CASCADING LEARNING SYSTEM AS SEMANTIC SEARCH
A cascading learning system as a 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 and determines term context in the request to normalize request data from the term context. The normalized request data are classified and dispatched to a corresponding domain-specific module. Each domain-specific module of a search module generates a prediction with a trained probability of an expected output. The terminology manager receives normalized request data from the request dispatcher and classifier, and manages terminology stored in a contextual network. The cluster manager controls data flow between the request dispatcher and classifier, the search module container, the terminology manager, and a business data source system.
1 . A method comprising:
receiving a request for search terms in business documents from a client application;
determining, at a request analyzer of a cascading learning system of a server, term context in the request and normalizing request data from the term context;
classifying and dispatching, at a request dispatcher and classifier, the normalized request data to a corresponding domain-specific module;
generating, at a domain-specific module of a search module container, a prediction with a trained probability of an expected output based on the normalized request data;
receiving normalized request data and managing terminology stored in a contextual network of a terminology manager; and
controlling data flow, at a cluster manager, between the request dispatcher and classifier, the search module container, the terminology manager, and a business data source system.
2 . The method of claim 1 , wherein sources of search terms of the client application comprise documents, messages, and terms in queries.
3 . The method of claim 1 , wherein the business data source system comprises document management system and a business application.
4 . The method of claim 1 , wherein classifying further comprises:
using an artificial neural network (ANN) to implement a classification algorithm to classify the normalized request data to the corresponding domain-specific module.
5 . The method of claim 1 , wherein generating further comprises:
generating a feed-forward neural network (FFNN) specialized in providing information most relevant to the end user of the client application.
6 . The method of claim 5 , further comprising:
calculating which document is most relevant for the end-user; and
calculating response information based on previously learned calculation functions.
7 . The method of claim 6 , further comprising:
building terminology definitions using semantic objects and relations; and
extracting terminology from particular domain-specific repositories.
8 . The method of claim 1 , further comprising:
extracting terminology from one or more domain-specific repositories comprising document management systems (DMS), business applications, and business objects; and
organizing data structure and data clusters in the request dispatcher and classifier and the search module container.
9 . The method of claim 1 , further comprising:
managing training, validation, and test sets for artificial neural networks (ANN) and feed-forward neural networks (FFNN); and
controlling cluster data flow of input data used in each domain-specific modules.
10 . A cascading learning system comprising:
a request analyzer configured to receive a request for search terms from a client application, to determine term context in the request, and to normalize request data from the term context;
a request dispatcher and classifier configured to classify and dispatch the normalized request data to a corresponding domain-specific module;
a search module container comprising a plurality of domain-specific modules, each domain-specific module configured to generate a prediction with a trained probability of an expected output;
a terminology manager configured to receive normalized request data from the request dispatcher and classifier, and to Manage terminology stored in a contextual network; and
a cluster manager configured to control data flow between the request dispatcher and classifier, the search module container, the terminology manager, and a business data source system.
11 . The cascading learning system of claim 10 , wherein sources of search terms of the client application comprise documents, messages, and terms in queries; and
wherein the business data source system comprises a document management system (DMS) and a business application.
12 . The cascading learning system of claim 10 , wherein the request dispatcher and classifier comprises an artificial neural network (ANN) configured to implement a classification algorithm to classify the normalized request data to the corresponding domain-specific module.
13 . The cascading learning system of claim 10 , wherein each domain-specific module includes a feed-forward neural network (FFNN) specialized in providing information most relevant to the end user of the client application.
14 . The cascading learning system of claim 13 , wherein the FFNN is configured to calculate which document is most relevant for the end-user and calculate response information based on previously learned calculation functions.
15 . The cascading learning system of claim 10 , wherein the terminology manager comprises a contextual network and a terminology extractor, the contextual network comprises terminology definitions built using semantic objects and relations, and the terminology extractor is configured to extract terminology from particular domain-specific repositories.
16 . The cascading learning system of claim 15 , wherein the contextual network comprises a provider terminology module, a common terminology module, and a domain specific terminology, the provider terminology module comprising terminology provided by a system provider, the common terminology module comprising a combined terminology from all knowledge domains and used by the request dispatcher and classifier to classify the request and dispatch it to the corresponding domain-specific module and domain-specific terminology, the domain-specific terminology comprising terminology that is mainly used to provide data in the corresponding domain-specific module.
17 . The cascading learning system of claim 15 , wherein the terminology extractor is configured to extract terminology from one or more domain-specific repositories comprising document management systems (DMS), business applications, and business objects.
18 . The cascading learning system of claim 10 , wherein the cluster manager is configured to organize data structures and data clusters in the request dispatcher and classifier and the search module container.
19 . The cascading learning system of claim 18 , wherein the cluster manager is configured to manage training, validation, test sets for artificial neural networks (ANN) and feed-forward neural networks (FFNN), and to control cluster data flow of input data used in each domain-specific module.
20 . A non-transitory, computer-readable medium that stores instructions, which, when performed by a computer, cause the computer to perform operations comprising:
receiving a request for search terms from a client application;
determining, at a request analyzer of a cascading learning system of a server, term context in the request and normalizing request data from the term context;
classifying and dispatching, at a request dispatcher and classifier, the normalized request data to a corresponding domain-specific module;
generating, at a domain-specific module of a search module container, a prediction with a trained probability of an expected output based on the normalized request data;
receiving normalized request data and managing terminology stored in a contextual network of a terminology manager; and
controlling data flow, at a cluster manager, between the request dispatcher and classifier, the search module container, the terminology manager, and a business data source system.