1. kisofire@chosun.ac.kr CHOSUN UNIV. The Study on the Semantic Image Retrieval Using the Cognitive Spatial Relationships in the Semantic Web Hyunjang Kong,Myunggwun Hwang,Kwansang Na,Pankoo Kim

2. CHOSUN UNIV. kisofire@chosun.ac.kr Contents Introduction Related Works Our Approach Test and Experimental Results Evaluations Conclusion

3. CHOSUN UNIV. kisofire@chosun.ac.kr Introduction Huge number of data in the web Image data is rapidly increasing Object Based Spatial Relationships VS Cognitive Spatial Relationships Building a Spatial Relationships Ontology

4. CHOSUN UNIV. kisofire@chosun.ac.kr Related Works Information Retrieval System –Keyword Matching –Very important technique on the web environment –Process the various information items Text Documents, Images, Sounds and etc. –Generally, accuracy is low

5. CHOSUN UNIV. kisofire@chosun.ac.kr Related Works Ontology based Image Retrieval –Try to solve the heterogeneous between the terminologies –Need the extra works Creating and Maintaining the ontologies –It is still unsuitable for the image retrieval system Because it doesn’t consider the features of the images

6. CHOSUN UNIV. kisofire@chosun.ac.kr Related Works The Spatial Description Logic –Region Connection Calculus : RCC-8 –Spatial representation is regular subsets of the topological space –Elementary binary relationships between the regions PO, NTPP, TPP, EQ, TPP -1, NTPP -1, EC, DC

7. CHOSUN UNIV. kisofire@chosun.ac.kr Our Approach Background Knowledge of the Cognitive Spatial Relationships

8. CHOSUN UNIV. kisofire@chosun.ac.kr Our Approach Building Process of the Spatial Relationships Ontology –Defining the Cognitive Spatial Relationships –User Research –Using WordNet and Dictionary –OWL Representation

9. CHOSUN UNIV. kisofire@chosun.ac.kr Our Approach Definition of the Cognitive Spatial Relationships

10. CHOSUN UNIV. kisofire@chosun.ac.kr Our Approach User Research –200 images –10 people –Clustering the spatial words

11. CHOSUN UNIV. kisofire@chosun.ac.kr Our Approach Architecture of the spatial ontology –Upper level –Basic spatial words level –Instance level

12. CHOSUN UNIV. kisofire@chosun.ac.kr Our Approach WordNet and Dictionary Matching Cognitive spatial relationships Research wordsWordNet matching words connectAttach Connect, link, tie, link up, fasten, touch, adjoin, meet, contact connectKiss Buss, osculate disconnectChase Chase after, trail, tail, tag, give chase, god, go after, pursue, follow disconnectJump Leap, bound, spring partofFloat Drift, be adrift, blow, swim, transport partofHide Conceal, shroud, enshroud, cover, obscure, blot out, obliterate, veil The spatial propositions based on OXFORD Dictionary connect On, along, across, through disconnect Over, under, above, below, by, beside, near, before, behind partof At, in, around, round

13. CHOSUN UNIV. kisofire@chosun.ac.kr Our Approach OWL Representation

14. CHOSUN UNIV. kisofire@chosun.ac.kr Test and Experimental Results System Architecture –Contents provider interface –Ontology part –User interface

15. CHOSUN UNIV. kisofire@chosun.ac.kr Test and Experimental Results Test Environment –Queries 1. Only one word query – e.g. swan 2. Two words query – e.g. swan and lake 3. Query containing the spatial relationships – e.g. swan in the lake 4. Natural Language query containing the spatial verbs – e.g. swimming swan 5. Natural Languages query containing the spatial verbs and proposition – e.g. swan swims in the lake

16. CHOSUN UNIV. kisofire@chosun.ac.kr Test and Experimental Results Accuracy Measurement Experimental Results

17. CHOSUN UNIV. kisofire@chosun.ac.kr Evaluations

18. CHOSUN UNIV. kisofire@chosun.ac.kr Evaluations

19. CHOSUN UNIV. kisofire@chosun.ac.kr Conclusion and Future Works Definition of the Cognitive Spatial Relationships Applying them to the Image Retrieval System Still have Limitation : Semi-Automatic Our study presents the vision of the semantic image retrieval and natural language query processing

20. CHOSUN UNIV. kisofire@chosun.ac.kr Does Anyone Have Any Questions?