The invention is ontology-based architecture for performing semantic data mining for insights. The major components of the architecture are illustrated using Network Ontology (NO), an ontology generated for the network domain for delivering improved network management. In addition, algorithms for summarizing performance profiles in the form of rank tables and for extracting insight rules (concrete action plan) from the rank tables are presented. By using this approach, an actionable plan for assisting decision maker can be obtained, as domain knowledge is incorporated in the system. Experimental results on a network dataset show that this model provides an optimal action plan for a network to improve its performance by encoding data-driven rules into the ontology and suggesting changes to its current network configuration.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An architecture for providing insights in a network domain to a decision maker, comprising: (a) a graphical user interface, comprising an experiments lookup service; (b) a rank table generation algorithm; (c) an insight rule generation algorithm; and (d) a triple store server, comprising: i. a network ontology; and ii. a reasoner; wherein the network ontology is built by capturing the semantics of the network. 2. The architecture of claim 1, wherein an output of the rank table generation algorithm is at least one rank table. 3. The architecture of claim 2, wherein the rank tables comprise summarized profiles of network configurations represented in the triple store. 4. The architecture of claim 2, wherein rank table serves as an input to the insight rule generation algorithm. 5. The architecture of claim 1, wherein an output of the insight rule generation algorithm is at least one insight rule. 6. The architecture of claim 5, wherein the insight rule is encoded in the network ontology. 7. A method for providing insights in a network domain to a decision maker, comprising: (a) building a wireless network ontology for the network domain; (b) applying a rank table generation algorithm; (c) inputting the rank tables into an insight rule generation algorithm; (d) applying a insight rule generation algorithm to generates insight rules; (e) encoding the insight rules into the ontology; (f) using a graphic user interface to operate an experiments lookup services; (g) selecting an experiment to perform; (h) submitting the chosen experiment; (i) invoking the reasoner, wherein the reasoner: i. enables an inference on the ontology by applying the encoded insight rule; ii. receives an inference from the ontology; and (j) displaying at least one inferred recommended configurable setting to the decision maker. 8. The method of claim 7, wherein applying a rank table generation algorithm comprises: (a) the rank table generation algorithm receiving input from the ontology and the decision maker, said input comprising: controllable parameters; uncontrollable parameters; performance metrics; and discretization of the parameters and performance metrics; (b) the rank table generation algorithm generates an output, comprising rank tables; and (c) using information in the rank tables to update the ontology. 9. The method of claim 7, wherein the rank table generation algorithm comprises: (a) providing an input; (b) providing an output, comprising a list of rank tables; (c) providing a list of Combination of Rank Table Names, wherein a Combination Rank Table Names List holds all possible combinations of uncontrollable parameter values; (d) providing a list of Combination of Each Row in Rank, wherein the Combination of Each Row in Rank list holds all possible combinations of the controllable parameters, with respect to the number of categories for each controllable parameter; (e) categorizing the performance metrics and controllable parameters of the experiment; (f) computing the sum of the performance metrics of each experiment to generate a rank value for each experiment; (g) computing an average of the rank values of all experiments under decision-maker selected controllable parameters of each rank table; (h) generating values for a Rank column of the rank tables; (i) generating a rank table for the combination rank table names, comprising the combination of each row in rank table and its rank; and (j) adding the rank table to the rank table list. 10. The method of claim 7, wherein the rank table generation algorithm comprises: (a) providing as inputs the following: i. providing a list of Combination of Rank Table Names, wherein a Combination Rank Table Names List holds all possible combinations of uncontrollable parameter values; ii. providing a list of Combination of Each Row in Rank, wherein the Combination of Each Row in Rank list holds all possible combinations of the controllable parameters, with respect to the number of categories for each controllable parameter; iii. categorizing the performance metrics and controllable parameters of the experiment; (b) computing the sum of the performance metrics of each experiment to generate a rank value for each experiment; (c) computing an average of the rank values of all experiments under decision-maker selected controllable parameters of each rank table; (d) providing outputs in the following: i. generating values for a Rank column of the rank tables; ii. generating a rank table for the combination rank table names, comprising the combination of each row in rank table and its rank; and (e) adding the rank table to the rank table list. 11. The method of claim 9, wherein the input comprises: (a) a network ontology; (b) a list of uncontrollable parameters; (c) a set of possible values for each uncontrollable parameter; (d) a list of controllable parameters; (e) a number of categories for each controllable parameters; and (f) a number of categories for each performance metric. 12. The method of claim 9, wherein the output comprises a list of rank tables. 13. The method of claim 9, wherein categorizing the performance metrics and controllable parameters of the experiment comprises the following steps: (a) for each Combination of Rank Table Names, retrieve all triples pertaining to the pertinent Combination of Rank Table Names by performing a SPARQLQUERY test; (b) for the retrieved triples, for each performance metric of the experiment in the Combination of Rank Table Names, categorize each into bins, wherein the number of bins corresponds to the number of performance metrics; (c) replacing the original performance metric values with a corresponding category label after discretization; (d) repeating the above categorizing process for each controllable parameter using the number of categories for each controllable parameter; (e) completing the experiments in each Combination of Rank Table Names; and (f) updating the ontology. 14. The method of claim 10, wherein categorizing the performance metrics and controllable parameters of the experiment comprises the following steps: (a) for each Combination of Rank Table Names, retrieve all triples pertaining to the pertinent Combination of Rank Table Names by performing a SPARQLQUERY test; (b) for the retrieved triples, for each performance metric of the experiment in the Combination of Rank Table Names, categorize each into bins, wherein the number of bins corresponds to the number of performance metrics; (c) replacing the original performance metric values with a corresponding category label after discretization; (d) repeating the above categorizing process for each controllable parameter using the number of categories for each controllable parameter; (e) completing the experiments in each Combination of Rank Table Names; and (f) updating the ontology. 15. The method of claim 9, wherein computing the sum of the performance metrics of each experiment to obtain a rank value for each experiment comprises the following steps: (a) for each Combination of Rank Table Names, retrieve all the triples pertaining to the pertinent Combination of Rank Table Names; and (b) for the received triples for each experiment compute the sum of all performance metrics to obtain the Rank of each experiment under decision-maker selected combination of controllable parameters that belongs to the rank table. 16. The method of claim 10, wherein computing the sum of the performance metrics of each experiment to obtain a rank value for each experiment comprises the following steps: (a) for each Combination of Rank Table Names, retrieve all the triples pertaining to the pertinent Combination of Rank Table Names; and (b) for the received triples for each experiment compute the sum of all performance metrics to obtain the Rank of each experiment under decision-maker selected combination of controllable parameters that belongs to the rank table. 17. The method of claim 15, wherein the triples are retrieved by performing a SPARQLQUERY. 18. The method of claim 16, wherein the triples are retrieved by performing a SPARQLQUERY. 19. The method of claim 7, wherein insight rule generation algorithm comprises: (a) an input, comprising a list of rank tables; (b) an output, comprising a list of insight rules; (c) performing insight extraction for each row in a rank table; and (d) generating at least one insight rule. 20. The method of claim 19, wherein performing insight extraction comprises the following steps: (a) applying the get_insight_rule(rk, ranktablei) function, which returns an optimal row rk′ based on the distances between rk and different combinations of controllable parameters with respect to the target rank category in the ranktablei; (b) construct an insight rule for row rk in rank table by comparing the values in rk and rk′; and (c) create list of insight rules. 21. A method for constructing an architecture for providing insights in a network domain, comprising: (a) constructing a Network Ontology, comprising: (b) computing the average of all metrics for each network configuration; (c) generating a rank table for each combination of uncontrollable parameters, wherein the rank of each combination is the summation of all performance metrics; wherein each performance metric is discretized; (d) updating the Network Ontology; (e) generating at least one insight rule, comprising: i. applying an insight rule generation algorithm on the rank tables; and ii. encoding the insight rule into the Network Ontology. 22. The method of claim 21, wherein construction of the Network Ontology comprises: (a) manually extracting semantics from a network dataset; (b) automatically extracting semantics from a relational database by breaking down relational data into Resource Description Framework triples; (c) identifying relationships between the different triples; and (d) forming an RDF schema; wherein the RDF schema stores network configuration parameters used in experiments; wherein the network configuration patterns are divided into two categories comprising: controllable parameters; and uncontrollable parameters.