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ÇѱÛÁ¦¸ñ(Korean Title) |
ÀÇ¹Ì ÁßÀǼºÀ» °í·ÁÇÑ ¿ÂÅç·ÎÁö ±â¹Ý ¸ÞŸµ¥ÀÌŸÀÇ ÀÚµ¿ »ý¼º |
¿µ¹®Á¦¸ñ(English Title) |
Ontology-based Automated Metadata Generation Considering Semantic Ambiguity |
ÀúÀÚ(Author) |
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¿ø¹®¼ö·Ïó(Citation) |
VOL 33 NO. 11 PP. 0986 ~ 0998 (2006. 11) |
Çѱ۳»¿ë (Korean Abstract) |
ÀÎÅͳÝÀÇ ¹ßÀüÀ¸·Î ¹æ´ëÇØÁø Á¤º¸¸¦ ÄÄÇ»ÅÍ°¡ ÀÌÇØÇÏ°í È¿À²ÀûÀ¸·Î °ü¸®Çϱâ À§Çؼ´Â ½Ã¸Çƽ À¥ ±â¹ÝÀÇ ¸ÞŸµ¥ÀÌŸ°¡ ¹Ýµå½Ã ÇÊ¿äÇÏ´Ù. ±×·¯³ª ¸ÞŸµ¥ÀÌŸ »ý¼º ½Ã ÀÇ¹Ì ÁßÀǼºÀ» °¡Áø Á¤º¸°¡ Á¸ÀçÇϸç ÀÌ ¹®Á¦ÀÇ ÇØ°áÃ¥ÀÌ ÇÊ¿äÇÏ´Ù. º» ³í¹®¿¡¼´Â ¼øÂ÷ÀûÀ¸·Î Á¸ÀçÇÒ ¼ö ÀÖ´Â ´Ü¾îµéÀÇ È®·ü ¸ðµ¨À» ÀÌ¿ëÇÏ¿© ¹®¼¿Í °°Àº Á¤º¸¿¡ Æ÷ÇÔµÈ Àǹ̰¡ ¾Ö¸ÅÇÑ ´Ü¾î¸¦ °ü·Ã¼ºÀÌ ³ôÀº ¸ðµ¨ÀÇ °³³äÀ¸·Î ¸ÞŸµ¥ÀÌŸ¸¦ »ý¼ºÇÏ´Â ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÑ ¹æ¹ý¿¡¼ ¸ÞŸµ¥ÀÌŸ¸¦ »ý¼º ÇÒ ¶§, ¿ÂÅç·ÎÁö¿¡ Á¤ÀÇµÈ °³³äµé °£ÀÇ ÁßÀǼºÀ» °í·ÁÇÏ°í ¸íĪ(named entity)ÀÇ ÀϺΠ´Ü¾î¿¡ ´ëÇÑ ÀνÄÀ» À§ÇØ Àº´Ð ¸¶¸£ÄÚÇÁ ¸ðµ¨(Hidden Markov Model)À» »ç¿ëÇÑ´Ù. ¸ÕÀú ¿ÂÅç·ÎÁö¿¡ Á¤ÀÇµÈ °¢ Ŭ·¡½º(class)ÀÇ ÀνºÅϽº(instance)¸¦ ÀνÄÇϱâ À§ÇÑ ¸¶¸£ÄÚÇÁ ¸ðµ¨À» »ý¼ºÇÑ´Ù. ´ÙÀ½À¸·Î ¹®¼·ÎºÎÅÍ Àǹ̰¡ ¾Ö¸ÅÇÑ ´Ü¾îÀÇ Àǹ̸¦ ÆľÇÇÒ ¼ö ÀÖ´Â »óȲÁ¤º¸(context)¸¦ »ý¼ºÇÏ°í, »óȲÁ¤º¸¿¡ Æ÷ÇÔµÈ ´Ü¾îµéÀÇ ¼ø¼¿¡ ´ëÀÀÇÏ´Â ÃÖÀûÀÇ ¸¶¸£ÄÚÇÁ ¸ðµ¨À» ã¾Æ ¸ÞŸµ¥ÀÌŸ »ý¼º½ÃÀÇ ÁßÀǼº ¹®Á¦¸¦ ÇØ°áÇÑ´Ù. Á¦¾ÈÇÑ ¹æ¹ýÀ¸·Î Àü»êÇаü·Ã ³í¹®¿¡ ´ëÇØ Àǹ̰¡ ¾Ö¸ÅÇÑ 7°³ÀÇ ´Ü¾î¸¦ ÃßÃâÇÏ¿© ½ÇÇèÇÏ¿´´Ù. ±× °á°ú »óȲÁ¤º¸¿¡ Á¸ÀçÇÏ´Â °³Ã¼(entity)ÀÇ Àǹ̺ηùµé Áß °¡Àå ºó¹øÇÑ ÀÇ¹Ì ºÎ·ù·Î ¾Ö¸ÅÇÑ ´Ü¾îÀÇ Àǹ̸¦ ¼±Á¤ÇÑ SemTagº¸´Ù Á¤È®µµ ¸é¿¡¼ 18%Á¤µµÀÇ ³ªÀº ¼º´ÉÀ» ³ªÅ¸³»¾ú´Ù. |
¿µ¹®³»¿ë (English Abstract) |
There has been an increasing necessity of Semantic Web-based metadata that helps computers efficiently understand and manage an information increased with the growth of Internet. However, it seems inevitable to face some semantically ambiguous information when metadata is generated. Therefore, we need a solution to this problem. This paper proposes a new method for automated metadata generation with the help of a concept of class, in which some ambiguous words imbedded in information such as documents are semantically more related to others, by using probability model of consequent words. We considers ambiguities among defined concepts in ontology and uses the Hidden Markov Model to be aware of part of a named entity. First of all, we construct a Markov Models a better understanding of the named entity of each class defined in ontology. Next, we generate the appropriate context from a text to understand the meaning of a semantically ambiguous word and solve the problem of ambiguities during generating metadata by searching the optimized the Markov Model corresponding to the sequence of words included in the context. We experiment with seven semantically ambiguous words that are extracted from computer science thesis. The experimental result demonstrates successful performance, the accuracy improved by about 18%, compared with SemTag, which has been known as an effective application for assigning a specific meaning to an ambiguous word based on its context. |
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Metadata
Automated Metadata Generation
Context
Semantic Web
Ontology
Semantic Ambiguity
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