Use of graphs and taxonomic classifications to analyze content relationships among courseware

Transcription

1 Institute of Computing UNICAMP Use of graphs and taxonomic classifications to analyze content relationships among courseware Márcio de Carvalho Saraiva and Claudia Bauzer Medeiros

2 Background and Motivation Videos Slides 2

3 Background and Motivation 3

4 Background and Motivation More than 1600 items about "databases" Changuel et al.,

5 5

6 Background and Motivation It should be easy to understand how different materials are related. Relationships: Authorship Date Location Visual Topics etc.?? Ouyang and Zhu, 2007??? 6

7 Related Work Educational Recognition of data relationships Analysis of relationships using graph databases Integration of multimedia data (Pereira, 2014) (Sathiyamurthy et al. 2012) (Cavoto et al. 2015) (Santanchè et al. 2014) Objects metadata Architecture with hierarchies Data Mining Analysis on a single level one kind of data not related to education semantic annotations training sets 7

8 Goal Allow the integration of different types of educational material, highlighting relationships among content. 8

9 Proposal CIMAL: Courseware Integration under Multiple relations to Assist Learning I'm having trouble on "Big Data" in discipline "X" of teacher "Y" what other material could help me to understand this issue? CIMAL Student Sources 1 to N 9

10 Proposal Step B - Intermediate Step A - Extraction of elements of interest DDEx input courseware Extractor Representation Instantiation Step C - Intermediate Representation Analysis elements of interest Java + Youtube API Step D - Courseware access 10

11 Proposal - Step A - Extraction of elements of interest Classification Algorithms Introduction to Databases 11

12 Proposal - Step A - Extraction of elements of interest Commented slide, highlighted concepts, Slide titles, Descriptions from figures and tables... 12

13 Proposal - Step A - Extraction of elements of interest Data Science Data Mining Classification 13

14 Proposal - Step A - Extraction of elements of interest 0:00-0:30...Databases are important... 0:31-1:00...everybody need to know SQL... 1:01-1:30...the DBMS is a computer software application... 14

15 Proposal - Step B - Intermediate Representation Instantiation Step B - Intermediate Step A - Extraction of elements of interest Representation Instantiation Step C - Intermediate Representation Analysis Shadows as graphs Builder input Metadata and Extractor Text Extractor elements of interest Intermediate Graph Representation Builder courseware Shadows Graph-based as graphs Representation Step D - Courseware access 15

16 Proposal - Step B - Intermediate Representation Instantiation Advanced Databases Discipline Author Coursewar e Text Prof. Saraiva Set of relevant concepts Introduction to Databases (video) Lorem ipsum dolor sit amet, onsectetur adipiscing elit... SQL Databases DBMS Date 10/11/2015 Mota and Medeiros,

17 Proposal - Step C - Intermediate Representation Analysis Step B - Intermediate Step A - Extraction of elements of interest Representation Instantiation Step C - Intermediate Representation Analysis Graph Database (Neo4J) Java + Lucene APIs Metadata and Extractor Text Extractor elements of interest Intermediate Graph Representation Builder courseware Graph-based Representation Step D - Courseware access Topics Classifier G Re rap pr h-b es as en ed tat ion input Classification of Shadows Shadows as graphs Enriched Taxonomy Classification of Representations Combiner Relationships Analyzer Information about Relations external sources Taxonomy 17

18 Proposal - Step C - Intermediate Representation Analysis The ACM Computing Classification System (CCS) General and reference Hardware A B Theory of computation C Information retrieval 1 Query languages 1 Information systems D Data management systems 2 Middleware for databases 2 World Wide Web 3 Information integration 3 18

19 Proposal - Step C - Intermediate Representation Analysis The ACM Computing Classification System (CCS) General and reference Hardware A B Theory of computation C Information retrieval 1 Query languages 1 Information systems D Data management systems 2 Middleware for databases 2 World Wide Web 3 Information integration 3 19

20 Proposal - Step C - Intermediate Representation Analysis Step B - Intermediate Step A - Extraction of elements of interest Representation Instantiation Step C - Intermediate Representation Analysis Graph Database (Neo4J) Java + Lucene APIs Metadata and Extractor Text Extractor elements of interest Intermediate Graph Representation Builder courseware Graph-based Representation Step D - Courseware access Topics Classifier G Re rap pr h-b es as en ed tat ion input Classification of Shadows Shadows as graphs Enriched Taxonomy Classification of Representations Combiner Relationships Analyzer Information about Relations external sources Taxonomy 20

21 Proposal - Step C - Intermediate Representation Analysis The ACM Computing Classification System (CCS) Advanced Databases General and reference A Hardware B Theory of computation C Information systems D Prof. Saraiva Introduction to Databases (video) Information retrieval SQL, Lorem ipsum dolor sit amet, onsectetur adipiscing elit... 10/11/2015 Database, 1 Data management systems World Wide Web 2 3 DBMS... Query languages 1 Middleware for databases Information integration 2 3 Topics??? 21

22 Proposal - Step C - Intermediate Representation Analysis Introduction to Databases (video) N wikipages 22

23 Proposal - Step C - Intermediate Representation Analysis 80% SQL Introduction to Databases (video) ESA 20% Depth-first search Gabrilovich and Markovitch, 2007 ; Apache Lucene,

24 Proposal - Step C - Intermediate Representation Analysis 80% SQL Courseware ESA Query languages 24

25 Proposal - Step C - Intermediate Representation Analysis Advanced Databases Prof. Saraiva Introduction to Databases (video) Lorem ipsum dolor sit amet, onsectetur adipiscing elit... SQL, Database, DBMS... 10/11/2015 Information Systems Topics Data management systems Query languages 25

26 Proposal - Step C - Intermediate Representation Analysis Step B - Intermediate Step A - Extraction of elements of interest Representation Instantiation Step C - Intermediate Representation Analysis Graph Database (Neo4J) Java + Lucene APIs Metadata and Extractor Text Extractor elements of interest Intermediate Graph Representation Builder courseware Graph-based Representation Step D - Courseware access Topics Classifier G Re rap pr h-b es as en ed tat ion input Classification of Shadows Shadows as graphs Enriched Taxonomy Classification of Representations Combiner Relationships Analyzer Information about Relations external sources Taxonomy 26

27 Proposal - Step C - Intermediate Representation Analysis Information Systems Introduction to Databases (video) Classificatio n Algorithms (slides) Data management systems Query languages Information Integration 27

28 Proposal - Step C - Intermediate Representation Analysis Information Systems Introduction to Databases (video) Classificatio n Algorithms (slides) Data management systems Query languages Information Integration 28

29 Proposal - Step C - Intermediate Representation Analysis Information Systems Introduction to Databases (video) Classificatio n Algorithms (slides) Data management systems Query languages Information Integration 29

30 Proposal - Step C - Intermediate Representation Analysis Information Systems Introduction to Databases (video) Databases I (video) Data management systems Query languages 30

31 Proposal - Step C - Intermediate Representation Analysis Information Systems Introduction to Databases (video) Databases I (video) Data management systems Query languages 31

32 Proposal - Step C - Intermediate Representation Analysis Introduction to Databases Classificatio n Algorithms Databases I Data Mining 32

33 Proposal - Step C - Intermediate Representation Analysis Clique Introduction to Databases Classification Algorithms Databases I Data Mining 33

34 Proposal - Step C - Intermediate Representation Analysis Shortest Path Graph to Data Mining Introduction to Databases 2 Databases I Classification Algorithms Data Mining 34

35 Proposal - Step C - Intermediate Representation Analysis Centrality Introduction to Databases Classification Algorithms Databases I Data Mining 35

36 Proposal Step B - Intermediate Step A - Extraction of elements of interest Shadows DDEx input Step C - Intermediate Representation Analysis Representation Instantiation Extractor elements of interest Intermediate Graph Representation Builder Topics Classifier G Re rap pr h-b es as en ed tat ion courseware Graph-based Representation Step D - Courseware access Graph query Interface output Java + 2graph API Graph Database (Neo4J) Java + Lucene APIs Enriched Taxonomy Classification of Representations Combiner Relationships Analyzer Information about Relations Graph builder Graph Database Graph-based representations, (Neo4J) informations about relations and classification external sources Taxonomy 36

37 Preliminary conclusions Expected contributions: A framework for integration of different courseware highlighting relationships among topics; It is not necessary tags and training sets; Analysis of multilevels relationships through graphs and taxonomy; Adaptation of the algorithm ESA to classification of topics of courseware using intrinsic features 37

38 References Changuel, S., Labroche, N., and Bouchon-Meunier, B. (2015). Resources sequencing using automatic prerequisite outcome annotation. ACM Trans. Intell. Syst. Technol.,6(1):pages 6:1 6:30. Gabrilovich, E. and Markovitch, S. (2007). Computing semantic relatedness using wikipedia-based explicit semantic analysis. In Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI 07, pages , San Francisco, CA, USA. Morgan Kaufmann Publishers Inc. Mishra, S., Gorai, A., Oberoi, T., and Ghosh, H. (2010). Efficient Visualization of Content and Contextual Information of an Online Multimedia Digital Library for Effective Browsing IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pages Mota, M. S. and Medeiros, C. B. (2013). Introducing shadows: Flexible document representation and annotation on the web. ICDE Workshops, pages Ouyang, Y. and Zhu, M. (2007). elorm: Learning object relationship mining based repository. Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007, pages

39 References Pereira, B. (2014). Entity Linking with Multiple Knowledge Bases: An Ontology Modularization Approach. In The Semantic Web - ISWC 2014, pages Springer International Publishing. Santanchè, A., Longo, J. S. C., Jomier, G., Zam, M., and Medeiros, C. B. (2014). Multifocus research and geospatial data anthropocentric concerns. JIDM - Journal of Information and Data Management, 5(2): Sathiyamurthy, K., Geetha, T. V., and Senthilvelan, M. (2012). An approach towards dynamic assembling of learning objects. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics, ICACCI 12, pages , New York, NY, USA. ACM. Shirakawa, M., Nakayama, K., Hara, T., and Nishio, S. (2015). Wikipedia-based semantic similarity measurements for noisy short texts using extended naive bayes. IEEE Trans. Emerging Topics Comput., 3(2): Tong, Y., Cao, C. C., Zhang, C. J., Li, Y., and Chen, L. (2014). CrowdCleaner: Data cleaning for multi-version data on the web via crowdsourcing IEEE 30th International Conference on Data Engineering, pages

40 Acknowledgements Laboratory of Information Systems - Unicamp Work partially financed by CAPES, FAPESP/Cepid in Computational Engineering and Sciences (2013/ ), FAPESP-PRONEX (escience project), INCT in Web Science (CNPq /2009-9), and individual grants from CAPES and CNPq. 40

41 Institute of Computing UNICAMP Thanks! Use of graphs and taxonomic classifications to analyze content relationships among courseware