Ontology-driven visualization system for semantic searching
Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especia...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ha, Inay [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2011 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media, LLC 2011 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 71(2011), 2 vom: 03. Nov., Seite 947-965 |
|---|---|
| Übergeordnetes Werk: |
volume:71 ; year:2011 ; number:2 ; day:03 ; month:11 ; pages:947-965 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-011-0889-8 |
|---|
| Katalog-ID: |
OLC2035010837 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2035010837 | ||
| 003 | DE-627 | ||
| 005 | 20230503192654.0 | ||
| 007 | tu | ||
| 008 | 200819s2011 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11042-011-0889-8 |2 doi | |
| 035 | |a (DE-627)OLC2035010837 | ||
| 035 | |a (DE-He213)s11042-011-0889-8-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 070 |a 004 |q VZ |
| 100 | 1 | |a Ha, Inay |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Ontology-driven visualization system for semantic searching |
| 264 | 1 | |c 2011 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media, LLC 2011 | ||
| 520 | |a Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization. | ||
| 650 | 4 | |a Ontology-driven visualization | |
| 650 | 4 | |a Semantic search | |
| 650 | 4 | |a Ontology modeling | |
| 650 | 4 | |a Ontology population | |
| 650 | 4 | |a Technical manual | |
| 700 | 1 | |a Oh, Kyeong-Jin |4 aut | |
| 700 | 1 | |a Hong, Myung-Duk |4 aut | |
| 700 | 1 | |a Lee, Yeon-Ho |4 aut | |
| 700 | 1 | |a Rosli, Ahmad Nurzid |4 aut | |
| 700 | 1 | |a Jo, Geun-Sik |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Springer US, 1995 |g 71(2011), 2 vom: 03. Nov., Seite 947-965 |w (DE-627)189064145 |w (DE-600)1287642-2 |w (DE-576)052842126 |x 1380-7501 |7 nnns |
| 773 | 1 | 8 | |g volume:71 |g year:2011 |g number:2 |g day:03 |g month:11 |g pages:947-965 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11042-011-0889-8 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OLC-BUB | ||
| 912 | |a SSG-OLC-MKW | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a GBV_ILN_4318 | ||
| 951 | |a AR | ||
| 952 | |d 71 |j 2011 |e 2 |b 03 |c 11 |h 947-965 | ||
| author_variant |
i h ih k j o kjo m d h mdh y h l yhl a n r an anr g s j gsj |
|---|---|
| matchkey_str |
article:13807501:2011----::nooyrvniulztossefre |
| hierarchy_sort_str |
2011 |
| publishDate |
2011 |
| allfields |
10.1007/s11042-011-0889-8 doi (DE-627)OLC2035010837 (DE-He213)s11042-011-0889-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Ha, Inay verfasserin aut Ontology-driven visualization system for semantic searching 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization. Ontology-driven visualization Semantic search Ontology modeling Ontology population Technical manual Oh, Kyeong-Jin aut Hong, Myung-Duk aut Lee, Yeon-Ho aut Rosli, Ahmad Nurzid aut Jo, Geun-Sik aut Enthalten in Multimedia tools and applications Springer US, 1995 71(2011), 2 vom: 03. Nov., Seite 947-965 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:71 year:2011 number:2 day:03 month:11 pages:947-965 https://doi.org/10.1007/s11042-011-0889-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4318 AR 71 2011 2 03 11 947-965 |
| spelling |
10.1007/s11042-011-0889-8 doi (DE-627)OLC2035010837 (DE-He213)s11042-011-0889-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Ha, Inay verfasserin aut Ontology-driven visualization system for semantic searching 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization. Ontology-driven visualization Semantic search Ontology modeling Ontology population Technical manual Oh, Kyeong-Jin aut Hong, Myung-Duk aut Lee, Yeon-Ho aut Rosli, Ahmad Nurzid aut Jo, Geun-Sik aut Enthalten in Multimedia tools and applications Springer US, 1995 71(2011), 2 vom: 03. Nov., Seite 947-965 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:71 year:2011 number:2 day:03 month:11 pages:947-965 https://doi.org/10.1007/s11042-011-0889-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4318 AR 71 2011 2 03 11 947-965 |
| allfields_unstemmed |
10.1007/s11042-011-0889-8 doi (DE-627)OLC2035010837 (DE-He213)s11042-011-0889-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Ha, Inay verfasserin aut Ontology-driven visualization system for semantic searching 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization. Ontology-driven visualization Semantic search Ontology modeling Ontology population Technical manual Oh, Kyeong-Jin aut Hong, Myung-Duk aut Lee, Yeon-Ho aut Rosli, Ahmad Nurzid aut Jo, Geun-Sik aut Enthalten in Multimedia tools and applications Springer US, 1995 71(2011), 2 vom: 03. Nov., Seite 947-965 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:71 year:2011 number:2 day:03 month:11 pages:947-965 https://doi.org/10.1007/s11042-011-0889-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4318 AR 71 2011 2 03 11 947-965 |
| allfieldsGer |
10.1007/s11042-011-0889-8 doi (DE-627)OLC2035010837 (DE-He213)s11042-011-0889-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Ha, Inay verfasserin aut Ontology-driven visualization system for semantic searching 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization. Ontology-driven visualization Semantic search Ontology modeling Ontology population Technical manual Oh, Kyeong-Jin aut Hong, Myung-Duk aut Lee, Yeon-Ho aut Rosli, Ahmad Nurzid aut Jo, Geun-Sik aut Enthalten in Multimedia tools and applications Springer US, 1995 71(2011), 2 vom: 03. Nov., Seite 947-965 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:71 year:2011 number:2 day:03 month:11 pages:947-965 https://doi.org/10.1007/s11042-011-0889-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4318 AR 71 2011 2 03 11 947-965 |
| allfieldsSound |
10.1007/s11042-011-0889-8 doi (DE-627)OLC2035010837 (DE-He213)s11042-011-0889-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Ha, Inay verfasserin aut Ontology-driven visualization system for semantic searching 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization. Ontology-driven visualization Semantic search Ontology modeling Ontology population Technical manual Oh, Kyeong-Jin aut Hong, Myung-Duk aut Lee, Yeon-Ho aut Rosli, Ahmad Nurzid aut Jo, Geun-Sik aut Enthalten in Multimedia tools and applications Springer US, 1995 71(2011), 2 vom: 03. Nov., Seite 947-965 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:71 year:2011 number:2 day:03 month:11 pages:947-965 https://doi.org/10.1007/s11042-011-0889-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4318 AR 71 2011 2 03 11 947-965 |
| language |
English |
| source |
Enthalten in Multimedia tools and applications 71(2011), 2 vom: 03. Nov., Seite 947-965 volume:71 year:2011 number:2 day:03 month:11 pages:947-965 |
| sourceStr |
Enthalten in Multimedia tools and applications 71(2011), 2 vom: 03. Nov., Seite 947-965 volume:71 year:2011 number:2 day:03 month:11 pages:947-965 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Ontology-driven visualization Semantic search Ontology modeling Ontology population Technical manual |
| dewey-raw |
070 |
| isfreeaccess_bool |
false |
| container_title |
Multimedia tools and applications |
| authorswithroles_txt_mv |
Ha, Inay @@aut@@ Oh, Kyeong-Jin @@aut@@ Hong, Myung-Duk @@aut@@ Lee, Yeon-Ho @@aut@@ Rosli, Ahmad Nurzid @@aut@@ Jo, Geun-Sik @@aut@@ |
| publishDateDaySort_date |
2011-11-03T00:00:00Z |
| hierarchy_top_id |
189064145 |
| dewey-sort |
270 |
| id |
OLC2035010837 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2035010837</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503192654.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2011 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-011-0889-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2035010837</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11042-011-0889-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ha, Inay</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Ontology-driven visualization system for semantic searching</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2011</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2011</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ontology-driven visualization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semantic search</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ontology modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ontology population</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Technical manual</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Oh, Kyeong-Jin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hong, Myung-Duk</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lee, Yeon-Ho</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rosli, Ahmad Nurzid</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jo, Geun-Sik</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">71(2011), 2 vom: 03. Nov., Seite 947-965</subfield><subfield code="w">(DE-627)189064145</subfield><subfield code="w">(DE-600)1287642-2</subfield><subfield code="w">(DE-576)052842126</subfield><subfield code="x">1380-7501</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:71</subfield><subfield code="g">year:2011</subfield><subfield code="g">number:2</subfield><subfield code="g">day:03</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:947-965</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11042-011-0889-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4318</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">71</subfield><subfield code="j">2011</subfield><subfield code="e">2</subfield><subfield code="b">03</subfield><subfield code="c">11</subfield><subfield code="h">947-965</subfield></datafield></record></collection>
|
| author |
Ha, Inay |
| spellingShingle |
Ha, Inay ddc 070 misc Ontology-driven visualization misc Semantic search misc Ontology modeling misc Ontology population misc Technical manual Ontology-driven visualization system for semantic searching |
| authorStr |
Ha, Inay |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)189064145 |
| format |
Article |
| dewey-ones |
070 - News media, journalism & publishing 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1380-7501 |
| topic_title |
070 004 VZ Ontology-driven visualization system for semantic searching Ontology-driven visualization Semantic search Ontology modeling Ontology population Technical manual |
| topic |
ddc 070 misc Ontology-driven visualization misc Semantic search misc Ontology modeling misc Ontology population misc Technical manual |
| topic_unstemmed |
ddc 070 misc Ontology-driven visualization misc Semantic search misc Ontology modeling misc Ontology population misc Technical manual |
| topic_browse |
ddc 070 misc Ontology-driven visualization misc Semantic search misc Ontology modeling misc Ontology population misc Technical manual |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Multimedia tools and applications |
| hierarchy_parent_id |
189064145 |
| dewey-tens |
070 - News media, journalism & publishing 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Multimedia tools and applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 |
| title |
Ontology-driven visualization system for semantic searching |
| ctrlnum |
(DE-627)OLC2035010837 (DE-He213)s11042-011-0889-8-p |
| title_full |
Ontology-driven visualization system for semantic searching |
| author_sort |
Ha, Inay |
| journal |
Multimedia tools and applications |
| journalStr |
Multimedia tools and applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2011 |
| contenttype_str_mv |
txt |
| container_start_page |
947 |
| author_browse |
Ha, Inay Oh, Kyeong-Jin Hong, Myung-Duk Lee, Yeon-Ho Rosli, Ahmad Nurzid Jo, Geun-Sik |
| container_volume |
71 |
| class |
070 004 VZ |
| format_se |
Aufsätze |
| author-letter |
Ha, Inay |
| doi_str_mv |
10.1007/s11042-011-0889-8 |
| dewey-full |
070 004 |
| title_sort |
ontology-driven visualization system for semantic searching |
| title_auth |
Ontology-driven visualization system for semantic searching |
| abstract |
Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization. © Springer Science+Business Media, LLC 2011 |
| abstractGer |
Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization. © Springer Science+Business Media, LLC 2011 |
| abstract_unstemmed |
Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization. © Springer Science+Business Media, LLC 2011 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4318 |
| container_issue |
2 |
| title_short |
Ontology-driven visualization system for semantic searching |
| url |
https://doi.org/10.1007/s11042-011-0889-8 |
| remote_bool |
false |
| author2 |
Oh, Kyeong-Jin Hong, Myung-Duk Lee, Yeon-Ho Rosli, Ahmad Nurzid Jo, Geun-Sik |
| author2Str |
Oh, Kyeong-Jin Hong, Myung-Duk Lee, Yeon-Ho Rosli, Ahmad Nurzid Jo, Geun-Sik |
| ppnlink |
189064145 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11042-011-0889-8 |
| up_date |
2024-07-03T23:25:29.214Z |
| _version_ |
1803602235722039296 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2035010837</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503192654.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2011 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-011-0889-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2035010837</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11042-011-0889-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ha, Inay</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Ontology-driven visualization system for semantic searching</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2011</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2011</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ontology-driven visualization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semantic search</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ontology modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ontology population</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Technical manual</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Oh, Kyeong-Jin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hong, Myung-Duk</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lee, Yeon-Ho</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rosli, Ahmad Nurzid</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jo, Geun-Sik</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">71(2011), 2 vom: 03. Nov., Seite 947-965</subfield><subfield code="w">(DE-627)189064145</subfield><subfield code="w">(DE-600)1287642-2</subfield><subfield code="w">(DE-576)052842126</subfield><subfield code="x">1380-7501</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:71</subfield><subfield code="g">year:2011</subfield><subfield code="g">number:2</subfield><subfield code="g">day:03</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:947-965</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11042-011-0889-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4318</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">71</subfield><subfield code="j">2011</subfield><subfield code="e">2</subfield><subfield code="b">03</subfield><subfield code="c">11</subfield><subfield code="h">947-965</subfield></datafield></record></collection>
|
| score |
7.402545 |