Patent Publication Number: US-2018039522-A1

Title: Composite task processor

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This Application is a continuation application under 35 U.S.C. § 120 of U.S. patent application Ser. No. 14/647,701, filed on May 27, 2015, which is a U.S. National Stage filing under 35 U.S.C. § 371 of International Application No. PCT/CN2014/076770, filed on May 5, 2014. The disclosures of U.S. patent application Ser. No. 14/647,701 and International Application No. PCT/CN2014/076770 are hereby incorporated herein by reference in their entireties. 
    
    
     BACKGROUND 
     Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. 
     In a parallel processing arrangement, more than one central processing unit (CPU) or processor core may execute a program or multi computational threads. A parallel processing arrangement may execute instructions faster than serial processing. The program may be divided in such a way that separate CPUs or cores can execute different portions of the program without interfering with each other. 
     SUMMARY 
     In an example, methods effective to process a composite task to be applied to an ontology are described. The methods may include a processor receiving the composite task. The methods may include the processor transforming the composite task into a set of atomic tasks. The set of atomic tasks may include at least a first atomic task, a second atomic task, and a third atomic task. The methods may include the processor determining that the first atomic task is equivalent to the second atomic task based on the ontology. The methods may include the processor removing the second atomic task from the set of atomic tasks to generate a list of atomic tasks. The methods may include the processor applying the list of atomic tasks to the ontology. 
     In an example, methods effective to process a composite task to be applied to an ontology are described. The methods may include a processor receiving the composite task. The methods may include the processor transforming the composite task into a set of atomic tasks. The set of atomic tasks may include at least a first atomic task, a second atomic task, and a third atomic task. The methods may include the processor analyzing a semantic relationship between the first atomic task, the second atomic task and the third atomic task based on a semantic relation graph. The semantic relation graph may be based on the ontology. The methods may include the processor determining a first semantic relationship between the first atomic task and the second atomic task. The first atomic task may be more semantically restrictive than the second atomic task. The methods may include the processor determining a second semantic relationship between the second atomic task and the third atomic task. The second atomic task may be more semantically restrictive than the third atomic task. The methods may include the processor generating an ordered list of the first atomic task, the second atomic task and the third atomic task based on the determined first and second semantic relationships. 
     In an example, devices configured to process a composite task to be applied to an ontology are described. The devices may include a processor and a memory. The memory may include the ontology, a semantic relation graph, and instructions. The instructions, when executed by the processor, may cause the processor to receive the composite task. The instructions, when executed by the processor, may cause the processor to transform the composite task into a set of atomic tasks. The set of atomic tasks may include at least a first atomic task, a second atomic task, and a third atomic task. The instructions, when executed by the processor, may cause the processor to determine the first atomic task is equivalent to the second atomic task based on the semantic relation graph. The instructions, when executed by the processor, may cause the processor to remove the second atomic task from the set of atomic tasks to generate a list of atomic tasks. The instructions, when executed by the processor, may cause the processor to order the list of atomic tasks based on the semantic relation graph. The instructions, when executed by the processor, may cause the processor to apply the ordered list of atomic tasks to the ontology. 
     The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1  illustrates an example composite task processor system; 
         FIG. 2  illustrates the example composite task processor system of  FIG. 1  with additional details relating to atomic tasks; 
         FIG. 3  depicts a flow diagram for example processes to process composite tasks; 
         FIG. 4  illustrates computer program products configured to process composite tasks; and 
         FIG. 5  is a block diagram illustrating an example computing device that is arranged to process composite tasks, all arranged in accordance with at least some embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the Figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein. 
     This disclosure is generally drawn, inter alia, to methods, apparatus, systems, devices, and computer program products related to a composite task processor. 
     Briefly stated, technologies are generally described for systems, devices and methods effective to process a composite task to be applied to an ontology. The ontology may include nodes representing concepts, and links between the nodes representing relationships among the concepts. The methods may include a processor receiving a composite task. For example, a composite task may include multiple tasks for the processor to apply to the ontology. In an example, the ontology may relate to mathematics (or math) and the composite task may be a request to find the area of a complicated shape. The methods may include the processor transforming the composite task into a set of atomic tasks such as tasks to find the area of simpler shapes like circles. For example, the composite task may include multiple smaller tasks or atomic tasks to be applied to the ontology. The set of atomic tasks may include at least a first atomic task, a second atomic task, and a third atomic task. The methods may include the processor determining that the first atomic task is equivalent to the second atomic task based on the ontology. For example, the ontology may be a math ontology. The first atomic task and the second atomic task may both relate to determining the area of a circle. Based on a math ontology, the first and second atomic tasks may be determined to the equivalent. The methods may include the processor removing the second atomic task from the set of atomic tasks to generate a list of atomic tasks. For example, if the first atomic task is equivalent to the second atomic task, then only one of the atomic tasks may need to be applied to the ontology to process the composite task. The methods may include the processor applying the list of atomic tasks to the ontology. 
       FIG. 1  illustrates an example composite task processor system, arranged in accordance with at least some embodiments described herein. As discussed in more detail below, in some examples, a system  100  may include a processor  104  and a memory  106 . Processor  104  may be configured to be in communication with memory  106 . Memory  106  may include an ontology  130 , a semantic relation graph  140 , transformation instructions  108 , equivalence instructions  110 , implication list instructions  112 , and binary reasoning instructions  114 . Processor  104  may also be configured to be in communication with ontology  130  and semantic relation graph  140  over a network  136  if ontology  130  and semantic relation graph  140  are not stored in memory  106 . As discussed in more detail below, processor  104  may process composite task  102  and apply composite task  102  to ontology  130 . 
     In one example, processor  104  may receive one or more composite tasks  102 . Composite task  102  may include multiple reasoning requests or atomic tasks to be applied to ontology  130  by processor  104 . Processor  104  may process composite task  102  by executing transformation instructions  108  to transform composite task  102  into a set of atomic tasks  126 . Atomic tasks in set of atomic tasks  126  may be subset tasks of composite task  102  where each atomic task in set of atomic tasks  126  may be a task that may be difficult to further divide into smaller tasks. For example, if composite task  102  is “take care of dog”, atomic tasks in set of atomic tasks  126  may include; “feed dog”, “give dog water”, “walk dog”, “clean up after dog”, “groom dog”, etc. Processor  104  may transform composite task  102  into set of atomic tasks  126  such that atomic tasks in set of atomic tasks  126  are in standard form descriptive logic notations and symbols—explained in more detail below. 
     Processor  104  may execute equivalence instructions  110  on atomic tasks in set of atomic tasks  126 . In executing equivalence instructions  110 , processor  104  may utilize semantic relation graph  140  to determine atomic tasks in set of atomic tasks  126  which are semantically equivalent within semantic relation graph  140 . For example, the atomic tasks “feed the dog” and “give the dog dinner” may be semantically equivalent in semantic relation graph  140  and in ontology  130 . Semantic relation graph  140  may be constructed prior to processor  104  receiving composite task  102 , may be generated based on ontology  130  and may illustrate relationships between standard form descriptive logic notations and symbols in ontology  130 —explained in more detail below. Semantic relation graph  140  may reflect semantic relationships between the atomic tasks in set of atomic tasks  126 . 
     Processor  104  may generate a list of atomic tasks  128  from set of atomic tasks  126  by removing all but one of each atomic task in set of atomic tasks  126  determined to be equivalent. For example, if atomic task A and atomic task B are determined to be equivalent, processor  104  may remove all instances of atomic task A and atomic task B from set of atomic tasks  126 , except for one instance of atomic task A of atomic task B, to generate list of atomic tasks  128 . 
     Processor may execute implication list instructions  112  on list of atomic tasks  128 . In executing implication list instructions  112 , processor  104  may utilize semantic relation graph  140  to order list of atomic tasks  128  and generate ordered list of atomic tasks  132 . Ordered list of atomic tasks  132  may include atomic tasks that are related within semantic relation graph  140  such as one atomic task implying or being more semantically restrictive than another, related atomic task. For example, if an individual can legally drive a car, than this may imply that the individual has a license; and having a license may imply that the individual is over sixteen years old. In the previous example, ordered list of atomic tasks  132  may include: can legally drive a car→has a license→over sixteen years old. 
     Processor  104  may execute binary reasoning instructions  114  on ordered list of atomic tasks  132  to determine atomic tasks to apply to ontology  130 . For example, if the more semantically restrictive task “take care of dog” can be processed, then processor  104  need not thereafter process the less semantically restrictive task “feed the dog.” Processor  104 , executing binary reasoning instructions  114 , may select an atomic task from approximately the middle of ordered list of atomic tasks  132  to apply to ontology  130 . For example, if there are n atomic tasks in ordered list of atomic tasks  132 , processor  104  may select atomic task at position n/2 within ordered list of atomic tasks  132  where n/2 is an integer. If n/2 is not an integer, processor  104  may select atomic task at position (n+1)/2. Processor  104  may receive a response to applying the selected atomic task to ontology  130 . A response from applying the selected atomic task to ontology  130  may indicate whether processor  104  can execute the selected atomic task and less semantically restrictive tasks. Based on the response and binary reasoning instructions  114 , processor  104  may select a second atomic task from ordered list of atomic tasks  132  to apply to ontology  130 . Processor  104  executing binary reasoning instructions  114  and applying atomic tasks from ordered list of atomic tasks  132  to ontology  130 , may determine which atomic tasks within ordered list of atomic tasks  132  must be executed and applied to ontology  130  to execute composite task  102 . For example, if composite task  102  is to determine habits of drivers with low insurance rates, and binary reasoning instructions are executed on ordered list of atomic tasks  132  (can legally drive a car→has a license→over sixteen years old), then processor  104  may only apply the atomic task of determining whether an individual “can legally drive a car” to ontology  130 . A response to this atomic task may indicate that ontology  130  may be able to determine an individual can legally drive a car. As a consequence, the atomic tasks of determining an individual “has a license” and determining an individual is “over sixteen years of age” may not be applied to ontology  130  as these two atomic tasks are implied by the first atomic task of determining an individual “can legally drive a car.” Processor  104  may apply atomic tasks from ordered list of atomic tasks  132  with use of binary reasoning instructions  114  to ontology  130  to execute composite task  102 . Use of binary reasoning instructions  114  may result in less processing time, less processing memory, and less power than applying all atomic tasks in ordered list of atomic tasks  132  on ontology  130 . 
       FIG. 2  illustrates example composite task processor system  100  of  FIG. 1  with additional details relating to atomic tasks, arranged in accordance with at least some embodiments described herein.  FIG. 2  is substantially similar to  FIG. 1 , with additional details. Those components in  FIG. 2  that are labeled identically to components of  FIG. 1  will not be described again for the purposes of clarity. 
     In one example, processor  104  may receive composite task  102 . Composite task  102  may include multiple reasoning requests or atomic tasks to be applied to ontology  130  by processor  104 . Processor  104  may process composite task  102  by executing transformation instructions  108  to transform composite task  102  into set of atomic tasks  126 , including atomic task  216 , atomic task  218 , atomic task  220 , atomic task  222 , and atomic task  224 . 
     Ontology  130  may include standard form descriptive logic notations that may be used to model concepts, roles, and individuals, and the relationships therebetween. Ontology  130  may include axioms in descriptive logic notation relating concepts and/or roles within ontology  130 . Axioms within ontology  130  may denote semantic data or knowledge and links between axioms may denote relationships among axioms. Ontology  130 , may be looked at as an axiom set, and may be used to denote a semantic data set or a knowledge base. Ontology  130  may include descriptive logic notation that may include the standard logic notation of the Semantic Web. 
     Processor  104 , executing transformation instructions  108 , may transform composite task  102  into standard form descriptive logic notations. Composite task  102 , transformed into standard form descriptive logic notation may include inclusion axioms. Transformation instructions  108  may include instructions to transform inclusion axioms in composite task  102  into concepts. Transformation instructions  108  may include instructions to subsequently transform concepts in composite task  102 , including concepts derived from transforming inclusion axioms in composite task  102 , into a negation normal form. Transformation instructions  108  may include a negation normal form of a concept, for example, when negation occurs only in front of a concept name. Transformation instructions  108  may include instructions to subsequently transform the negation normal form concepts into a conjunction form. Transformation instructions  108  may include instructions to separate concepts or assertions connected with the conjunctions into different atomic tasks. 
     Processor  104  may further execute transformation instructions  108  to transform queries in composite task  102  into assertions. For example, a query in composite task  102  may be transformed into multiple assertions. Processor  104 , executing transformation instructions  108 , may separate the assertions transformed from queries in composite task  102  into atomic tasks to be applied to ontology  130 . Processor  104  may transform inclusion axioms, concepts, assertions, and queries in composite task  102  into set of atomic tasks  126 . 
     Semantic relation graph  140  may be constructed prior to processor  104  receiving composite task  102  and may be generated based on ontology  130 . Semantic relation graph  140 , based on ontology  130 , may illustrate relationships between standard form descriptive logic notations and symbols in ontology  130 . Semantic relation graph  140  may be generated by processor  104  and stored in memory  106 . Processor  104  may be configured to be in communication with semantic relation graph  140  over a network  136  if semantic relation graph  140  was not generated by processor  104  and/or is not stored in memory  106 . Semantic relation graph  140  may include semantic relationships between symbols of ontology  130 . Semantic relationships in semantic relation graph  140  may include semantic relations between pairs of symbols. For example, a first and a second symbol may be related in semantic relations graph  140  as equivalent, the first symbol may be more semantically restrictive than the second symbol, or the second symbol may be more semantically restrictive than the first symbol. Semantic relationships between symbols on an ontology may be used to determine relationships between atomic tasks in set of atomic tasks  126 . 
     Processor  104  or another processor may construct semantic relation graph  140  based on ontology  130 . A semantic relationship may be determined for all symbols within ontology  130 . The determined semantic relationships between all symbols of ontology  130  may be represented in semantic relation graph  140 . Semantic relation graph  140  may be constructed by determining a semantic relationship between a first symbol and a second symbol of ontology  130 , until every possible pair combination of symbols in ontology  130  is related on semantic relation graph  140 . In one example, processor  104  may check a semantic relationship between a first symbol and a second symbol. Processor  104  may determine the first symbol semantically equivalent to the second symbol. Processor  104  may determine the first symbol more semantically restrictive than the second symbol. Processor  104  may determine the second symbol more semantically restrictive than the first symbol. Processor  104  may graph the first and second symbols on semantic relation graph  140  with the determined semantic relationship. Processor  104  may continue to determine semantic relationships between pairs of symbols of ontology  130  until all pair combinations have been graphed on semantic relation graph  140 . Processor  104  may construct semantic relation graph  140  prior to receiving composite task  102 . 
     An example construction of a semantic relation graph  140  (“SG”) based on ontology  130  represented by O and first and second symbols represented by s i  and s j  is presented below. 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 Input: an ontology O; 
               
               
                   
                 Output: a graph SG; 
               
               
                   
                 1: for each pair (s i  and s j ) where s i , s j  ∈ O 
               
               
                   
                 2: check semantic relation between s i, s j  using the definition below 
               
               
                   
                 3: if s i  = s j  then 
               
               
                   
                 4: build s i     s j  into graph SG 
               
               
                   
                 5: else if s i  &gt;&gt; s j  then 
               
               
                   
                 6: build si →j into graph SG 
               
               
                   
                 7: if s i  &lt;&lt;  sj  then 
               
               
                   
                 8: build s i  ← s j  into SG 
               
               
                   
                   
               
            
           
         
       
     
     Reasoning for composite task  102  may be determined by the semantic interpretation of symbols within composite task  102 . A semantic interpretation for a symbol in ontology  130  may be determined by a syntactic definition of the symbol. Semantic relationships between symbols within ontology  130  may be based on the symbols syntactic definitions. 
                                                Definition:   Given ontology O, s 1  and s 2  are two symbols in ontology O                         Let A(s) denote all axioms which contain s in ontology O           A(s) s→s′  represents replacing symbols s with s′ for all axioms A(s)                 1. If A(s 1 ) s1→s2  = A(s 2 ), then s 1  and s 2  are semantically equivalent, denoted by s 1  == s 2.         2. If A(s 1 ) s1→s2    ⊃  A(s 2 ), then s 1  is more semantically restricted than s 2 , denoted by s 1  &lt;&lt;       s 2.         3. If A(s 1 ) s1→s2    ⊂  A(s 2 ), then s 2  is more semantically restricted than s 1 , denoted by s 1  &gt;&gt;       s 2.                      
This definition of the semantic relationships between two symbols of ontology  130  may be used to construct semantic relation graph  140 .
 
     Processor  104  may execute equivalence instructions  110  on atomic tasks  216 ,  218 ,  220 ,  222 , and  224 . For example, processor  104  may utilize semantic relation graph  140  to determine those of atomic tasks  216 ,  218 ,  220 ,  222 ,  224  which are semantically equivalent within semantic relation graph  140 . Semantic relation graph  140  may include semantic relationships among atomic tasks  216 ,  218 ,  220 ,  222 ,  224 . In an example, as shown at  230 , processor  104  may, by executing equivalence instructions  110 , utilize semantic relation graph  140  to determine that atomic task  216  is semantically equivalent to atomic task  222 . Processor  104  may generate list of atomic tasks  128  from set of atomic tasks  126  by removing all but one of atomic tasks  216  and  222  determined to be equivalent. Processor  104  may determine that list of atomic tasks  128  includes atomic tasks  216 ,  218 ,  220 , and  224 . 
     Processor  104  may execute implication instructions  112  on atomic tasks  216 ,  218 ,  220 , and  224  in list of atomic tasks  128  and may utilize semantic relation graph  140  to generate ordered list of atomic tasks  132 . Ordered list of atomic tasks  132  may include atomic tasks that imply other atomic tasks within semantic relation graph  140 . For example, processor  104  may utilize semantic relation graph  140  to generate ordered list of atomic tasks  132 —that includes atomic tasks  216 ,  218 ,  220 , and  224 . Semantic relation graph  140  may identify atomic task  216  as implying atomic task  220 , identify atomic task  220  as implying atomic task  224 , and identify atomic task  224  as implying atomic task  218 . Processor  104  may determine ordered list of atomic tasks  132  including atomic tasks  216 ,  218 ,  220 , and  224  as:  216 → 220 → 224 → 218 . 
     Processor  104  may execute binary reasoning instructions  114  on ordered list of atomic tasks  132 . Processor  104  may, by executing binary reasoning instructions  114  on ordered list of atomic tasks  132 , determine which of atomic tasks  216 ,  220 ,  224 ,  218  to apply to ontology  130 . Processor  104 , executing binary reasoning instructions  114 , may select an atomic task from approximately the middle of an ordered list of atomic tasks  132  to apply to ontology  130 . For example, ordered list of atomic tasks  132  may include ordered atomic tasks  216 ,  220 ,  224 ,  218  to apply to ontology  130 . Processor  104  may execute binary reasoning instructions  114  and apply atomic task  220  to ontology  130 . Processor  104  may receive a response to applying atomic task  220  to ontology  130 . A response from applying an atomic task to ontology  130  may indicate whether processor  104  can execute the atomic task on ontology  130 . Based on the response and binary reasoning instructions  114 , processor  104  may select a second atomic task from ordered list of atomic tasks  132  to apply to ontology  130 . For example, the response may indicate that atomic task  220  can be executed on ontology  130 . As a consequence, processor  104  may determine that atomic tasks  224  and  218  do not need to be applied to ontology  130  and may select atomic task  216  as the next atomic task to apply to ontology  130 . 
     Among other potential benefits, a system in accordance with the disclosure may take less time and use less processing resources to process a composite task. The system may avoid duplicate processing by identifying equivalent atomic tasks of a composite task and only process the equivalent atomic tasks once, saving processing time and other processing resources such as battery life, energy consumption, and memory requirements. The system may also avoid unnecessary processing by determining atomic tasks that do not need to be applied to the ontology based on the ordered list, thus lowing the number of atomic tasks to apply to the ontology and reducing processing time and resources. Concurrent reasoning tasks may be processed efficiently. 
     Experimental Data 
     Simulations were run using a descriptive logic reasoner (PELLET) and an application programmable interface (API) for ontology manipulation (OWLAPI) on several Lehigh University Benchmark (LUBM) ontologies. LUBM ontologies ranged from 10 5  axioms to 10 6  axioms with corresponding number of reasoning tasks from 10 4  to 10 6 . The syntax used was SHIF(D). 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                   
                   
                   
                 Total Size 
                 |reasoning 
               
               
                 Ontology 
                 Syntax 
                 |Axioms| 
                 (MB) 
                 tasks| 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 LUBM-Lite1 +10K   
                 SHIF(D) 
                 100,729 
                 8.03 
                 10 4   
               
               
                 LUBM-Lite10 +50K   
                   
                 1,001,738 
                 80.7 
                 5 × 10 4   
               
               
                 LUBM-Lite50 +100K   
                   
                 5,096,008 
                 697.2 
                 10 6   
               
               
                   
               
            
           
         
       
     
     All experiments were run on a 2.60 GHz PENTIUM-4 processor with 4GB of physical memory and with a maximum Java heap size set to 3072 MB for applying PELLET. First, a semantic relation graph  140  was constructed for each ontology  130 . Construction of semantic relation graph  140  may be done prior to receiving composite task to process and may be done off line. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Ontology 
                 Execution Time (s) 
                 Graph Size (MS) 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 LUBM-Lite1 
                 747 
                 8.78 
               
               
                   
                 LUBM-Lite10 
                 3,847 
                 90.7 
               
               
                   
                 LUBM-Lite50 
                 13,276 
                 901.2 
               
               
                   
                   
               
            
           
         
       
     
     Experimental results indicate construction time of dozens of minutes for ontology  130  of about 10 5  axioms (LUBM-Lite1) to several hours for ontology  130  of about 5×10 6  axioms (LUBM-Lite50). The experimental results illustrate the construction of semantic relation graph  140  may be completed in a reasonable amount of time. For ontology  130 , semantic relation graph  140  need only be constructed once, and may be constructed prior to receiving composite task  102  to process. Semantic relation graph  140  may be loaded into memory  106 . Semantic relation graph  140  may require slightly more memory than ontology  130  from which semantic relation graph  140  is constructed. 
     
       
         
           
               
               
             
               
                   
                 TABLE 3 
               
             
            
               
                   
                   
               
               
                   
                 Total Execution Time (s) 
               
            
           
           
               
               
               
               
            
               
                   
                 Non-optimized 
                 Present Method 
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 Ontology 
                 method 
                 Ordered 
                 Binary 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 LUBM-Lite1 +10K   
                 6.2 
                 0.6 
                 2.1 
               
               
                   
                 LUBM-Lite10 +50K   
                 150.6 
                 12.1 
                 20.3 
               
               
                   
                 LUBM-Lite50 +100K   
                 2,063.5 
                 50.6 
                 232.5 
               
               
                   
                   
               
            
           
         
       
     
     Table 3 illustrates the experimental amount of time to process composite task  102  for the three ontologies  130  by standard processing of the entire composite task (Non-optimized method) and the present method. Table 3 includes columns for amounts of time to generate ordered lists and binary reasoning. As shown, the present method completed composite task  102  in significantly less time. For ontology  130  with 10 5  axioms (LUBM-Litel +10K ) processing time was decreased to about half and for ontology  130  with 10 6  axioms (LUBM-Lite50 +100K ) processing time was reduced by over 85%. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 4 
               
             
            
               
                   
                   
               
               
                   
                 Actual Number of Tasks 
                   
               
            
           
           
               
               
               
               
            
               
                   
                 Ontology 
                 Non-optimized method 
                 Present 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                   
                 LUBM-Lite1 +10K   
                 10,000 
                 2,078 
               
               
                   
                 LUBM-Lite10 +50K   
                 50,000 
                 7,836 
               
               
                   
                 LUBM-Lite50 +100K   
                 100,000 
                 13,765 
               
               
                   
                   
               
            
           
         
       
     
     Table 4 illustrates the number of tasks applied to ontology  130  to process composite task  102 . As shown, the number of tasks applied to ontology  130  is significantly reduced by the present method. For ontology  130  with 10 5  axioms (LUBM-Lite1 +10K ) the number of tasks to apply to ontology  130  was decreased by just under 80% for ontology  130  with 10 6  axioms (LUBM-Lite50 +100K ) the number of tasks to apply to ontology  130  was decreased by over 86%. 
       FIG. 3  illustrates a flow diagram for example processes to process composite tasks, arranged in accordance with at least some embodiments presented herein. The process in  FIG. 3  could be implemented using, for example, system  100  discussed above. An example process may include one or more operations, actions, or functions as illustrated by one or more of blocks S 2 , S 4 , S 6 , S 8  and/or S 10 . Although illustrated as discrete blocks, various blocks may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the desired implementation. 
     Processing may begin at block S 2 , “Receive the composite task.” At block S 2 , the processor may receive the composite task. The composite task may include multiple reasoning requests or atomic tasks to be applied to an ontology by the processor. 
     Processing may continue from block S 2  to block S 4 , “Transform the composite task into a set of atomic tasks, the set of atomic tasks including at least a first atomic task, a second atomic task, and a third atomic task.” At block S 4 , the processor may transform the composite task into a set of atomic tasks. The set of atomic tasks may include at least a first atomic task, a second atomic task, and a third atomic task. The atomic tasks in the set of atomic tasks may be subset tasks of the composite task which may be difficult to divide into smaller tasks. The processor may transform the composite task into the set of atomic tasks such that the atomic tasks in the set of atomic tasks are in standard form descriptive logic notations and symbols. 
     Processing may continue from block S 4  to block S 6 , “Determine that the first atomic task is equivalent to the second atomic task based on the ontology.” At block S 6 , the processor may determine that the first atomic task is equivalent to the second atomic task. The determination may be based on the ontology. The processor may utilize a semantic relationship graph to determine atomic tasks in the set of atomic tasks which are semantically equivalent within the semantic relation graph. The semantic relation graph may be constructed prior to the processor receiving the composite task. The semantic relation graph may be generated based on the ontology and may illustrate relationships between standard form descriptive logic notations and symbols in the ontology. The semantic relationship graph may include semantic relationships between atomic tasks of the ontology. 
     Processing may continue from block S 6  to block S 8 , “Remove the second atomic task from the set of atomic tasks to generate a list of atomic tasks.” At block S 8 , the processor may remove the second atomic task from the set of atomic tasks to generate a list of atomic tasks. The processor may generate the list of atomic tasks from the set of atomic tasks by removing all but one of each atomic task in the set of atomic tasks determined to be equivalent. 
     Processing may continue from block S 8  to block S 10 , “Apply the list of atomic tasks to the ontology.” At block S 10 , the processor may apply the list of atomic tasks to the ontology. The processor may utilize the semantic relationship graph to order the list of atomic tasks to generate an ordered list of atomic tasks prior to applying the list of atomic tasks to the ontology. The ordered list of atomic tasks may include atomic tasks that are related within the semantic relationship graph such as one atomic task implying another atomic task. The processor may select an atomic task from approximately the middle of the ordered list of atomic tasks to apply to the ontology. The processor may receive a response to applying the selected atomic task to the ontology. A response from applying the atomic task to the ontology may indicate whether the ontology can execute the atomic task. Based on the response, the processor may select a second atomic task from the ordered list of atomic tasks to apply to the ontology. The processor may determine atomic tasks within the ordered list of atomic tasks that should be applied to the ontology to execute the composite task. 
       FIG. 4  illustrates computer program products  400  configured to process composite tasks, arranged in accordance with at least some embodiments presented herein. Computer program product  400  may include a signal bearing medium  402 . Signal bearing medium  402  may include one or more instructions  404  that, when executed by, for example, a processor, may provide the functionality described above with respect to  FIGS. 1-3 . Thus, for example, referring to system  100 , processor  104  may undertake one or more of the blocks shown in  FIG. 4  in response to instructions  404  conveyed to the system  100  by signal bearing medium  402 . 
     In some implementations, signal bearing medium  402  may encompass a computer-readable medium  406 , such as, but not limited to, a hard disk drive, a compact disc (CD), a digital video disk (DVD), a digital tape, memory, etc. In some implementations, signal bearing medium  402  may encompass a recordable medium  408 , such as, but not limited to, memory, read/write (R/W) CDs, R/W DVDs, etc. In some implementations, signal bearing medium  402  may encompass a communications medium  410 , such as, but not limited to, a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communication link, a wireless communication link, etc.). Thus, for example, computer program product  400  may be conveyed to one or more modules of the system  100  by an RF signal bearing medium  402 , where the signal bearing medium  402  is conveyed by a wireless communications medium  410  (e.g., a wireless communications medium conforming with the IEEE 802.11 standard). 
       FIG. 5  is a block diagram illustrating an example computing device  500  that is arranged to process composite tasks, arranged in accordance with at least some embodiments presented herein. In a very basic configuration  502 , computing device  500  typically includes one or more processors  504  and a system memory  506 . A memory bus  508  may be used for communicating between processor  504  and system memory  506 . 
     Depending on the desired configuration, processor  504  may be of any type including but not limited to a microprocessor (μP), a microcontroller (μC), a digital signal processor (DSP), or any combination thereof. Processor  504  may include one or more levels of caching, such as a level one cache  510  and a level two cache  512 , a processor core  514 , and registers  516 . An example processor core  514  may include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. An example memory controller  518  may also be used with processor  504 , or in some implementations memory controller  518  may be an internal part of processor  504 . 
     Depending on the desired configuration, system memory  506  may be of any type including but not limited to volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.) or any combination thereof. System memory  506  may include an operating system  520 , one or more applications  522 , and program data  524 . 
     Application  522  may include a composite tasks processing algorithm  526  that is arranged to perform the functions as described herein including those described previously with respect to  FIGS. 1-4 . Program data  524  may include composite tasks processing data  528  that may be useful for processing of composite tasks as is described herein. In some embodiments, application  522  may be arranged to operate with program data  524  on operating system  520  such that processing of composite tasks may be provided. This described basic configuration  502  is illustrated in  FIG. 5  by those components within the inner dashed line. 
     Computing device  500  may have additional features or functionality, and additional interfaces to facilitate communications between basic configuration  502  and any required devices and interfaces. For example, a bus/interface controller  530  may be used to facilitate communications between basic configuration  502  and one or more data storage devices  532  via a storage interface bus  534 . Data storage devices  532  may be removable storage devices  536 , non-removable storage devices  538 , or a combination thereof. Examples of removable storage and non-removable storage devices include magnetic disk devices such as flexible disk drives and hard-disk drives (HDD), optical disk drives such as compact disk (CD) drives or digital versatile disk (DVD) drives, solid state drives (SSD), and tape drives to name a few. Example computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. 
     System memory  506 , removable storage devices  536  and non-removable storage devices  538  are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by computing device  500 . Any such computer storage media may be part of computing device  500 . 
     Computing device  500  may also include an interface bus  540  for facilitating communication from various interface devices (e.g., output devices  542 , peripheral interfaces  544 , and communication devices  546  ) to basic configuration  502  via bus/interface controller  530 . Example output devices  542  include a graphics processing unit  548  and an audio processing unit  550 , which may be configured to communicate to various external devices such as a display or speakers via one or more A/V ports  552 . Example peripheral interfaces  544  include a serial interface controller  554  or a parallel interface controller  556 , which may be configured to communicate with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, etc.) or other peripheral devices (e.g., printer, scanner, etc.) via one or more I/O ports  558 . An example communication device  546  includes a network controller  560 , which may be arranged to facilitate communications with one or more other computing devices  562  over a network communication link via one or more communication ports  564 . 
     The network communication link may be one example of a communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and may include any information delivery media. A “modulated data signal” may be a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), microwave, infrared (IR) and other wireless media. The term computer readable media as used herein may include both storage media and communication media. 
     Computing device  500  may be implemented as a portion of a small-form factor portable (or mobile) electronic device such as a cell phone, a personal data assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions. Computing device  500  may also be implemented as a personal computer including both laptop computer and non-laptop computer configurations. 
     The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various aspects. Many modifications and variations can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the appended claims. The present disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. It is to be understood that this disclosure is not limited to particular methods, reagents, compounds compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. 
     With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity. 
     It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation, no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general, such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general, such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C″ would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.” 
     As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as “up to,” “at least,” “greater than,” “less than,” and the like include the number recited and refer to ranges which can be subsequently broken down into subranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to groups having 1, 2, 3, 4, or 5 cells, and so forth. 
     While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.