From Dynamic to Unbalanced Ontology Matching

Transcription

1 From Dynamic to Unbalanced Ontology Matching Jie Tang Knowledge Engineering Group, Dept. of Computer Science and Technology Tsinghua University May 22 th

2 What is Ontology Matching? 本体 O 本体 2 1 O Object Thing Washington_course Cornell_course College_of_Arts_and_Sciences Asian_Studies Asian_Languages_and_Literature Linguistics College_of_Arts_and_Sciences Linguistics French_Linguistics_FRLING Linguistics_LING Romance_Linguistics_ROLING Spanish_Linguistics_SPLING 2

3 Ontology Matching attr n attr n attr 1 attr n inst1 inst1 inst1 inst1 inst1 inst1 inst1 3

4 Problem Definition Matching Function: Map ({ ei1}, O1, O2 ) { ei2} Cardinality O 1 O 2 Mapping Expression 1:1 Faculty Academic staff O 1.Faculty= O 2.Academic staff 1:n Name First name, Last name O 1.Name= O 2.First name+o 2.Last name n:1 Cost, Tax ratio Price O 1.Cost*(1+ O 1. Tax ratio)= O 2.Price 1:null null:1 n:m AI BookTitle, BookaNo, PublisherNo, PublisherName AI Book, Publisher O 1.BookTitle + O 1.BookaNo + O 1.PublisherNo + O 1.PublisherName = O 2.Book + O 2.Publisher 4

5 Ontology Matching Our work RiMOM: Risk minimization based approach Jie Tang, et al. Journal of Web Semantics. 2006, Dec. (JoWS, IF:3.41) Dynamic ontology matching framework Juanzi Li, Jie Tang, et al. TKDE, 2009 Unbalanced ontology matching Qian Zhong, Hanyu Li, Juanzi Li, Guotong Xie, Jie Tang. SIGMOD

6 RiMOM A tool for ontology matching OAEI( ):an international contest on ontology alignment Benchmark Results Precsion Recall F-measure Anatomy Results Precision 0.2 Recall 0 Recall+ F-measure 6 Msg from agrafsa Chair: I m Subtrack really Results surprised 1 by the good results of 0.8 these 0.6 years RiMOM, you can 0.4 compete 0.2 with the top systems 0 that make use of such background knowledge. Precision

7 RiMOM A tool for ontology matching 7

8 Outline Dynamic Multi-strategy Ontology Matching Unbalanced Ontology Matching Discussion 8

9 A Dynamic Multi-strategy Ontology Alignment Framework Matching = Multi-strategies + Strategy selection Concept/Attribute name Concept/Attribute path F _ Concept/Attribute s description Instance Structure F _ SS - Linguistic similarity factor Structural similarity factor Associate a loss for each candidate matching Strategy selection: determine if we should use the strategies LS # same _ label max(# c,# c ) # common _ concept max(# nonleaf _ c,# nonleaf _ c ) 1 2 9

10 A General Processing Flow Similarity factor Strategy pool 10

11 Multiple Strategies Schema O Schema O2 1 Object Thing Washington_course O1 O2 Cornell_course College_of_Arts_and_Sciences Asian_Studies Asian languages KOREAN Asian studies Asian_Languages_and_Literature College_of_Arts_and_Sciences Korean xyz Linguistics Thai Linguistics CHIN HINDI THAI Thai French_Linguistics_FRLING Linguistics_LING Hindi Romance_Linguistics_ROLING Spanish_Linguistics_SPLING Concept name: similarity(washington_course, cornell_course) Concept path: similarity(/object/washington_course, /thing/cornell_course) Concept description: classifier = train(o 2 ) and classify (O 1, classifier) Instance: classifier = train(o 2 ) and classify (O 1, classifier) Structure: taxonomy information. E.g. Hypernyms and Hyponyms 11

12 Multiple Linguistic Strategies Edit distance on entity s label label Conference description Conferece The location of an event, An event presenting work WordNet: Vector-based similarity Query vector Doc1 vector Doc3 vector instances Spg04 (label:) SemPGrid 04 Workshop (name:) SemPGrid 04 Workshop (location:) New-York NY US (date:) Doc4 vector 12

13 Similarity Propagation Ontology 1 Ontology 2 Thing Object Thing Object subclassof subclassof subclassof Reference Address Entry Directions Reference Directions Reference Entry Address Direction Address Entry hasproperty range hasproperty range hasproperty range location place location place The construction of an intermediate graph from original ontologies 13

14 Similarity Propagation (cont.) Propagate similarities along edges Three types of edges: Class to Class (CCP) Class to Property (CPP) Property to Property (PPP) 0.3 Reference Directions 0.7 Reference Entry hasproperty * *0.5=1.4 Thing Object subclassof location place Address Direction range Address Entry weight=0.5 14

15 Strategy Pool Strategy pool Edit-distance Sim = 1-ED(label 1, label 2 ) Vector-similarity weighted vector generation content feature structure feature cosine similarity Background-knowledge external knowledge similarity definition Similarity-combination Map e, e 1 2 k 1... n k k 1... n w Map e, e w k k 1 2 Path-similarity entity path path similarity definition Similarity-propagation three propagation strategies CCP, PPP, CPP 15

16 Strategy Selection Similarity factor Label similarity factor Ontology 1 Ontology 2 Part Part F _ LS # same _ label max(# c,# c ) 1 2 Chapter InBook Chapter InBook InCollection InCollection Structure similarity factor InProceedings JournalPart InProceedings Article F _ SS # common _ concept max(# nonleaf _ c,# nonleaf _ c ) 1 2 Article Review F_LS = 6/10 Editorial F_SS = 1/2 Letter max(#c 1, #c 2 ) = 10 max(#nonleaf_c 1, #nonleaf_c 2 ) = 2 17

17 Strategy Selection Strategy Selection Selection with the two similarity factors Determining whether a strategy is to be used in the alignment process E.g. if F_SS>0.25, we use CCP, CPP, and PPP for propagation. Linguistic Strategy Adding structural features in vector-based similarity 18

18 Outline Dynamic Multi-strategy Ontology Matching Experimental Results Unbalanced Ontology Matching Discussion 20

19 OAEI 2006 Data Sets Benchmark (15-69), 53 alignment tasks Directory: (4,500), Yahoo and ODP Food: (16,000 vs. 41,000), two SKOS thesaurus OAEI 2007 Comparison methods 21

20 Statistics on the Data Set 22 Data set Ontology #concept #attribute Benchmark #alignment (ground truth) #instance Reference Ontology

21 23 Similarity between Ontologies

22 24 Results on OAEI2006

23 25 RiMOM vs. RiMOM-SP

24 26 RiMOM vs. RiMOM-SS

25 Relationship with Several Classical Methods 27

26 28 Results on OAEI 2006

27 Results on OAEI2006 Directory Food 29

28 30 Results on OAEI 2007

29 Result on OAEI Benchmark Results Precsion Recall F-measure Anatomy Results Precision 0.2 Recall 0 Recall+ F-measure agrafsa Subtrack Results Precision 31

30 Experiences Structure information is very important in many alignment tasks for achieving high performance An effective method for combining the multiple strategies can enhance alignment performance Investigate more factors to describe the characteristics of the ontologies Exploit new strategies for ontology alignment 32

31 Outline Dynamic Multi-strategy Ontology Matching Unbalanced Ontology Matching Discussion 33

32 Unbalanced Ontology Several challenges: Single domain vs. multiple domains Small size vs. large-size ontology 34

33 Key Problems Linguistic-based strategy O 1 x O 2 Structure-based strategy In memory graphs Iterative propagation Onto1 Thing Onto2 Object Thing Object subclassof subclassof subclassof Reference Address Entry Directions Reference Directions Reference Entry Address Direction Address Entry hasproperty range hasproperty range hasproperty range location place location place 35

34 Our Approach 1.Select candidates 2. construct Lightweight ontology Sub-ontology 36 Heavyweight ontology

35 Step 1: Select Candidates Edit-distance Complexity: O 1 x O 2 e.g. site vs. cite WordNet Similarity between c i and O l 37

36 Step 2: Construct Sub-ontology similarity influence E V 38

37 Step 3: Finding Matching Results Onto1 Thing Onto2 Object Thing Object subclassof subclassof subclassof Reference Address Entry Directions Reference Directions Reference Entry Address Direction Address Entry hasproperty range hasproperty range hasproperty range location place location place 39

38 Outline Dynamic Multi-strategy Ontology Matching Unbalanced Ontology Matching Experimental Results Discussion 40

39 41 OAEI 2007 Data Set GEMET: (5,280) The European Environment Agency GEMET ontology. AGROVOC: (28,439) AGROVOC thesaurus provided by Food and Agriculture Organization of the United Nations. NAL: (42,326) The Agricultural thesaurus released by the National Agricultural Library. Evaluation Measures Precision Recall F1-Measure CPU Time

40 42 Data Statistics

41 43 Precision

42 44 Recall

43 45 F1-Measure

44 46 CPU Time

45 Outline Dynamic Multi-strategy Ontology Matching Unbalanced Ontology Matching Discussion 47

46 Discussion Large-scale ontology matching Both ontologies are very large Group ontology matching A large number of sub ontologies Social ontology integration Folksonomies Active learning for ontology matching User interactions Beyond one-one alignment Beyond alignment 48

47 Related Publications Jie Tang, Juanzi Li, Bangyong Liang, Xiaotong Huang, Yi Li, and Kehong Wang. Using Bayesian Decision for Ontology Mapping. Journal of Web Semantics, Vol(4) 4: , December (Top 10 cited papers in JWS's history) Juanzi Li, Jie Tang, Yi Li, and Qiong Luo. RiMOM: A Dynamic Multi-Strategy Ontology Alignment Framework. IEEE Transaction on Knowledge and Data Engineering (TKDE). August 2009 (vol. 21 no. 8) pp (one of top cited papers among TKDE 2009's 100+ papers) Qian Zhong, Hanyu Li, Juanzi Li, Guotong Xie, Jie Tang, and Lizhu Zhou. A Gauss Function based Approach for Unbalanced Ontology Matching. In Proceedings of the 2009 ACM SIGMOD international conference on Management of data (SIGMOD'2009). pp Feng Shi, Juanzi Li, and Jie Tang. Actively Learning Ontology Matching via User Interaction. In Proceedings of the 8th International Conference of Semantic Web (ISWC'2009). pp

48 Thanks! HP: Q&A 50