Table understanding using a rule engine
•We propose an approach to table understanding using a rule engine.•It is restricted by tasks of table analysis and interpretation.•Spatial, style, and text information of tables is used for table understanding.•Experimental results show the applicability of approach to a wide range of tables.•The a...
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| Veröffentlicht in: | Expert systems with applications 2015-02, Vol.42 (2), p.929-937 |
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| container_title | Expert systems with applications |
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| creator | Shigarov, Alexey O. |
| description | •We propose an approach to table understanding using a rule engine.•It is restricted by tasks of table analysis and interpretation.•Spatial, style, and text information of tables is used for table understanding.•Experimental results show the applicability of approach to a wide range of tables.•The approach is designed for unstructured tabular data integration.
The paper discusses issues on the conversion of tabular data from unstructured to structured form. Particularly, we propose an approach to table understanding (i.e. recovering semantic relationships in a table), which is designed for unstructured tabular data integration. Our approach is based on using a rule engine. It is assumed that spatial, style (typographical), and natural language information can be used for table analysis and interpretation. The CELLS system based on the approach has been developed for integrating unstructured tabular data presented in Excel spreadsheet format. Experimental results show that the approach and system can be applied to a wide range of tables from statistical and financial reports. |
| doi_str_mv | 10.1016/j.eswa.2014.08.045 |
| format | Article |
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The paper discusses issues on the conversion of tabular data from unstructured to structured form. Particularly, we propose an approach to table understanding (i.e. recovering semantic relationships in a table), which is designed for unstructured tabular data integration. Our approach is based on using a rule engine. It is assumed that spatial, style (typographical), and natural language information can be used for table analysis and interpretation. The CELLS system based on the approach has been developed for integrating unstructured tabular data presented in Excel spreadsheet format. Experimental results show that the approach and system can be applied to a wide range of tables from statistical and financial reports.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2014.08.045</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Conversion ; Data integration ; Engines ; Expert systems ; Information extraction from tables ; Semantics ; Spreadsheets ; Table canonicalization ; Table model ; Table understanding ; Tables ; Tables (data) ; Unstructured tabular data integration</subject><ispartof>Expert systems with applications, 2015-02, Vol.42 (2), p.929-937</ispartof><rights>2014 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c333t-d7cd208cddebea897cbb888e76ba9183209d0ecf7a1b3b651d378e2de74ad4193</citedby><cites>FETCH-LOGICAL-c333t-d7cd208cddebea897cbb888e76ba9183209d0ecf7a1b3b651d378e2de74ad4193</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0957417414005272$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27903,27904,65309</link.rule.ids></links><search><creatorcontrib>Shigarov, Alexey O.</creatorcontrib><title>Table understanding using a rule engine</title><title>Expert systems with applications</title><description>•We propose an approach to table understanding using a rule engine.•It is restricted by tasks of table analysis and interpretation.•Spatial, style, and text information of tables is used for table understanding.•Experimental results show the applicability of approach to a wide range of tables.•The approach is designed for unstructured tabular data integration.
The paper discusses issues on the conversion of tabular data from unstructured to structured form. Particularly, we propose an approach to table understanding (i.e. recovering semantic relationships in a table), which is designed for unstructured tabular data integration. Our approach is based on using a rule engine. It is assumed that spatial, style (typographical), and natural language information can be used for table analysis and interpretation. The CELLS system based on the approach has been developed for integrating unstructured tabular data presented in Excel spreadsheet format. Experimental results show that the approach and system can be applied to a wide range of tables from statistical and financial reports.</description><subject>Conversion</subject><subject>Data integration</subject><subject>Engines</subject><subject>Expert systems</subject><subject>Information extraction from tables</subject><subject>Semantics</subject><subject>Spreadsheets</subject><subject>Table canonicalization</subject><subject>Table model</subject><subject>Table understanding</subject><subject>Tables</subject><subject>Tables (data)</subject><subject>Unstructured tabular data integration</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp9kD1PwzAQhi0EEqXwB5i6wZJwjpOcI7GgigJSJZYyW_64Vq5Sp9gJiH9PojKz3Dvc-5x0D2O3HHIOvH7Y55S-dV4AL3OQOZTVGZtxiSKrsRHnbAZNhVnJsbxkVyntATgC4IzdbbRpaTEERzH1OjgfdoshTVMv4jCuKOx8oGt2sdVtopu_nLOP1fNm-Zqt31_elk_rzAoh-syhdQVI6xwZ0rJBa4yUkrA2uuFSFNA4ILtFzY0wdcWdQEmFIyy1K3kj5uz-dPcYu8-BUq8OPllqWx2oG5LiIyNQVCDGanGq2tilFGmrjtEfdPxRHNRkRe3VZEVNVhRINVoZoccTROMTX56iStZTsOR8JNsr1_n_8F_E5mrY</recordid><startdate>20150201</startdate><enddate>20150201</enddate><creator>Shigarov, Alexey O.</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20150201</creationdate><title>Table understanding using a rule engine</title><author>Shigarov, Alexey O.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c333t-d7cd208cddebea897cbb888e76ba9183209d0ecf7a1b3b651d378e2de74ad4193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Conversion</topic><topic>Data integration</topic><topic>Engines</topic><topic>Expert systems</topic><topic>Information extraction from tables</topic><topic>Semantics</topic><topic>Spreadsheets</topic><topic>Table canonicalization</topic><topic>Table model</topic><topic>Table understanding</topic><topic>Tables</topic><topic>Tables (data)</topic><topic>Unstructured tabular data integration</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shigarov, Alexey O.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shigarov, Alexey O.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Table understanding using a rule engine</atitle><jtitle>Expert systems with applications</jtitle><date>2015-02-01</date><risdate>2015</risdate><volume>42</volume><issue>2</issue><spage>929</spage><epage>937</epage><pages>929-937</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>•We propose an approach to table understanding using a rule engine.•It is restricted by tasks of table analysis and interpretation.•Spatial, style, and text information of tables is used for table understanding.•Experimental results show the applicability of approach to a wide range of tables.•The approach is designed for unstructured tabular data integration.
The paper discusses issues on the conversion of tabular data from unstructured to structured form. Particularly, we propose an approach to table understanding (i.e. recovering semantic relationships in a table), which is designed for unstructured tabular data integration. Our approach is based on using a rule engine. It is assumed that spatial, style (typographical), and natural language information can be used for table analysis and interpretation. The CELLS system based on the approach has been developed for integrating unstructured tabular data presented in Excel spreadsheet format. Experimental results show that the approach and system can be applied to a wide range of tables from statistical and financial reports.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2014.08.045</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0957-4174 |
| ispartof | Expert systems with applications, 2015-02, Vol.42 (2), p.929-937 |
| issn | 0957-4174 1873-6793 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1651373503 |
| source | Elsevier ScienceDirect Journals Complete |
| subjects | Conversion Data integration Engines Expert systems Information extraction from tables Semantics Spreadsheets Table canonicalization Table model Table understanding Tables Tables (data) Unstructured tabular data integration |
| title | Table understanding using a rule engine |
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