Text and Data Mining Techniques in Judgment Open Data Analysis for Administrative Practice Control

Metsker, Oleg G.; Trofimov, Egor Viktorovich; Sikorsky, Sergey; Kovalchuk, Sergey V.

doi:10.1007/978-3-030-13283-5_13

Cited by 19 publications

(4 citation statements)

References 11 publications

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“…Ferencek: Impact Assesment of Open Government Data 781 2021). Classification and clustering algorithms, regression models and feature selection were used to predict taxpayer groups (Cha, 2020), to formulate an environmental management strategy (Kang et al, 2021), to classify government expenditure records (De Oliviera, 2021) or to make analysis of open data judgments (Metsker, 2019). For this research, knowledge extraction would be made by using Natural Language Processing (NLP).…”

Section: Preliminary/expected Results and Future Developmentmentioning

confidence: 99%

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Abbas¹,

Agahari²,

Ven³

et al. 2021

34th Bled eConference Digital Support From Crisis to Progressive Change: Conference Proceedings

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Data marketplaces are expected to play a crucial role in tomorrow’s data economy but hardly achieve commercial exploitation. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially neglected research topics that may contribute to advancing data marketplaces towards commercialization. This study provides an overview of the state of the art of data marketplace research. We employ a Systematic Literature Review (SLR) approach and structure our analysis using the Service-TechnologyOrganization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.

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Section: Preliminary/expected Results and Future Developmentmentioning

confidence: 99%

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Abbas¹,

Agahari²,

Ven³

et al. 2021

34th Bled eConference Digital Support From Crisis to Progressive Change: Conference Proceedings

View full text Add to dashboard Cite

show abstract

Section: Preliminary/expected Results and Future Developmentmentioning

confidence: 99%

Solopreneur Digital Ecosystems: Genesis, Lineage and Preliminary Categorization

Piccoli¹,

Palese²,

Rodríguez³

2021

34th Bled eConference Digital Support From Crisis to Progressive Change: Conference Proceedings

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This paper traces the genesis and lineage of solopreneur digital ecosystems. These ecosystems, fostered by a digital environment that is infrastructural, combinatorial and servitized, are enabling the rise to prominence of the solopreneur. We theorize solopreneur digital ecosystems as the latest incarnation of systems beyond firm control, with digital platforms and digital marketplaces as their principal enablers. In an effort to compare them from the perspective of the solopreneur, we categorize solopreneur digital ecosystems on three dimensions: algorithmic control, commoditization, and lock-in. Our work contributes a framework that solopreneurs can use to identify ecosystems in which they can optimally invest their talents and scarce resources. We discuss the findings of this mapping and draw implications for research and practice.

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“…Apart from these, there exist some linguistic-based works on judicial documents, as in Halim, Ali, and Khan (2020), Rehan (2020) Metsker, Trofimov, Sikorsky, andKovalchuk (2018), Tang and Kageura (2019) and Yuan, Lan, Hao, and Zhao (2019). These practical applications ensured strong motivation for research in the judicial text.…”

Section: Why Judicial Domain?mentioning

confidence: 99%

Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

Thomas

Sangeetha

2021

Expert Systems

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Tremendous growth in the availability of judicial documents has demanded the rise of information extraction (IE) techniques that support the automatic extraction of relevant concepts or data from judicial texts. Among various approaches available for IE, ontology‐based IE has proven to be the most appropriate for extracting domain‐specific information from natural language text. Through this article, we propose a knowledge‐driven, semi‐supervised pattern‐based learning (bootstrapping) approach to extract domain‐specific facts from judicial text, starting with a small set of seed facts. In the semantic analysis of legal text, fact extraction is the next step to entity identification, which involves the identification of roles played by each entity in the judicial text. The proposed methodology learns extraction patterns for 12 classes of facts from the judicial text through the integration of the domain ontology called judicial case ontology (JCO). The experimental results were evaluated by human judges and found to be quite promising. One main feature of the proposed methodology is its portability across various domains (such as medical, banking, insurance, etc.) which in turn helps build expert systems in various sectors.

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Text and Data Mining Techniques in Judgment Open Data Analysis for Administrative Practice Control

Cited by 19 publications

References 11 publications

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Solopreneur Digital Ecosystems: Genesis, Lineage and Preliminary Categorization

Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

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