alert request from an event management system, and then carries out operations using several sets of inference rules, associating the alert request to a generic conceptual data model of alert request and alert resolution. The generic model is specialized to represent the specific environment being monitored. The alert request is enriched with contextual and network information in order to generate one or several alert resolution actions.

Alert management system and method

An approach for building an ontology is provided. Syntactically transformed complex triples whose core terms are identified are semantically transformed into simplified triples referring to new conceptualized core terms formed by user selections from a lexical database or by adding a suffix to core terms. The core terms are assigned to respective definitions and keys in a reference ontology, thereby retaining the semantics of the complex triples. Based on the reference ontology, an enrichment transformation of the simplified triples is performed to create simplified and enriched triples by adding relations derived from a correspondence each term in the simplified triples has with the reference ontology and by adding representations of semantics of reference ontology definitions of the terms. The simplified and enriched triples are stored as an ontology representing knowledge in an application providing the free-form text from which the complex triples were extracted.

A method, process, and associated systems for ranking elements of an answer set retrieved by a conceptual search engine in response to a set of queried concepts submitted as a conceptual query. A processor of a computer system orders by relevance the semantic schemas that make up the retrieved answer set by first identifying a weight of each concept in a retrieved schema as a function of relationships among the concepts in that schema, then by identifying a semantic value of each queried concept in the retrieved schema as a function of the weights, and finally by identifying a semantic value of the query in the schema as a function of the semantic values of all queried concepts. The relative relevance of each schema may then be identified as a function of the semantic value of the query in that schema.

Ranking answers to a conceptual query

Ordering search-engine results

An approach is presented for determining a value of an association between first and second ontologies, Sand S. A first bridge of Swith Sis generated. A schema S' is created from sub-schema S extracted from the first bridge by removing common concepts and relationships to the common concepts, and for each common concept to which first and second concepts have immediate links in S, by creating a new immediate link in S&#x2032; between the first and second concepts. Based on an intrinsic value of S&#x2032; and a strength of the bridge of Swith S, a value of the first bridge is determined. Similarly, a value of a second bridge of Swith Sis determined. A value of the association between Sand Sis determined based on a sum of the values of the first and second bridges.

Determining the value of an association between ontologies

An approach for building an ontology is provided. Based on a grammar, extracted complex triples are syntactically transformed to identify core terms. The syntactically transformed complex triples are semantically transformed into simplified triples referring to new terms that conceptualize the core adjectives, adverbs and verbs, and assigning the core terms to respective definitions and keys in a reference ontology, thereby retaining the semantics of the complex triples. Based on a meta-schema of the reference ontology, an enrichment transformation of the simplified triples is performed to create simplified and enriched triples by adding relations derived from a correspondence each term in the simplified triples has with the reference ontology and by adding representations of semantics of reference ontology definitions of the terms. The simplified and enriched triples are stored as an ontology representing knowledge in an application providing the free-form text from which the complex triples were extracted.

An approach is presented for determining a value of a bridge of a first ontology schema (S1) with a second ontology schema (S2). A bridge of S1 with S2 is generated. A sub-schema S is extracted from the bridge of S1 with S2. A schema S' is created from S by removing common concepts and relationships to the common concepts, and for each common concept to which first and second concepts have immediate links in S, by creating a new immediate link in S&#x2032; between the first and second concepts. An intrinsic value of S&#x2032; and a strength of a bridge of S1 with S are determined. Based on the intrinsic value of S&#x2032; and the strength of the bridge of S1 with S, the value of the bridge of S1 with S2 is determined.

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Vedrin, Belgium

Freddy Lorge