Abstract: A method and apparatus for determining relationships between objects in a meta-model semantic network is described. A contextual network graph comprising nodes and edges representing semantic objects and semantic relationships is generated from a meta-model of business objects from the meta-model semantic network. The contextual network graph is used to generate a unique identifier for each node and associated edge. The unique identifiers are used to compute information of cost and energy between the nodes. The information is stored in a meta-model semantic network database.

Abstract: In an embodiment, a method is provided for searching similar documents. Here, a document is accessed and terms from a metamodel semantic network is identified. The document is analyzed to identify a number of the terms from the metamodel semantic network that are also found in the document, and to identify a frequency of occurrence in the document for each term. A search is conducted for other documents having frequencies of occurrences that are similar to the identified frequency of occurrence. These other documents have been previously analyzed using the same terms from the metamodel semantic network.

Abstract: In an embodiment, a method is provided for utilizing a meta-model semantic network. In this method, a meta-model of the enterprise data is obtained. The meta-model provides semantic information regarding a definition of a business object. The meta-model is then used to generate a rule definition that maps enterprise data to a semantic object definition and a semantic relation definition. With the rule definition, embodiments may then generate a semantic object and a semantic relation from data extracted from the enterprise data. The semantic object and semantic relation are stored in the meta-model semantic network.

Abstract: A semantic phrase suggestion engine that provides term and sentence suggestions based on context-specific user groups. Knowledge domains within a semantic network may be automatically derived from user software applications, and each term within the knowledge domain includes meta-data about the terms, e.g., term type and an importance indicator. The indicators may be defined within the context of specific user groups and relate to how many times that group has used the term (e.g., in documents, emails, etc.) The semantic phrase suggestion engine may also include spelling conditions and grammar conditions, which can then provide phrase suggestions according to the conditions and importance indicators, specific to a user group.

Abstract: In example embodiments, a technique is provided to determine the similarity between two terms. For example, example embodiments may store a meta-model semantic network that includes a first and second term. Further, both the first and second terms are respectively associated with model and meta-model information. A request to calculate a term similarity value is received. A term similarity value expresses a correlation between the first term and the second term. The term similarity value is then calculated based on a comparison of the model and the meta-model information associated with the first and second terms.

Abstract: In an embodiment, a method is provided for conducting a search. In this method, a message is received from a client application. A user that initiated the message is identified and context information associated with the user is retrieved. Thereafter, a business object associated with the context information is identified and a domain associated with the business object is identified. Here, the domain includes a number of terms, and one or more of these terms are selected to be included in a query. A search of a data source can then be conducted using this query.

Abstract: A method, machine readable storage medium, and system for providing a self learning semantic search engine. A semantic network may be set up with initial configuration. A search engine coupled to the semantic network may build indexes and semantic indexes. A user request for business data may be received. The search engine may be accessed via a semantic dispatcher. And based on the access, search engine may update the indexes and semantic indexes.

Abstract: A method, machine readable storage medium, and system for providing personalized semantic controls for semantic systems. A semantic network may be set up with initial configuration. A business application user interface, including semantic controls, may be coupled to the semantic network to interact with the semantic network. Semantic objects and relations may be defined in the semantic network for business terminology. A user request for business data may be received. The semantic network may update the objects and relations for business terminology based on the request. The business application user interface may provide for personalized semantic controls based on the updated objects and relations.

Abstract: A semantic phrase suggestion engine that provides term and sentence suggestions based on context-specific user groups. Knowledge domains within a semantic network may be automatically derived from user software applications, and each term within the knowledge domain includes meta-data about the terms, e.g., term type and an importance indicator. The indicators may be defined within the context of specific user groups and relate to how many times that group has used the term (e.g., in documents, emails, etc.) The semantic phrase suggestion engine may also include spelling conditions and grammar conditions, which can then provide phrase suggestions according to the conditions and importance indicators, specific to a user group.

Abstract: A cascading learning system for semantic search is described including the generation, training and testing of a domain-specific module for a domain-specific search. One or more input elements and output elements are specified for the domain-specific module with reference to a domain that relates these elements together through data sets that include related metadata. The related metadata may include semantic terms that are incorporated into a contextual network applicable to the domain.

Abstract: A method, machine readable storage medium, and system for providing a self learning semantic search engine. A semantic network may be set up with initial configuration. A search engine coupled to the semantic network may build indexes and semantic indexes. A user request for business data may be received. The search engine may be accessed via a semantic dispatcher. And based on the access, search engine may update the indexes and semantic indexes.

Abstract: A method, machine readable storage medium, and system for providing personalized semantic controls for semantic systems. A semantic network may be set up with initial configuration. A business application user interface, including semantic controls, may be coupled to the semantic network to interact with the semantic network. Semantic objects and relations may be defined in the semantic network for business terminology. A user request for business data may be received. The semantic network may update the objects and relations for business terminology based on the request. The business application user interface may provide for personalized semantic controls based on the updated objects and relations.

Abstract: A cascading learning system as a normalized semantic search is described. The cascading learning system has a request analyzer, a request dispatcher and classifier, a search module, a terminology manager, and a cluster manager. The request analyzer receives a request for search terms from a client application. The request analyzer has a normalization manager, a semantic parser, and a context builder. The normalization manager normalizes the search terms and generates a normalized semantic request based on a context. The request dispatcher and classifier classifies and dispatches the normalized semantic request to a corresponding domain-specific module that generates a prediction with a trained probability of an expected output. The terminology manager receives the normalized semantic request from the request dispatcher and classifier, and manages terminology stored in a contextual network.

Abstract: A method, machine readable storage medium, and system for providing a self learning semantic search engine. A semantic network may be set up with initial configuration. A search engine coupled to the semantic network may build indexes and semantic indexes. A user request for business data may be received. The search engine may be accessed via a semantic dispatcher. And based on the access, search engine may update the indexes and semantic indexes.

Abstract: A method and apparatus for detection of relationships between objects in a meta-model semantic network is described. Semantic objects and semantic relations of a meta-model of business objects are generated from a meta-model semantic network. The semantic relations are based on connections between the semantic objects. A probability model of terminology usage in the semantic objects and the semantic relations is generated. A neural network is formed based on usage of the semantic objects, the semantic relations, and the probability model. The neural network is integrated with the semantic objects, the semantic relations, and the probability model to generate a contextual network. The generated probability model is integrated with semantic objects and neural networks for form parallel networks.

Abstract: The invention concerns a method for preparing organic-inorganic hybrid materials with controlled porosity and functionality comprising hydrolysis and condensation polymerization of a compound of formula (I): Z-R1—Si(OR)(OR?)(OR?), with an alkaline or alkaline-earth silicate in a mol ratio of (I)/(silicate)=1:9, in the presence of a non-ionic surfactant, to form a mesoporous silica represented by Z-R1—SiO1.5 9 SiO2, whereon is then anchored an organic compound, represented by (?)N—H, comprising a —NH— group capable of reacting with Z, to form a functionalized mesoporous silica represented by (?)N—R—SiO1.5 9 SiO2, which is optionally complexed with a metallic cation to form an organometallic complex. The invention also concerns the use of these materials to separate gases.

Abstract: The invention relates to the use of molecular tweezers as sensitive materials in chemical sensors intended to detect or assay organic compounds in the vapour state, and in particular nitro compounds. These molecular tweezers correspond to the general formula (I): in which: MC1 and MC2 represent macrocycles; p and q are equal to 0 or 1; X and Y are optionally substituted C1 to C10 alkylene groups; while E represents an optionally substituted cyclic or heterocyclic spacer group; and in which MC1 and MC2 are positioned facing each other. Fields of application: detection of explosives, control and monitoring of atmospheric pollution and of the quality of ambient air in relatively confined spaces, and monitoring of industrial sites.

Abstract: The invention concerns compounds of formula (I), wherein: R1, R2, R3 and R4 represent a hydrogen atom, an alkyl radical comprising 1 to 4 carbon atoms, a phenyl radical optionally substituted by one or several groups selected among vinyl, hydroxy, nitro, amino, bromo, chloro, fluoro, iodo, benzyloxy radicals, alkyl or alkyloxy radicals comprising 1 to 4 carbon atoms optionally substituted by one or several bromo, chloro, fluoro or iodo groups; R5, R6, R7 and R8 represent a hydrogen atom or an alkyl radical comprising 1 to 4 carbon atoms; Ra, Rb, and Rc represent a hydrogen atom, a phenyl radical optionally substituted by one or several groups selected among the vinyl, hydroxy, nitro, amino, bromo, chloro, fluoro, iodo, benzyloxy radicals, alkyl or alkyloxy radicals comprising 1 to 4 carbon atoms, optionally substituted by one or several bromo, chloro, fluoro or iodo groups.

Abstract: The invention concerns a method for preparing organic-inorganic hybrid materials with controlled porosity and functionality, comprising hydrolysis and condensation polymerisation of at least a compound of formula (I): Z—R1—Si(OR)(OR′)(OR″), wherein: Z represents either an atom selected among chlorine, bromine or iodine atoms, or an amino or phosphino group; R, R′ and R″, independently of one another, represent an alkyl radical comprising on to four carbon atoms; R1 represents a divalent radical derived from an aliphatic hydrocarbon chain comprising 1 to 30 carbon atoms, wherein are optionally interspersed one or several arylene; —O—, —S—, —O—C(═O), —N(R6)—C(═O)— or N(R6), with at least an alkaline or alkaline-earth silicate of (II) in a mol ratio (I)/(II)=1/9 in the presence of at least a non-ionic surfactant in micellar concentration, to form a mesoporous silica represented by formula (III): Z—R1&