Large Knowledge Collider (LarKC) :
      A Platform for Web Scale Reasoning

 Ning Zhong1,3, Frank van Harmelen2, Yi Zeng3...
The World is Creating
                                the Linked Data Every Day!




        Late br
                e
   ...
ay
                                     da
                                        y
                                   rd...
4
http://www.zemanta.com/




                          5
toxic releases       consumer expenditure
recent earthquakes   consumer price index
crime statistics     tornado reports
a...
Things to do with data.gov




                             7
8
9
<rdf:RDF>
 <rdf:Description rdf:about="/music/artists/584c04d2-4acc-491b-8a0a-e63133f4bfc4.rdf
  <rdfs:label>Description o...
<foaf:made>
  <mo:Record>
   <dc:title>It's Blitz!</dc:title>
   <mo:musicbrainz rdf:resource="http://musicbrainz.org/rele...
AND much more…




                 12
What to do for the success of Web-scale
        Semantic Data Processing?

Refining Search by Reasoning              Refin...
The LarKC Consortium
 13 partner institutions (from 11 countries, 2 from Asia)




                                       ...
The Large Knowledge Collider

          a platform for infinitely scalable reasoning
             on the data-web
“a configurable platform for
infinitely scalable semantic web reasoning”
                            “pipeline” suggests
 ...
What to about
the problem of success:

         parallelization




                      17
Supermarket!




Takes seconds

                18
Supermarket!




Takes a couple of minutes

                            19
Supermarket!




Get a better register



                        20
Massive Data
(even Web Scale
     Data!)




       Ooops!




                  21
From Linked Data Website
More than 7x108 triples




                     22
Parallelization

                         I am with Web-scale
                         data : 7x10^8 triples




Cashier1:...
Data
   two for the         dependencies
  price of one?
   2nd for half
      price?




Cashier1: 53
Cashier2: 14
Cashie...
Fruit        Split
   two for the                      Responsibility
  price of one?
   2nd for half        Vegetables

 ...
Fruit      Load
   two for the                      Balancing
  price of one?
   2nd for half        Vegetables

      pri...
Fruit         Data
  With a box of                      dependencies
   detergent
  and a box of         Vegetables   For ...
Towards Parallelization and Distribution



   Different parallel computing models:
   −   Peer-to-peer (MaRVIN)
   −   Ma...
The
           MaRVIN
            Way!




                    compute


        compute               compute            ...
MARVIN
        (Massive RDF Versatile Inference Network)
… is:
 −   a distributed technique for computing RDFS/OWL closure...
Divide-Conquer-Swap




                      SPLIT




             Repeat
                      COMP
                   ...
Current performance

200 Million triples in 7.2 minutes on 64 nodes.




                                                 ...
Reasoning-Hadoop!

RDFS/OWL reasoning with the MapReduce framework.




                                                  ...
The MapReduce
             Distributed Programming Model
  Initially designed and developed by Google in 2004 for large da...
What to about
the problem of success:

    cognitive heuristics




                       35
Stopping Rules
On very large datasets,
incompleteness is the rule
Must stop before we are finished
When to stop?
Stopping ...
Take inspiration from
     economics, biology, psychology

                                                  Lael Schooler...
When to switch between tasks?


                                  Lael Schooler
  hard task & easy task
   hard task & eas...
What to about
the problem of success:

         data selection




                      39
Take data-selection seriously

Where do the axioms come from?
• Which subset to use?
• Relevance measures                 ...
Take identifiers seriously
exploit the grounding of logical symbols
  in natural language
• Google distance as relevance m...
Unifying Search and Reasoning from the
                    Viewpoint of Granularity
                       Barriers for We...
Concrete Strategies



•    The Starting Point.
•    Multi-level Completeness.
•    Multi-level Specificity.
•    Multi-pe...
The Starting Point Strategy




[Collins 1969] Collins, A.M. and Quillian, M.R. Retrieval time from
semantic memory. Journ...
(I) The Starting Point Strategy
The “ Basic level advantage ” [Rogers2007].
Concepts in a basic level -- > more frequently...
Interest Retention and Interest Prediction


           A comparative study of TI during               Difference on the
 ...
Evaluations and the Released Dataset

•   interest retentions vs future interests.
    publication >= 100
    top 9 intere...
DBLP-SSE : DBLP Search Support Engine

Recent interests are extracted using the power law interest retention model.
Terms ...
DBLP-SSE : DBLP Search Support Engine
          Log in      Dieter Fensel

          Top 9       Web, Service, Semantic, A...
Multi-level Completeness Strategy



Low completeness                 Limited Time


High completeness                More...
Choosing the pivotal nodes
   in the network first !




                                                          51
    ...
Multi-level Completeness Strategy

     Nodes are grouped together by Node degrees under a perspective.

Completeness Pred...
Multi-level Specificity Strategy



  general          Limited Time




  Specific         More time Available




       ...
A Case Study on Multi-level Specificity Strategy
                                                   Specificity    Relevan...
The Multi-perspective Strategy

       Multiple representation of Knowledge [Minsky2006]
       User needs may differ from...
The Multi-perspective Strategy
           Under different perspectives, the distribution characteristics are different!


...
Comparison of Results
                from Different Perspectives


  A partial result of the multilevel specificity reaso...
Summarizing

The Semantic Web is rapidly becoming real
Scale is becoming a real problem
Different ways of scaling up:
 −  ...
LarKC Chinese Forum




                      59
Acknowledgement
Slides for this talk is mainly from 3 previous talks :


   Frank van Harmelen. Large Scale Reasoning on t...
Contact Info
  Want to play with LarKC?
  Want to play with LarKC?
  Want to contribute plugins?
  Want to contribute plug...
References
[Berners-Lee1999] Berners-Lee, T., Fischetti, M.: Weaving the Web: The Original
Design and Ultimate Destiny of ...
Thank you!




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Large Knowledge Collider (LarKC) : A Platform for Web Scale Reasoning

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This is an invited talk from the 2009 Asian Scalable Semantic Data Processing Workshop, co-located with the 2009 Asian Semantic Web Conference.

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Large Knowledge Collider (LarKC) : A Platform for Web Scale Reasoning

  1. 1. Large Knowledge Collider (LarKC) : A Platform for Web Scale Reasoning Ning Zhong1,3, Frank van Harmelen2, Yi Zeng3, Zhisheng Huang2 Maebashi Institute of Technology, Japan Vrije University Amsterdam, the Netherlands International WIC Institute, Beijing University of Technology, China http://www.larkc.eu 1
  2. 2. The World is Creating the Linked Data Every Day! Late br e Google aking news: Video now al with R annota so DF-a ( ted f ro m Y using v ahoo a ocabul nd Fac aries e bo o k ) 2
  3. 3. ay da y rd er pe p tts s en e n um cum oc do d n iio n llll o ii m rr m ffou ou 3
  4. 4. 4
  5. 5. http://www.zemanta.com/ 5
  6. 6. toxic releases consumer expenditure recent earthquakes consumer price index crime statistics tornado reports assaults on police trade statistics social benefits river elevations 6 unemployment rates energy consumption
  7. 7. Things to do with data.gov 7
  8. 8. 8
  9. 9. 9
  10. 10. <rdf:RDF> <rdf:Description rdf:about="/music/artists/584c04d2-4acc-491b-8a0a-e63133f4bfc4.rdf <rdfs:label>Description of the artist Yeah Yeah Yeahs</rdfs:label> <foaf:primaryTopic rdf:resource="/music/artists/584c04d2-4acc-491b-8a0a-e63133f4b </rdf:Description> <mo:MusicArtist rdf:about="/music/artists/584c04d2-4acc-491b-8a0a-e63133f4bfc4#a <rdf:type rdf:resource="http://purl.org/ontology/mo/MusicGroup"/> <foaf:name>Yeah Yeah Yeahs</foaf:name> <ov:sortLabel>Yeah Yeah Yeahs</ov:sortLabel> <bio:event> <bio:Birth><bio:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime </bio:event> <owl:sameAs rdf:resource="http://dbpedia.org/resource/Yeah_Yeah_Yeahs"/> <mo:image rdf:resource="/music/images/artists/7col_in/584c04d2-4acc-491b-8a0a-e63 <foaf:page rdf:resource="/music/artists/584c04d2-4acc-491b-8a0a-e63133f4bfc4.html"/ <mo:musicbrainz rdf:resource="http://musicbrainz.org/artist/584c04d2-4acc-491b-8a0a <foaf:homepage rdf:resource="http://www.yeahyeahyeahs.com/"/> <mo:wikipedia rdf:resource="http://en.wikipedia.org/wiki/Yeah_Yeah_Yeahs"/> <mo:myspace rdf:resource="http://www.myspace.com/yeahyeahyeahs"/> <mo:member rdf:resource="/music/artists/a1439b8d-672a-446f-a7ff-6f09d68254b3#art <mo:member rdf:resource="/music/artists/14d44067-99c2-4f77-b58b-138f0b6911fa#ar <mo:member rdf:resource="/music/artists/20dc35ec-6cc1-4c66-98a3-4a6116cb3869#a ... 10
  11. 11. <foaf:made> <mo:Record> <dc:title>It's Blitz!</dc:title> <mo:musicbrainz rdf:resource="http://musicbrainz.org/release/9c4177fe-bdce-4f9d-ab <rev:hasReview rdf:resource="/music/reviews/hnp2#review"/> </mo:Record> </foaf:made> ..... <mo:MusicArtist rdf:about="/music/artists/a1439b8d-672a-446f-a7ff-6f09d68254b3#arti <foaf:name>Brian Chase</foaf:name> </mo:MusicArtist> <mo:MusicArtist rdf:about="/music/artists/14d44067-99c2-4f77-b58b-138f0b6911fa#art <foaf:name>Karen O</foaf:name> </mo:MusicArtist> <mo:MusicArtist rdf:about="/music/artists/20dc35ec-6cc1-4c66-98a3-4a6116cb3869#art <foaf:name>Nick Zinner</foaf:name> </mo:MusicArtist> </rdf:RDF> 11
  12. 12. AND much more… 12
  13. 13. What to do for the success of Web-scale Semantic Data Processing? Refining Search by Reasoning Refining Reasoning by Search [Berners-Lee 1999] [Fensel & Frank 2007] Unifying Search and Reasoning (ReaSearch) [Fensel & Frank 2007] 13
  14. 14. The LarKC Consortium 13 partner institutions (from 11 countries, 2 from Asia) 14 14
  15. 15. The Large Knowledge Collider a platform for infinitely scalable reasoning on the data-web
  16. 16. “a configurable platform for infinitely scalable semantic web reasoning” “pipeline” suggests linear structure: but in LarKC also: 16
  17. 17. What to about the problem of success: parallelization 17
  18. 18. Supermarket! Takes seconds 18
  19. 19. Supermarket! Takes a couple of minutes 19
  20. 20. Supermarket! Get a better register 20
  21. 21. Massive Data (even Web Scale Data!) Ooops! 21
  22. 22. From Linked Data Website More than 7x108 triples 22
  23. 23. Parallelization I am with Web-scale data : 7x10^8 triples Cashier1: 53 Cashier2: 14 Cashier3: 33 Cashier4: 72 Cashier2: 34 Cashier3: 13 Cashier4: 32 -------------------- 23 Total : 340
  24. 24. Data two for the dependencies price of one? 2nd for half price? Cashier1: 53 Cashier2: 14 Cashier3: 33 Cashier4: 72 Cashier2: 34 Cashier3: 13 Cashier4: 32 -------------------- 24 Total : 340
  25. 25. Fruit Split two for the Responsibility price of one? 2nd for half Vegetables price? Household Cashier1: 53 Packaged Cashier2: 14 Cashier3: 33 Cashier4: 72 Cashier2: 34 Rest Cashier3: 13 Cashier4: 32 -------------------- 25 Total : 340
  26. 26. Fruit Load two for the Balancing price of one? 2nd for half Vegetables price? Household Cashier1: 53 Packaged Cashier2: 14 Cashier3: 33 Cashier4: 72 Cashier2: 34 Rest Cashier3: 13 Cashier4: 32 -------------------- 26 Total : 340
  27. 27. Fruit Data With a box of dependencies detergent and a box of Vegetables For RDF data, any triple can refer to any URI. cereal get a free pen! Household Cashier1: 53 Packaged Cashier2: 14 Cashier3: 33 Cashier4: 72 Cashier2: 34 Rest Cashier3: 13 Cashier4: 32 -------------------- 27 Total : 340
  28. 28. Towards Parallelization and Distribution Different parallel computing models: − Peer-to-peer (MaRVIN) − Map-Reduce (Reasoning-Hadoop) 28
  29. 29. The MaRVIN Way! compute compute compute Eyal Oren input output data data compute compute Spyros Kotoulas compute 29 Divide-Conquer-Swap
  30. 30. MARVIN (Massive RDF Versatile Inference Network) … is: − a distributed technique for computing RDFS/OWL closure … scales by: − distributing computation over many nodes − approximate (sound but incomplete) reasoning − anytime convergence (more complete over time) … runs on: − in principle: any grid, using Ibis middleware − the DAS-3 distributed supercomputer (300 nodes) 30
  31. 31. Divide-Conquer-Swap SPLIT Repeat COMP UTE JOIN 31
  32. 32. Current performance 200 Million triples in 7.2 minutes on 64 nodes. 32
  33. 33. Reasoning-Hadoop! RDFS/OWL reasoning with the MapReduce framework. 33
  34. 34. The MapReduce Distributed Programming Model Initially designed and developed by Google in 2004 for large data processing [Jeffrey & Sanjay 2004]. The computation is expressed with two functions: map and reduce. Map-Reduce on 64 machines: Peak inference rates at 8M triples/sec Sustained inference rates at 4M triples/sec C2 ApC Map <C,_,_> Reduce p1 AqB <A, r3 DrD ErD . . _,_ > . . q1 . _,_> . D3 FrC <C, F1 Map <F,_,_> Reduce Map-Reduce Jacopo Urbani 34
  35. 35. What to about the problem of success: cognitive heuristics 35
  36. 36. Stopping Rules On very large datasets, incompleteness is the rule Must stop before we are finished When to stop? Stopping rules are important − determine length of computation (don’t stop too late) − quality of result (don’t stop too early)
  37. 37. Take inspiration from economics, biology, psychology Lael Schooler Humans have good heuristics for when to stop problem solving: Time between solutions “Name capital cities in Europe”: London, Paris, Berlin, Rome, Amsterdam, … Milan, Madrid, …., ….., Paris, …., Wrong answers Repetitions
  38. 38. When to switch between tasks? Lael Schooler hard task & easy task hard task & easy task combined combined task task Humans (& animals) are very Humans (& animals) are very good finding this optimum good finding this optimum
  39. 39. What to about the problem of success: data selection 39
  40. 40. Take data-selection seriously Where do the axioms come from? • Which subset to use? • Relevance measures Zhisheng Huang • Example: syntactic relevance: • δ(α,β)=1 if α,β share a concept symbol • δ(α,β)=k if δ(α,γ)=k-1 and β,γ share a concept symbol • very simple measure, very syntactically unstable, but: Gives a high quality sound approximation Gives a high quality sound approximation (> 90% recall, 100% precision for small k) (> 90% recall, 100% precision for small k)
  41. 41. Take identifiers seriously exploit the grounding of logical symbols in natural language • Google distance as relevance measure Zhisheng Huang max{log f ( x ), log f ( y )} − log f ( x , y ) NGD ( x , y ) = log M − min{log f ( x ), log f ( y )} = symmetric conditional probability of co-occurrence = estimate of semantic distance Gives almost perfect “forgetting function” Gives almost perfect “forgetting function” for matching class definitions in 2 vocabularies for matching class definitions in 2 vocabularies
  42. 42. Unifying Search and Reasoning from the Viewpoint of Granularity Barriers for Web-scale Problem Solving (1) most relevant data vs search results space [Berners-Lee 1999]. (2) Traditional reasoning systems vs Web-scale data vs rational time [Fensel 2007]. Refining Search by Reasoning Refining Reasoning by Search [Berners-Lee 1999] [Fensel & Frank 2007] Unifying Search and Reasoning (ReaSearch) [Fensel & Frank 2007] Granularity Human Problem Solving Web Problem Solving Inspire! Basic level advantage, Cognitive Memory Retention Multi-level, multi-perspective, Variable Precision 42
  43. 43. Concrete Strategies • The Starting Point. • Multi-level Completeness. • Multi-level Specificity. • Multi-perspective. 43 43
  44. 44. The Starting Point Strategy [Collins 1969] Collins, A.M. and Quillian, M.R. Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behaviour, 8, 240-247. 44
  45. 45. (I) The Starting Point Strategy The “ Basic level advantage ” [Rogers2007]. Concepts in a basic level -- > more frequently than other terms [Wisniewski1989]. TI (i ) = ∑ j =1 m(i, j ) n • (Frequency) Total Interest : As a step forward “familiar term” in basic level, “interests retention” focuses on frequency and recency at the same time. Interest retention models < -- > Cognitive memory retention models [Anderson, Schooler 1991]. • (Frequency and Recency) Exponential Model for Interest Retention : EIR(i ) = ∑ j =1 m(i, j ) × Ae − bTi n • (Frequency and Recency) Power Model for Interest Retention : PIR(i ) = ∑ j =1 m(i, j ) × ATi n −b 45
  46. 46. Interest Retention and Interest Prediction A comparative study of TI during Difference on the 1990-2008 and IR in 2009 contribution values from papers published in different years A comparative study on the A comparative study on the prediction and real prediction and real publication numbers by the publication numbers by the exponential law model power law model 46
  47. 47. Evaluations and the Released Dataset • interest retentions vs future interests. publication >= 100 top 9 interests 2000 to 2007 1226 persons 49.54% predict 3 out of 9 interests. • 615,124 computer scientists in the SwetoDBLP dataset. • http://wiki.larkc.eu/csri-rdf 47
  48. 48. DBLP-SSE : DBLP Search Support Engine Recent interests are extracted using the power law interest retention model. Terms with high frequency do not necessarily have high interest retention. (e.g. “Knowledge”) 48
  49. 49. DBLP-SSE : DBLP Search Support Engine Log in Dieter Fensel Top 9 Web, Service, Semantic, Architecture, Model, Ontology, interests Knowledge, Computing, Language Query : Artificial Intelligence List 1 : without current interests constraints (Top 5 results) * PROLOG Programming for Artificial Intelligence, Second Edition. * Artificial Intelligence Architectures for Composition and Performance Environment. * Artificial Intelligence in Music Education: A Critical Review. * Music, Intelligence and Artificiality. Artificial Intelligence and Music Education. * Musical Knowledge: What can Artificial Intelligence Bring to the Musician? * ... List 2 : with current interests constraints (Top 5 results) * Web Intelligence and Artificial Intelligence in Education. * Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)-A New Standard for System Diagnostics. * Semantic Model for Artificial Intelligence Based on Molecular Computing. * Open Information Systems Semantics for Distributed Artificial Intelligence. * Artificial Intelligence and Financial Services. *… 49
  50. 50. Multi-level Completeness Strategy Low completeness Limited Time High completeness More time Available One practical question : How to choose the nodes to be reasoned over? 50
  51. 51. Choosing the pivotal nodes in the network first ! 51 Another one: If I stop in here, what is the completeness like now!
  52. 52. Multi-level Completeness Strategy Nodes are grouped together by Node degrees under a perspective. Completeness Prediction Function : | Nrel (i ) | ×(| Nsub(i ) | − | Nsub(i ' ) |) PC (i ) = | Nrel (i ) | ×(| N | − | Nsub(i ' ) |)+ | Nrel (i ' ) | ×(| Nsub(i ' ) | − | N |) degree(n, Pcn) to stop Satisfied authors AI authors “Who are 70 2885 151 authors in 30 17121 579 Artificial 11 78868 1142 Intelligence?” 4 277417 1704 1 575447 2225 0 615124 2355 Unifying search and reasoning with multilevel Comparison of predicted and actual completeness and anytime behavior. completeness value. 52
  53. 53. Multi-level Specificity Strategy general Limited Time Specific More time Available 53
  54. 54. A Case Study on Multi-level Specificity Strategy Specificity Relevant Keywords Number of Authors Level 1 Artificial Intelligence 2355 Answers to “Who are the authors in Artificial Level 2 Agents 9157 Intelligence?” in multiple levels of specificity according to the hierarchical ontology of Automated Reasoning 222 Artificial Intelligence. Cognition 19775 Constriants 8744 Games 3817 Specificity Number of authors Completeness Knowledge 1537 Level 1 2355 0.85% Representation 2939 Level 1,2 207468 75.11% Natural Language Level 1,2,3 276205 100% 16425 Robot … … A comparative study on the answers in Level 3 Case-Based Reasoning 1133 different levels of specificity. Cognitive Modeling 76 Decision Trees 1112 Search 32079 Translation 4414 Web Intelligence 122 … … 54
  55. 55. The Multi-perspective Strategy Multiple representation of Knowledge [Minsky2006] User needs may differ from each other < -- > expect answers from different perspectives. Normalized Degree Distribution of predicates in SwetoDBLP dataset 55
  56. 56. The Multi-perspective Strategy Under different perspectives, the distribution characteristics are different! Fig. 2. Coauthor number distribution Fig. 3. log-log diagram of Figure 2. Fig. 4. A zoomed in version in the SwetoDBLP dataset. of Figure 2. Fig. 5. A zoomed in version of coauthor Fig. 6. Publication number distribution Fig. 7. log-log diagram distribution for Artificial Intelligence". in the SwetoDBLP dataset. of Figure 6. 56
  57. 57. Comparison of Results from Different Perspectives A partial result of the multilevel specificity reasoning task The list of authors in Artificial Intelligence" in level 1 from two perspectives. Publication number perspective Coauthor number perspective Thomas S. Huang (387) Carl Kesselman (312) John Mylopoulos (261) Thomas S. Huang (271) Hsinchun Chen (260) Edward A. Fox (269) Henri Prade (252) Lei Wang (250) Didier Dubois (241) John Mylopoulos (245) Thomas Eiter (219) Ewa Deelman (237) ... ... 57
  58. 58. Summarizing The Semantic Web is rapidly becoming real Scale is becoming a real problem Different ways of scaling up: − parallelization − exploiting cognitive heuristics Stopping rules, cognitive memory retention, etc. − data-selection for incomplete reasoning. − New Forms of Reasoning.
  59. 59. LarKC Chinese Forum 59
  60. 60. Acknowledgement Slides for this talk is mainly from 3 previous talks : Frank van Harmelen. Large Scale Reasoning on the Semantic Web or: When success is becoming a problem. Invited talk at the 2009 International Joint Conferences on Active Media Technology and Brain Informatics. Yi Zeng. Unifying Web-scale Search and Reasoning from the viewpoint of Granularity. the 2009 International Joint Conferences on Active Media Technology and Brain Informatics. Spyros. Marvin and the Billion Triple Challenge. Super Computing Seminar, University of Amsterdam, 2008. 60
  61. 61. Contact Info Want to play with LarKC? Want to play with LarKC? Want to contribute plugins? Want to contribute plugins? Want to deploy LarKC? Want to deploy LarKC? Frank.van.Harmelen@cs.vu.nl http://www.larkc.eu Asia: Asia: Ning Zhong: zhong@maebashi-it.ac.jp Ning Zhong: zhong@maebashi-it.ac.jp Yi Zeng ::yzeng@emails.bjut.edu.cn Yi Zeng yzeng@emails.bjut.edu.cn @ WIC @ WIC 61
  62. 62. References [Berners-Lee1999] Berners-Lee, T., Fischetti, M.: Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor. HarperSanFrancisco (1999) [Fensel2007] Fensel, D., van Harmelen, F.: Unifying reasoning and search to web scale. IEEE Internet Computing 11(2) (2007) 94-96 [Michalski1986] Michalski, R.S. and Winston, P.H. Variable precision logic. Artificial Intelligence, 29(2), 121–146, 1986. [Minsky2006] Minsky, M. The Emotion Machine : commonsense thinking, artificial intelligence, and the future of the human mind. Simon & Schuster, 2006. [Rogers 2007] Rogers, T., Patterson, K.: Object categorization: Reversals and explanations of the basic-level advantage. Journal of Experimental Psychology: General 136(3) (2007) 451-469 [Wickelgren1976] Wickelgren, W.: Memory storage dynamics. In: Handbook of learning and cognitive processes. Hillsdale, NJ: Lawrence Erlbaum Associates (1976) 321-361 [Aleman-Meza2007] Aleman-Meza, B. Hakimpour, F., Arpinar, I., Sheth, A.: Swetodblp ontology of computer science publications. Web Semantics: Science, Services and Agents on the World Wide Web 5(3) (2007) 151-155 [Ebbinghaus1913] Ebbinghaus, H.: Memory: A Contribution to Experimental Psychology Hermann Ebbinghaus. Teachers College, Columbia University (1913) 62
  63. 63. Thank you! 63

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