Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
Spark ÇÁ·¹ÀÓ¿öÅ©¸¦ Àû¿ëÇÑ ´ë¿ë·® SHIF ¿ÂÅç·ÎÁö Ãß·Ð ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
An Approach of Scalable SHIF Ontology Reasoning using Spark Framework |
ÀúÀÚ(Author) |
±èÁ¦¹Î
¹Ú¿µÅÃ
Je-Min Kim
Young-Tack Park
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¿ø¹®¼ö·Ïó(Citation) |
VOL 42 NO. 10 PP. 1195 ~ 1206 (2015. 10) |
Çѱ۳»¿ë (Korean Abstract) |
Áö½Ä °ü¸® ½Ã½ºÅÛÀ» ¿î¿µÇϱâ À§Çؼ´Â ´ë·®ÀÇ Áö½Ä Á¤º¸¸¦ ÀÚµ¿À¸·Î Ãß·Ð ¹× °ü¸®ÇÏ´Â ±â¼úÀÌ ÇÊ¿äÇÏ´Ù. ÇöÀç, ÀÌ·¯ÇÑ ½Ã½ºÅÛÀÇ ´ë´Ù¼ö´Â ÄÄÇ»ÅÍ°£ÀÇ Áö½Ä Á¤º¸¸¦ ÀÚµ¿À¸·Î ±³È¯ÇÏ°í ½º½º·Î »õ·Î¿î Áö½ÄÀ» Ãß·ÐÇϱâ À§ÇØ ¿ÂÅç·ÎÁö¸¦ Àû¿ëÇÏ°í ÀÖ´Ù. µû¶ó¼ ´ë¿ë·®ÀÇ ¿ÂÅç·ÎÁö¸¦ ´ë»óÀ¸·Î »õ·Î¿î Á¤º¸¸¦ Ãß·ÐÇÏ´Â È¿À²ÀûÀÎ ±â¼úÀÌ ¿ä±¸µÇ°í ÀÖ´Ù. º» ³í¹®Àº ºÐ»ê Ŭ·¯½ºÅÍÀÇ ¸Þ¸ð¸®»ó¿¡¼ MapReduce¿Í À¯»çÇÑ ÀÛ¾÷À» ¼öÇàÇÏ´Â Spark ÇÁ·¹ÀÓ¿öÅ©¸¦ Àû¿ëÇÏ¿©, SHIF ¼öÁØÀ¸·Î ÀÛ¼ºµÈ ´ë¿ë·®ÀÇ ¿ÂÅç·ÎÁö¸¦ ±ÔÄ¢ ±â¹ÝÀ¸·Î Ãß·ÐÇÏ´Â ±â¼ú¿¡ ´ëÇؼ Á¦¾ÈÇÑ´Ù. ÀÌ¿¡ º» ³í¹®Àº ´ÙÀ½ 3 °¡Áö¿¡ ÃÊÁ¡À» ¸ÂÃß¾î ¼³¸íÀ» ÇÑ´Ù. Ŭ·¯½ºÅͳ»ÀÇ ºÐ»êµÈ ¸Þ¸ð¸®»ó¿¡¼ ´ë¿ë·® Ãß·ÐÀ» ½Ç½ÃÇϱâ À§Çؼ, ¸ÕÀú °¢ Ãß·Ð ±ÔÄ¢¿¡ µû¶ó ´ë¿ë·®ÀÇ ¿ÂÅç·ÎÁö Æ®¸®ÇÃÀ» È¿°úÀûÀ¸·Î ºÐ·ùÇÏ¿© ÀûÀçÇϱâ À§ÇÑ ÀڷᱸÁ¶, µÎ ¹ø° ±ÔÄ¢°£ÀÇ Á¾¼Ó °ü°è¿Í »óÈ£ ¿¬°ü¼º¿¡ µû¸¥ ±ÔÄ¢ ½ÇÇà ¼ø¼¿Í ¹Ýº¹ Á¶°Ç Á¤ÀÇ, ¸¶Áö¸·À¸·Î ±ÔÄ¢ ½ÇÇà¿¡ ÇÊ¿äÇÑ ¸í·ÉÀ» Á¤ÀÇÇÏ°í ÀÌ·¯ÇÑ ¸í·É¾î¸¦ ½ÇÇàÇÏ¿© Ãß·ÐÀ» ¼öÇàÇÏ´Â ¾Ë°í¸®Áò¿¡ ´ëÇØ ¼³¸íÇÑ´Ù. Á¦¾ÈÇÏ´Â ±â¹ýÀÇ È¿À²¼ºÀ» °ËÁõÇϱâ À§ÇØ, ¿ÂÅç·ÎÁö Ã߷аú °Ë»ö ¼Óµµ¸¦ Æò°¡ÇÏ´Â °ø½Ä µ¥ÀÌÅÍÀÎ LUBMÀ» ´ë»óÀ¸·Î ½ÇÇèÀ» ¼öÇàÇÏ¿´´Ù. ´ëÇ¥ÀûÀÎ ºÐ»ê Ŭ·¯½ºÅÍ ±â¹Ý ´ë¿ë·® ¿ÂÅç·ÎÁö Ãß·Ð ¿£ÁøÀÎ WebPie¿Í ºñ±³ ½ÇÇèÇÑ °á°ú, LUBM¿¡ ´ëÇؼ WebPieÀÇ Ã߷Р󸮷®ÀÌ 553 Æ®¸®ÇÃ/ÃÊ Àε¥ ºñÇØ 284¹è °³¼±µÈ 157k Æ®¸®ÇÃ/ÃÊÀÇ ¼º´É Çâ»óÀÌ ÀÖ¾ú´Ù.
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¿µ¹®³»¿ë (English Abstract) |
For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.
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Å°¿öµå(Keyword) |
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Áö½Ä Ãß·Ð
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Spark
ontology
knowledge reasoning
scalable data
distributed computing cluster
spark
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