Construction of Semantic Data Models

Perez-Arriaga, Martha O.; Estrada, Trilce; Abad-Mota, Soraya

doi:10.1007/978-3-319-94809-6_3

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Schemas can be userdefined [18] or heuristically obtained [17] and then incrementally merged into a larger ontology [18,73]. Alternatively, an already existing external ontology [9] or one text-mined from the surrounding text [50,79] can generate a feasible table schema mapping. Machine-learning models can then recognize tables with similar schemas but different formatting with 80-85 % accuracy [41].…”

Section: Document Information Extractionmentioning

confidence: 99%

[Tool] Automatically Extracting Hardware Descriptions from PDF Technical Documentation

Hauser,

Pennekamp

2023

Journal of Systems Research

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The paper presents a modular processor designed to extract detailed data sets from technical documentation in PDF format, specifically targeting microcontroller hardware descriptions. The extracted data can be used for code generation in the embedded software domain. The table processing and text mining paradigms are applied to extract and convert data from the technical documentation in a deterministic process that yields reproducible results. The processor functionality is verified by comparing the data extracted from the technical documentation against the existing machine-readable sources. Most reviewers find the work very interesting and beneficial for the community.

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Section: Document Information Extractionmentioning

confidence: 99%

[Tool] Automatically Extracting Hardware Descriptions from PDF Technical Documentation

Hauser,

Pennekamp

2023

Journal of Systems Research

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“…Semantic publishing applies semantic technology to scientific publishing, and comes in many forms and does not always align with what we have introduced above as genuine semantic publishing ( Kuhn & Dumontier, 2017 ). Under this umbrella of semantic publishing, there are approaches that generate semantically-enriched data models from digital publications for the integration, sharing, management and data comparison between publications ( Perez-Arriaga, 2018 ), study the semantic annotation and enhancement of scholarly articles ( Shotton, 2009 ), provide dynamic visualizations in semantically enhanced papers ( Senderov & Penev, 2016 ), assess the versioning aspect of semantic publishing ( Papakonstantinou, Fundulaki & Flouris, 2018 ), create a global-scale platform with a dataset metadata for automated ingestion, discover, and linkage ( Jacob, Griffith & Le, 2017 ), and propose semantic and web-friendly HTML-based alternatives to the currently PDF-focussed scientific writing process ( Peroni et al, 2016 ). Semantic enhancements of scientific articles can be used for semantic interlinking, interactive figures, re-orderable references and even summary creation ( Shotton et al, 2009 ), and workflows to convert regular scientific articles into linked open data have also been investigated ( Sateli & Witte, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Nanopublication-based semantic publishing and reviewing: a field study with formalization papers

Bucur

Kuhn

Ceolin

et al. 2023

PeerJ Computer Science

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With the rapidly increasing amount of scientific literature, it is getting continuously more difficult for researchers in different disciplines to keep up-to-date with the recent findings in their field of study. Processing scientific articles in an automated fashion has been proposed as a solution to this problem, but the accuracy of such processing remains very poor for extraction tasks beyond the most basic ones (like locating and identifying entities and simple classification based on predefined categories). Few approaches have tried to change how we publish scientific results in the first place, such as by making articles machine-interpretable by expressing them with formal semantics from the start. In the work presented here, we propose a first step in this direction by setting out to demonstrate that we can formally publish high-level scientific claims in formal logic, and publish the results in a special issue of an existing journal. We use the concept and technology of nanopublications for this endeavor, and represent not just the submissions and final papers in this RDF-based format, but also the whole process in between, including reviews, responses, and decisions. We do this by performing a field study with what we call formalization papers, which contribute a novel formalization of a previously published claim. We received 15 submissions from 18 authors, who then went through the whole publication process leading to the publication of their contributions in the special issue. Our evaluation shows the technical and practical feasibility of our approach. The participating authors mostly showed high levels of interest and confidence, and mostly experienced the process as not very difficult, despite the technical nature of the current user interfaces. We believe that these results indicate that it is possible to publish scientific results from different fields with machine-interpretable semantics from the start, which in turn opens countless possibilities to radically improve in the future the effectiveness and efficiency of the scientific endeavor as a whole.

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Clinical Trials Data Management in the Big Data Era

Perez-Arriaga¹,

Poddar²

2020

Lecture Notes in Computer Science

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Construction of Semantic Data Models

Cited by 5 publications

References 24 publications

[Tool] Automatically Extracting Hardware Descriptions from PDF Technical Documentation

[Tool] Automatically Extracting Hardware Descriptions from PDF Technical Documentation

Nanopublication-based semantic publishing and reviewing: a field study with formalization papers

Clinical Trials Data Management in the Big Data Era

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