1 / 41

Automated Mapping of Ontologies for Text Classification

This thesis explores automating ontology mapping for text classification to reduce human effort, with discussions on semantic web, approaches, and experimental results. Using Naïve Bayes text classifier for ontology mapping.

yamin
Download Presentation

Automated Mapping of Ontologies for Text Classification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Learning the Semantic Meaning of a Concept from the Web Yang Yu Master’s Thesis Defense August 03, 2006

  2. LIVING_THINGS ANIMAL PLANT HUMAN CAT TREE GRASS MAN WOMAN ARBOR FRUTEX The Problem • Manually preparing training data for text classification based ontology mapping is expensive.

  3. http://www.google.com/ The Thesis • Automatically collecting training data for the concept defined in an ontology. • Benefits • Reduce the amount of human work • Fully automated ontology mapping

  4. Overview • Background • The semantic Web and ontology • Ontology Mapping • Proposal • System • Experimental Results • WEAPONS ontology • LIVING_THINGS ontology • Discussions and Conclusion

  5. Find all types of jets that are made in the USA Made-in WA partOf USA Semantic Web and Ontology • What is it? • “an extension of the current web” • An Example

  6. Ontology Mapping • Interoperability problem • Independently developed ontologies for the same or overlapped domain • Mapping • r = f (Ci, Cj) where i=1, …, n and j=1, …, m; • r {equivalent, subClassOf, superClassOf, complement, overlapped, other}

  7. Approaches to Ontology Mapping • Manual mapping • String Matching • Text classification • the semantic meaning of a concept is reflected in the training data that use the concept • Probabilistic feature model • Classification • Results highly depend on training data

  8. Motivation • Preparing exemplars manually is costly • Billions of documents available on the web • Search engines

  9. The Proposal • Using the concept defined in an ontology as a query and processing the search results to obtain exemplars • Verification • Build a prototype system • Check ontology mapping results

  10. Ontology A Parser Queries Retriever Retriever WWW Links to Web Pages Processor HTML Docs Text Files System overview – Part I Search Engine

  11. Concepts Queries FOOD FRUIT APPLE ORANGE living+things living+things animal living+things+animal plant living+things+plant cat living+things+animal+cat human living+things+animal+human man living+things+animal+human+man woman living+things+animal+human+woman tree living+things+plant+tree grass living+things+plant+grass frutex living+things+plant+tree+Frutex arbor living+things+plant+tree+arbor The parser (Query expansion) FOOD+FRUIT+APPLE

  12. The retriever

  13. The processor

  14. Naïve Bayes text classifier • Bow toolkit • McCallum, Andrew Kachites, Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering, http://www.cs.cmu.edu/~mccallum/bow 1996. • rainbow -d model --index dir/* • rainbow –d model –query • Bayes Rule • Naïve Bayes text classifier

  15. Prior P (B | A) * P (A) P (B) posterior Normalizing constant P(A, B) Mitchell Tom, Machine Learning, McGraw Hill, 1997 B A P (B | A) = P (A, B) / P (A) P (A | B) = P (A, B) / P (B) Bayes Rule • P (A | B) =

  16. Naïve Bayes classifier • A text classification problem • “What’s the most probable classification of the new instance given the training data?” • vj: category j. • (a1, a2, …, an): attributes of a new document • So Naïve (Mitchell Tom, Machine Learning, McGraw Hill) 1997

  17. Ontology A Ontology B Feature Model Mapping Results Text Files (B) Text Files (A) Rainbow Rainbow Model Builder Calculator System overview– Part II

  18. LIVING_THINGS ANIMAL PLANT LIVING_THINGS HUMAN CAT TREE GRASS ANIMAL PLANT HUMAN CAT TREE GRASS MAN WOMAN ARBOR FRUTEX MAN WOMAN ARBOR FRUTEX The model builder • Mutually exclusive and exhaustive • Leaf classes • C+and C-

  19. The calculator • Naïve Bayes text classifier tends to give extreme values (1/0) • Tasks • Feed exemplars to the classifier one by one • Keep records of classification results • Take averages and generate report

  20. Categories in WeaponsA.n3 Num. of exemplars TANK-VEHICLE 170 AIR-DEFENSE-GUN 20 SAUDI-NAVAL-MISSILE-CRAFT 10 An Example of the Calculator TANK-VEHICLE APC AIR-DEFENSE-GUN Classifier 200 SAUDI-NAVAL- MISSILE-CRAFT P(TANK-VEHICLE | APC) = 170 /200= 0.85 P(AIR-DEFENSE-GUN | APC) = 0.10 P(SAUDI-NAVAL-MISSILE-CRAFT| APC) = 0.05

  21. Experiments with WEAPONS ontology • Information Interpretation and Integration Conference (http://www.atl.lmco.com/projects/ontology/i3con.html) • WeaponsA.n3 and WeaponsB.n3 • Both over 80 classes defined • More than 60 classes are leaf classes • Similar structure

  22. WeaponsA.n3 Part of WeaponsA.n3 WEAPON CONVENTIONAL- WEAPON ARMORED- COMBAT-VEHICLE MODERN- NAVAL-SHIP WARPLANE SUPER-ETENDARD AIRCRAFT-CARRIER PATROL-CRAFT TANK-VEHICLE -

  23. WEAPON CONVENTIONAL- WEAPON ARMORED- COMBAT-VEHICLE MODERN- NAVAL-SHIP WARPLANE FIGHTER-PLANE AIRCRAFT-CARRIER PATROL- WARTER-CRAFT TANK-VEHICLE - FIGHTER-ATTACK-PLANE LIGHT-TANK APC LIGHT-AIRCRAFT-CARRIER PATROL- BOAT- RIVER PATROL- BOAT SUPER-ETENDARD-FIGHTER WeaponsB.n3 Part of WeaponsB.n3

  24. Part of WeaponsB.n3 Expected Results AIRCRAFT-CARRIER PATROL-CRAFT SUPER- ETENDARD TANK-VEHICLE FIGHTER-PLANE LIGHT-AIRCRAFT-CARRIER PATROL- WARTER-CRAFT APC FIGHTER-ATTACK-PLANE LIGHT-TANK SUPER-ETENDARD-FIGHTER PATROL- BOAT- RIVER PATROL- BOAT

  25. A Typical Report P(APC | Ci) where i = 1 … 63 ...... ……

  26. New Classes Whole file Prob Sentences with Keywords Prob LIGHT-AIRCRAFT-CARRIER AIRCRAFT-CARRIER 0.65 AIRCRAFT-CARRIER 0.57 P(TANK-VEHICLE | APC ) = 0.28 APC SILKWORM-MISSILE-MOD 0.46 SELF-PROPELLED-ARTILLERY 0.36 SUPER-ETENDARD-FIGHTER SILKWORM-MISSILE-MOD 0.66 MRBM 0.51 FIGHTER-ATTACK-PLANE SILKWORM-MISSILE-MOD 0.83 MRBM 0.38 P(SUPER-ETENDARD | SUPER-ETENDARD-FIGHTER ) = 0.21 PATROL-WATERCRAFT SILKWORM-MISSILE-MOD 0.28 PATROL-CRAFT 0.52 PATROL-BOAT-RIVER SILKWORM-MISSILE-MOD 0.65 PATROL-CRAFT 0.54 PATROL-BOAT SILKWORM-MISSILE-MOD 0.51 PATROL-CRAFT 0.66 LIGHT-TANK SILKWORM-MISSILE-MOD 0.56 TANK-VEHICLE 0.3 FIGHTER-PLANE AIRCRAFT-CARRIER 0.49 MRBM 0.38 classes with highest conditional probability

  27. New Classes Group-whole-50 Prob Group-whole-100 Prob LIGHT-AIRCRAFT-CARRIER SILKWORM-MISSILE-MOD 0.60 AIRCRAFT-CARRIER 0.65 APC SILKWORM-MISSILE-MOD 0.65 SILKWORM-MISSILE-MOD 0.46 SUPER-ETENDARD-FIGHTER SILKWORM-MISSILE-MOD 0.74 SILKWORM-MISSILE-MOD 0.66 FIGHTER-ATTACK-PLANE SILKWORM-MISSILE-MOD 0.83 SILKWORM-MISSILE-MOD 0.83 PATROL-WATERCRAFT SILKWORM-MISSILE-MOD 0.64 SILKWORM-MISSILE-MOD 0.28 PATROL-BOAT-RIVER SILKWORM-MISSILE-MOD 0.89 SILKWORM-MISSILE-MOD 0.65 PATROL-BOAT SILKWORM-MISSILE-MOD 0.64 SILKWORM-MISSILE-MOD 0.51 LIGHT-TANK SILKWORM-MISSILE-MOD 0.62 SILKWORM-MISSILE-MOD 0.56 FIGHTER-PLANE SILKWORM-MISSILE-MOD 0.80 AIRCRAFT-CARRIER 0.49 different numbers of exemplars (whole)

  28. New Classes Group-sentence-50 Prob Group-sentence-100 Prob LIGHT-AIRCRAFT-CARRIER AIRCRAFT-CARRIER 0.44 AIRCRAFT-CARRIER 0.57 APC TANK-VEHICLE 0.54 SELF-PROPELLED-ARTILLERY 0.36 SUPER-ETENDARD-FIGHTER HY-4-C-201-MISSILE 0.4 MRBM 0.51 FIGHTER-ATTACK-PLANE ICBM 0.19 MRBM 0.38 PATROL-WATERCRAFT PATROL-CRAFT 0.49 PATROL-CRAFT 0.52 PATROL-BOAT-RIVER PATROL-CRAFT 0.36 PATROL-CRAFT 0.54 PATROL-BOAT PATROL-CRAFT 0.37 PATROL-CRAFT 0.66 LIGHT-TANK TANK-VEHICLE 0.59 TANK-VEHICLE 0.3 FIGHTER-PLANE MRBM 0.38 MRBM 0.38 different numbers of exemplars (sentence)

  29. Groups of experiments Mapping accuracy judged by desired class mapped Group-whole-50 0% Group-whole-100 11% Group-sentence-50 67% Group-sentence-100 56% Comparison of mapping accuracy of different groups of experiments Higher Conditional Probability

  30. HUMAN MAN WOMAN Experiment with LIVING_THINGS ontology • P(MAN | HUMAN) • P (WOMAN | HUMAN) • Find a mapping for GIRL

  31. Conditional Probability Using first 50 exemplars Using first 100 exemplars Using first 200 exemplars P(MAN | HUMAN) 0.75 0.58 0.62 P(WOMAN | HUMAN) 0.24 0.41 0.38 WOMAN HUMAN MAN Actual Experiment Results: L-1 Results of experiment (1)

  32. P(ANIMAL | GIRL) 0.83 P(PLANT | GIRL) 0.17 P(HUMAN | GIRL) 0.92 P(ANIMAL | GIRL) 0.76 P(CAT | GIRL) 0.08 P(PLANT | GIRL) 0.23 P(WOMAN | GIRL) 0.63 P(HUMAN | GIRL) 0.70 P(MAN | GIRL) 0.37 P(CAT | GIRL) 0.30 P(MAN | GIRL) 0 P(DOG | GIRL) 0.56 P(WOMAN | GIRL) 1 P(CAT | GIRL) 0.01 P(HUMAN | GIRL) 0.43 P(PYCNOGONID | GIRL) 0 Actual Experiment Results: L-2 With clustering on exemplars Without clustering on exemplars with additional classes

  33. Conditional Probability Using first 50 exemplars Using first 100 exemplars Using first 200 exemplars P(ANIMAL | GIRL) 0.66 0.53 0.77 P(PLANT | GIRL) 0.34 0.47 0.23 P(HUMAN | GIRL) 0.86 0.56 0.43 P(CAT | GIRL) 0.01 0.15 0.01 P(DOG | GIRL) 0.13 0.29 0.56 P(PYCNOGONID | GIRL) 0 0 0 P(MAN | GIRL) 0.02 0.03 0 P(WOMAN | GIRL) 0.98 0.97 1 Actual Experiment Results: L-3 Comparison between different numbers of exemplars (sentence)

  34. Concepts Queries living+things Living+things animal Living+things+animal+Animalia plant Living+things+plant+Plantae cat Living+things+animal+Animalia+cat+Felidae human Living+things+animal+Animalia+human+intelligent man Living+things+animal+Animalia+human+intelligent+man+male woman Living+things+animal+Animalia+human+intelligent+woman+female tree Living+things+plant+Plantae+tree grass Living+things+plant+Plantae+grass frutex Living+things+plant+Plantae+tree+Frutex arbor Living+things+plant+Plantae+tree+arbor Actual Experiment Results: Different Queries Queries augmented with class properties

  35. Conditional Probability Whole Keyword Sentences P(MAN | HUMAN) 0.91 0.93 P(WOMAN | HUMAN) 0.09 0.07 Conditional Probability Whole Keyword Sentences WOMAN HUMAN MAN P(ANIMAL | GIRL) 0.9 0.83 P(PLANT | GIRL) 0.1 0.17 P(HUMAN | GIRL) 0.78 0.83 P(CAT | GIRL) 0.22 0.17 P(MAN | GIRL) 0.14 0.16 P(WOMAN | GIRL) 0.86 0.84 Actual Experiment Results: L-4 Results of experiment (1) with new queries Results of experiment (2) with new queries

  36. HUMAN MAN WOMAN Limitation 1: An exemplar is not a sample of a concept • An exemplar is a combination of strings that represent some usage of a concept. • An exemplar is not an instance of a concept. • The way we calculate conditional probability is an estimation.

  37. Limitation 2: Popularity does not equal relevancy • Limited by a search engine’s algorithm • PageRank™ • Popularity does not equal relevancy • Weight cannot be specified for words in a search query

  38. Limitation 3: Relevancy does not equal to similarity Search Results for concept A Text related to concept A Text against concept A Text for concept A i.e. desired exemplars Text for related concept B

  39. Related Research • UMBC OntoMapper • Sushama Prasad, Peng Yun and Finin Tim, A Tool for Mapping between Two Ontologies Using Explicit Information, AAMAS 2002 Workshop on Ontologies and Agent Systems, 2002. • CAIMEN • Lacher S. Martin and Groh Georg ,Facilitating the Exchange of Explicit Knowledge through Ontology Mappings, Proc of the Fourteenth International FLAIRS conference, 2001. • GLUE • Doan Anhai, Madhavan Jayant, Dhamankar Robin, Domingos Pedro, and Halevy Alon, Learning to Match Ontologies on the Semantic Web, WWW2002, May, 2002. • Google Conditional Probability • P(HUMAN | MAN) = 1.77 billion / 2.29 billion = 0.77 • P(HUMAN | WOMAN) = 0.6 billion / 2.29 billion = 0.26 • Wyatt D., Philipose M., and Choudhury T., Unsupervised Activity Recognition Using Automatically Mined Common Sense. Proceedings of AAAI-05. pp. 21-27.

  40. Conclusion and Future Work • Text retrieved from the web can be used as exemplars for text classification based ontology mapping • Many parameters affect the quality of the exemplars • There are noise contained in the processed documents • Future work • Clustering

  41. Questions

More Related