Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies

Abstract Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and wh...
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Gespeichert in:

Autor*in:	Cifuentes-Silva, Francisco [verfasserIn] Astudillo, Hernán [verfasserIn] Gayo, José Emilio Labra [verfasserIn] Rivera-Polo, Felipe [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Semantic web Data analysis Linked open data Legal informatics Polarization Political alignment

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: SN Computer Science - Springer Nature Singapore, 2020, 5(2024), 5 vom: 31. Mai
Übergeordnetes Werk:	volume:5 ; year:2024 ; number:5 ; day:31 ; month:05

Links:	Volltext

DOI / URN:	10.1007/s42979-024-02933-y

Katalog-ID:	SPR056083580

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allfields	10.1007/s42979-024-02933-y doi (DE-627)SPR056083580 (SPR)s42979-024-02933-y-e DE-627 ger DE-627 rakwb eng Cifuentes-Silva, Francisco verfasserin (orcid)0000-0001-6570-5421 aut Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner. Semantic web (dpeaa)DE-He213 Data analysis (dpeaa)DE-He213 Linked open data (dpeaa)DE-He213 Legal informatics (dpeaa)DE-He213 Polarization (dpeaa)DE-He213 Political alignment (dpeaa)DE-He213 Astudillo, Hernán verfasserin (orcid)0000-0002-6487-5813 aut Gayo, José Emilio Labra verfasserin (orcid)0000-0001-8907-5348 aut Rivera-Polo, Felipe verfasserin aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 5 vom: 31. Mai (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:5 day:31 month:05 https://dx.doi.org/10.1007/s42979-024-02933-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 5 31 05
spelling	10.1007/s42979-024-02933-y doi (DE-627)SPR056083580 (SPR)s42979-024-02933-y-e DE-627 ger DE-627 rakwb eng Cifuentes-Silva, Francisco verfasserin (orcid)0000-0001-6570-5421 aut Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner. Semantic web (dpeaa)DE-He213 Data analysis (dpeaa)DE-He213 Linked open data (dpeaa)DE-He213 Legal informatics (dpeaa)DE-He213 Polarization (dpeaa)DE-He213 Political alignment (dpeaa)DE-He213 Astudillo, Hernán verfasserin (orcid)0000-0002-6487-5813 aut Gayo, José Emilio Labra verfasserin (orcid)0000-0001-8907-5348 aut Rivera-Polo, Felipe verfasserin aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 5 vom: 31. Mai (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:5 day:31 month:05 https://dx.doi.org/10.1007/s42979-024-02933-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 5 31 05
allfields_unstemmed	10.1007/s42979-024-02933-y doi (DE-627)SPR056083580 (SPR)s42979-024-02933-y-e DE-627 ger DE-627 rakwb eng Cifuentes-Silva, Francisco verfasserin (orcid)0000-0001-6570-5421 aut Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner. Semantic web (dpeaa)DE-He213 Data analysis (dpeaa)DE-He213 Linked open data (dpeaa)DE-He213 Legal informatics (dpeaa)DE-He213 Polarization (dpeaa)DE-He213 Political alignment (dpeaa)DE-He213 Astudillo, Hernán verfasserin (orcid)0000-0002-6487-5813 aut Gayo, José Emilio Labra verfasserin (orcid)0000-0001-8907-5348 aut Rivera-Polo, Felipe verfasserin aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 5 vom: 31. Mai (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:5 day:31 month:05 https://dx.doi.org/10.1007/s42979-024-02933-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 5 31 05
allfieldsGer	10.1007/s42979-024-02933-y doi (DE-627)SPR056083580 (SPR)s42979-024-02933-y-e DE-627 ger DE-627 rakwb eng Cifuentes-Silva, Francisco verfasserin (orcid)0000-0001-6570-5421 aut Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner. Semantic web (dpeaa)DE-He213 Data analysis (dpeaa)DE-He213 Linked open data (dpeaa)DE-He213 Legal informatics (dpeaa)DE-He213 Polarization (dpeaa)DE-He213 Political alignment (dpeaa)DE-He213 Astudillo, Hernán verfasserin (orcid)0000-0002-6487-5813 aut Gayo, José Emilio Labra verfasserin (orcid)0000-0001-8907-5348 aut Rivera-Polo, Felipe verfasserin aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 5 vom: 31. Mai (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:5 day:31 month:05 https://dx.doi.org/10.1007/s42979-024-02933-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 5 31 05
allfieldsSound	10.1007/s42979-024-02933-y doi (DE-627)SPR056083580 (SPR)s42979-024-02933-y-e DE-627 ger DE-627 rakwb eng Cifuentes-Silva, Francisco verfasserin (orcid)0000-0001-6570-5421 aut Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner. Semantic web (dpeaa)DE-He213 Data analysis (dpeaa)DE-He213 Linked open data (dpeaa)DE-He213 Legal informatics (dpeaa)DE-He213 Polarization (dpeaa)DE-He213 Political alignment (dpeaa)DE-He213 Astudillo, Hernán verfasserin (orcid)0000-0002-6487-5813 aut Gayo, José Emilio Labra verfasserin (orcid)0000-0001-8907-5348 aut Rivera-Polo, Felipe verfasserin aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 5 vom: 31. Mai (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:5 day:31 month:05 https://dx.doi.org/10.1007/s42979-024-02933-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 5 31 05
language	English
source	Enthalten in SN Computer Science 5(2024), 5 vom: 31. Mai volume:5 year:2024 number:5 day:31 month:05
sourceStr	Enthalten in SN Computer Science 5(2024), 5 vom: 31. Mai volume:5 year:2024 number:5 day:31 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Semantic web Data analysis Linked open data Legal informatics Polarization Political alignment
isfreeaccess_bool	true
container_title	SN Computer Science
authorswithroles_txt_mv	Cifuentes-Silva, Francisco @@aut@@ Astudillo, Hernán @@aut@@ Gayo, José Emilio Labra @@aut@@ Rivera-Polo, Felipe @@aut@@
publishDateDaySort_date	2024-05-31T00:00:00Z
hierarchy_top_id	1668832976
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author	Cifuentes-Silva, Francisco
spellingShingle	Cifuentes-Silva, Francisco misc Semantic web misc Data analysis misc Linked open data misc Legal informatics misc Polarization misc Political alignment Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies
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topic_title	Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies Semantic web (dpeaa)DE-He213 Data analysis (dpeaa)DE-He213 Linked open data (dpeaa)DE-He213 Legal informatics (dpeaa)DE-He213 Polarization (dpeaa)DE-He213 Political alignment (dpeaa)DE-He213
topic	misc Semantic web misc Data analysis misc Linked open data misc Legal informatics misc Polarization misc Political alignment
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title	Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies
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title_full	Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies
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author_browse	Cifuentes-Silva, Francisco Astudillo, Hernán Gayo, José Emilio Labra Rivera-Polo, Felipe
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title_sort	toward efficient legislative processes: analysis of chilean congressional bill votes using semantic web technologies
title_auth	Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies
abstract	Abstract Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner. © The Author(s) 2024
abstractGer	Abstract Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner. © The Author(s) 2024
abstract_unstemmed	Abstract Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner. © The Author(s) 2024
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title_short	Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies
url	https://dx.doi.org/10.1007/s42979-024-02933-y
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author2	Astudillo, Hernán Gayo, José Emilio Labra Rivera-Polo, Felipe
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up_date	2024-07-03T20:05:40.407Z
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Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. 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Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies

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