Commit 6870fc64 authored by Alberto Monge's avatar Alberto Monge
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code for recrules

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# RecRules - a Hybrid Semantic Recommender System of IF-THEN Rules
RecRules is a hybrid semantic recommender system of IF-THEN rules. Through a mixed content and collaborative approach, the goal of RecRules is to recommend rules by functionality: it suggests rules based on their final purposes, thus overcoming details like manufacturers and brands. The algorithm uses a semantic reasoning process to enrich rules with semantic information, with the aim of uncovering hidden connections between rules in terms of shared functionality. Then, it builds a collaborative semantic graph, and it exploits different types of path-based features to train a learning to rank algorithm and compute top-N recommendations.
Currently, RecRules support [IFTTT](https://ifttt.com/) rules by leveraging on the [EUPont](http://elite.polito.it/ontologies/eupont-ifttt.owl) ontology.
The algorithm is implemented in Java, and it exploits [lodreclib](https://github.com/sisinflab/lodreclib).
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RECRULES EVALUATION
# RULES = 4015
# USERS = 144
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #1 RANDOM FOREST RDF ONLY [EXPLICIT FEEDBACK]
DATA EXTRACTION = 2 s
ITEM PATHS EXTRACTION = 108 s
# ITEM PATHS = 1543990
USER PATHS EXTRACTION = 10 s
LEARNING = 89 s
COMPUTE RECOMMENDATIONS = 15 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,2605 0,08649 0,42713 0,20933 0 0,01843 0,01835 0,00437
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,11261 0,1896 0,49569 0,6978 1 0,06102 0,01101 0,01267
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,07479 0,24497 0,58091 1,10586 1 0,10162 0,00826 0,01582
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,02992 0,24497 0,42149 1,10586 1 0,10162 0,0033 0,01582
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,01496 0,24497 0,31138 1,10586 1 0,10162 0,00165 0,01582
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00748 0,24497 0,21708 1,10586 1 0,10162 0,00083 0,01582
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,00299 0,24497 0,1236 1,10586 1 0,10162 0,00033 0,01582
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #2 RANDOM FOREST RDF (including technologies) & OWL [EXPLICIT FEEDBACK] [BEST]
DATA EXTRACTION = 33 s
ITEM PATHS EXTRACTION = 588 s
# ITEM PATHS = 6816811
USER PATHS EXTRACTION = 76 s
LEARNING = 149 s
COMPUTE RECOMMENDATIONS = 24 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,13445 0,03912 0,35054 0,2254 0 0,01469 0,00917 0,00306
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,11092 0,18676 0,4852 0,85989 1 0,05455 0,00183 0,00306
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,08235 0,30651 0,58168 1,30256 1 0,10162 0,00367 0,00981
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,03294 0,30651 0,42166 1,30256 1 0,10162 0,00147 0,00981
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,01647 0,30651 0,31133 1,30256 1 0,10162 0,00073 0,00981
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00824 0,30651 0,21693 1,30256 1 0,10162 0,00037 0,00981
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,00329 0,30651 0,12346 1,30256 1 0,10162 0,00015 0,00981
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #3 RANDOM FOREST RDF (without technologies) & OWL [EXPLICIT FEEDBACK]
DATA EXTRACTION = 36 s
ITEM PATHS EXTRACTION = 312 s
# ITEM PATHS = 6774040
USER PATHS EXTRACTION = 36 s
LEARNING = 122 s
COMPUTE RECOMMENDATIONS = 22 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,15966 0,04335 0,36409 0,22647 0 0,01445 0,01835 0,00408
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,09748 0,14335 0,47224 0,84031 1 0,06102 0,0055 0,00418
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,07311 0,2687 0,57204 1,24916 1 0,11731 0,00642 0,01195
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,02924 0,2687 0,41507 1,24916 1 0,11731 0,00257 0,01195
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,01462 0,2687 0,30665 1,24916 1 0,11731 0,00128 0,01195
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00731 0,2687 0,21379 1,24916 1 0,11731 0,00064 0,01195
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,00292 0,2687 0,12174 1,24916 1 0,11731 0,00026 0,01195
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #4 RANDOM FOREST RDF ONLY [IMPLICIT FEEDBACK]
DATA EXTRACTION = 1 s
ITEM PATHS EXTRACTION = 105 s
# ITEM PATHS = 1543990
USER PATHS EXTRACTION = 9 s
LEARNING = 88 s
COMPUTE RECOMMENDATIONS = 15 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,22222 0,02296 0,20116 0,21061 0 0,02242 0 0
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,09583 0,05386 0,14923 0,7916 1 0,0807 0 0
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,06806 0,07845 0,15964 1,3597 1 0,13823 0 0
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,02722 0,07845 0,15833 1,3597 1 0,13823 0 0
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,01361 0,07845 0,15818 1,3597 1 0,13823 0 0
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00681 0,07845 0,15816 1,3597 1 0,13823 0 0
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,00272 0,07845 0,15816 1,3597 1 0,13823 0 0
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #5 RANDOM FOREST RDF (including technologies) & OWL [IMPLICIT FEEDBACK]
DATA EXTRACTION = 31 s
ITEM PATHS EXTRACTION = 555 s
# ITEM PATHS = 6816811
USER PATHS EXTRACTION = 33 s
LEARNING = 155 s
COMPUTE RECOMMENDATIONS = 23 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,14583 0,01405 0,13911 0,22157 0 0,01818 0 0
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,10139 0,06034 0,14565 0,89645 1 0,07148 0 0
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,07222 0,09353 0,16511 1,49468 1 0,12354 0 0
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P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
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P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,00289 0,09353 0,16351 1,49468 1 0,12354 0 0
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #6 RANDOM FOREST RDF (without technologies) & OWL [IMPLICIT FEEDBACK]
DATA EXTRACTION = 29 s
ITEM PATHS EXTRACTION = 319 s
# ITEM PATHS = 6774040
USER PATHS EXTRACTION = 29 s
LEARNING = 116 s
COMPUTE RECOMMENDATIONS = 21 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,16667 0,01846 0,14606 0,20918 0 0,01918 0 0
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,09444 0,0529 0,13062 0,89265 1 0,07646 0 0
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,07639 0,09996 0,16502 1,48526 1 0,13998 0 0
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,03056 0,09996 0,16376 1,48526 1 0,13998 0 0
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,01528 0,09996 0,16362 1,48526 1 0,13998 0 0
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00764 0,09996 0,1636 1,48526 1 0,13998 0 0
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,00306 0,09996 0,1636 1,48526 1 0,13998 0 0
RECRULES EVALUATION
# RULES = 4015
# USERS = 144
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #1 RANDOM FOREST RDF ONLY [EXPLICIT FEEDBACK]
DATA EXTRACTION = 2 s
ITEM PATHS EXTRACTION = 108 s
# ITEM PATHS = 1543990
USER PATHS EXTRACTION = 10 s
LEARNING = 89 s
COMPUTE RECOMMENDATIONS = 15 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,2605 0,08649 0,42713 0,20933 0 0,01843 0,01835 0,00437
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,11261 0,1896 0,49569 0,6978 1 0,06102 0,01101 0,01267
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,07479 0,24497 0,58091 1,10586 1 0,10162 0,00826 0,01582
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,02992 0,24497 0,42149 1,10586 1 0,10162 0,0033 0,01582
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,01496 0,24497 0,31138 1,10586 1 0,10162 0,00165 0,01582
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00748 0,24497 0,21708 1,10586 1 0,10162 0,00083 0,01582
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,00299 0,24497 0,1236 1,10586 1 0,10162 0,00033 0,01582
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #2 RANDOM FOREST RDF (including technologies) & OWL [EXPLICIT FEEDBACK]
DATA EXTRACTION = 33 s
ITEM PATHS EXTRACTION = 588 s
# ITEM PATHS = 6816811
USER PATHS EXTRACTION = 76 s
LEARNING = 149 s
COMPUTE RECOMMENDATIONS = 24 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,13445 0,03912 0,35054 0,2254 0 0,01469 0,00917 0,00306
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,11092 0,18676 0,4852 0,85989 1 0,05455 0,00183 0,00306
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,08235 0,30651 0,58168 1,30256 1 0,10162 0,00367 0,00981
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,03294 0,30651 0,42166 1,30256 1 0,10162 0,00147 0,00981
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,01647 0,30651 0,31133 1,30256 1 0,10162 0,00073 0,00981
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00824 0,30651 0,21693 1,30256 1 0,10162 0,00037 0,00981
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,00329 0,30651 0,12346 1,30256 1 0,10162 0,00015 0,00981
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #3 RANKBOOST RDF ONLY [EXPLICIT FEEDBACK]
DATA EXTRACTION = 1 s
ITEM PATHS EXTRACTION = 106 s
# ITEM PATHS = 1543990
USER PATHS EXTRACTION = 8 s
LEARNING = 54 s
COMPUTE RECOMMENDATIONS = 1 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,03361 0,0124 0,26976 0,06102 0 0,01968 0 0
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,02521 0,04588 0,39432 0,24888 1 0,06625 0,0055 0,01162
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,01765 0,06198 0,48749 0,40179 1 0,11158 0,00734 0,01805
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,00706 0,06198 0,3506 0,40179 1 0,11158 0,00294 0,01805
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,00353 0,06198 0,25747 0,40179 1 0,11158 0,00147 0,01805
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00176 0,06198 0,17859 0,40179 1 0,11158 0,00073 0,01805
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,00071 0,06198 0,10119 0,40179 1 0,11158 0,00029 0,01805
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #4 RANKBOOST RDF (including technologies) & OWL [EXPLICIT FEEDBACK]
DATA EXTRACTION = 30 s
ITEM PATHS EXTRACTION = 558 s
# ITEM PATHS = 6816811
USER PATHS EXTRACTION = 33 s
LEARNING = 89 s
COMPUTE RECOMMENDATIONS = 4 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,07563 0,03011 0,28996 0,09669 0 0,01868 0,00917 0,00071
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,04202 0,07367 0,40438 0,38142 1 0,07796 0,00734 0,00282
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,03193 0,11835 0,501 0,6007 1 0,13973 0,00642 0,00703
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,01277 0,11835 0,36058 0,6007 1 0,13973 0,00257 0,00703
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,00639 0,11835 0,26495 0,6007 1 0,13973 0,00128 0,00703
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00319 0,11835 0,18387 0,6007 1 0,13973 0,00064 0,00703
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,00128 0,11835 0,10423 0,6007 1 0,13973 0,00026 0,00703
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #5 LAMBDAMART RDF ONLY [EXPLICIT FEEDBACK]
DATA EXTRACTION = 1 s
ITEM PATHS EXTRACTION = 109 s
# ITEM PATHS = 1543990
USER PATHS EXTRACTION = 10 s
LEARNING = 6 s
COMPUTE RECOMMENDATIONS = 5 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,01681 0,00457 0,26108 0,1481 0 0,001 0 0
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,01345 0,02152 0,38614 0,68478 1 0,00324 0 0
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,01261 0,03939 0,48216 1,1294 1 0,00648 0 0
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,00504 0,03939 0,3467 1,1294 1 0,00648 0 0
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,00252 0,03939 0,25488 1,1294 1 0,00648 0 0
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00126 0,03939 0,177 1,1294 1 0,00648 0 0
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
0,0005 0,03939 0,10046 1,1294 1 0,00648 0 0
------------------------------------------------------------------------------------------------------------------------------------------------------------
TRIAL #6 LAMBDAMART RDF (including technologies) & OWL [EXPLICIT FEEDBACK]
DATA EXTRACTION = 35 s
ITEM PATHS EXTRACTION = 606 s
# ITEM PATHS = 6816811
USER PATHS EXTRACTION = 34 s
LEARNING = 16 s
COMPUTE RECOMMENDATIONS = 6 s
P@1 R@1 nDCG@1 EBN@1 ILD@1 ItemCov@1 BAD_P@1 BAD_R@1
0,01681 0,00457 0,26108 0,14315 0 0,00075 0 0
P@5 R@5 nDCG@5 EBN@5 ILD@5 ItemCov@5 BAD_P@5 BAD_R@5
0,01345 0,02152 0,38614 0,72476 1 0,00224 0 0
P@10 R@10 nDCG@10 EBN@10 ILD@10 ItemCov@10 BAD_P@10 BAD_R@10
0,01261 0,03939 0,48206 1,13645 1 0,00374 0 0
P@25 R@25 nDCG@25 EBN@25 ILD@25 ItemCov@25 BAD_P@25 BAD_R@25
0,00504 0,03939 0,34664 1,13645 1 0,00374 0 0
P@50 R@50 nDCG@50 EBN@50 ILD@50 ItemCov@50 BAD_P@50 BAD_R@50
0,00252 0,03939 0,25483 1,13645 1 0,00374 0 0
P@100 R@100 nDCG@100 EBN@100 ILD@100 ItemCov@100 BAD_P@100 BAD_R@100
0,00126 0,03939 0,17697 1,13645 1 0,00374 0 0
P@250 R@250 nDCG@250 EBN@250 ILD@250 ItemCov@250 BAD_P@250 BAD_R@250
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<?xml version="1.0" encoding="UTF-8"?>
<props>
<prop name="http://elite.polito.it/ontologies/eupont.owl#hasTrigger">
<prop name="http://elite.polito.it/ontologies/eupont.owl#offerTrigger">
<prop name="http://elite.polito.it/ontologies/eupont.owl#hasCategory"></prop>
</prop>
</prop>
<!--<prop name="triggerFunctionality">
<prop name="owlSubClassOf">
<prop name="owlSubClassOf">
<prop name="owlSubClassOf">
</prop>
</prop>
</prop>
</prop>-->
<prop name="http://elite.polito.it/ontologies/eupont.owl#hasAction">
<prop name="http://elite.polito.it/ontologies/eupont.owl#offerAction">
<prop name="http://elite.polito.it/ontologies/eupont.owl#hasCategory"></prop>
</prop>
</prop>
<!--<prop name="actionFunctionality">
<prop name="owlSubClassOf">
<prop name="owlSubClassOf">
<prop name="owlSubClassOf">
</prop>
</prop>
</prop>
</prop> -->
</props>
<?xml version="1.0" encoding="UTF-8"?>
<props>
<prop name="http://elite.polito.it/ontologies/eupont.owl#hasTrigger">
<prop name="http://elite.polito.it/ontologies/eupont.owl#offerTrigger">
<prop name="http://elite.polito.it/ontologies/eupont.owl#hasCategory"></prop>
</prop>
</prop>
<prop name="triggerFunctionality">
<prop name="owlSubClassOf">
<prop name="owlSubClassOf">
<prop name="owlSubClassOf">
</prop>
</prop>
</prop>
</prop>
<prop name="http://elite.polito.it/ontologies/eupont.owl#hasAction">
<prop name="http://elite.polito.it/ontologies/eupont.owl#offerAction">
<prop name="http://elite.polito.it/ontologies/eupont.owl#hasCategory"></prop>
</prop>
</prop>
<prop name="actionFunctionality">
<prop name="owlSubClassOf">
<prop name="owlSubClassOf">
<prop name="owlSubClassOf">
</prop>
</prop>
</prop>
</prop>
</props>
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