Webinar: SPaR.txt – a cheap Shallow Parsing approach for Regulatory texts

I’ll be presenting my slides for the NLLP (legal NLP) workshop during EMNLP 2021. We’ve created a text processing tool that can be used as a building block for ACC (Automated Compliance Checking) using semantic parsing.  This work has been done in collaboration with Ioannis Konstas, Alasdair Gray, Farhad Sadeghineko, Richard Watson and Bimal Kumar, and is part of the Intelligent Regulatory Compliance (i-ReC) project, a collaboration between Northumbria University and HWU. You can find our data and code at: https://github.com/rubenkruiper/SPaR.txt

Title: SPaR.txt – a cheap Shallow Parsing approach for Regulatory texts

Summary:
Understanding written instructions is a notoriously hard task for computers. One can imagine that the task becomes harder when more and more instructions are given at once, especially when these instructions can be ambiguous or even conflicting. These reasons, amongst others, are why it is so hard to achieve (semi-)automated regulatory compliance checking — something that has been researched in the fields of Architecture, Engineering, and Construction (AEC) since the early 80s. 

One necessary part of the puzzle is that a computer must recognise entities in a text, e.g., that a `party wall’ is not someone dressed up for halloween but a wall shared by multiple buildings. We’d like a computer to understand such domain-specific terms. But we don’t want to spend a lot of time defining exactly which words and word combinations belong to our domain lexicon. Therefore, we developed a tool that can help us identify groups of words (in the context of a sentence) that are likely a term of interest.

Example of the annotation scheme, see Figure 2 in our preprint.

We show that it is easy to train our tool — a simple annotation task, but also few training examples needed to achieve reasonable results. Even using just 200 annotated sentences the tool achieves 70,3% exact matches and 24,2% partial matches for entities. The output of this tool (currently focused on the AEC domain) can be used to improve Information Retrieval results and help assemble a lexicon of terminology in support of semantic parsing.

Seminar: Data Quality Issues in Current Nanopublications

Speaker: Imran Asif
Date: Wednesday 18 September 2019
Time: 11:15 – 12:15
Venue: CM T.01 EM1.58

Imran will give a practice version of his workshop paper that will be given at Research Objects 2019 (RO2019).

Abstract: Nanopublications are a granular way of publishing scientific claims together with their associated provenance and publication information. More than 10 million nanopublications have been published by a handful of researchers covering a wide range of topics within the life sciences. We were motivated to replicate an existing analysis of these nanopublications, but then went deeper into the structure of the existing nanopublications. In this paper, we analyse the usage of nanopublications by investigating the distribution of triples in each part and discuss the data quality issues raised by this analysis. From this analysis we argue that there is a need for the community to develop a set of community guidelines for the modelling of nanopublications.

ISWC 2018

ISWC 2018 Trip Report Keynotes There were three amazing and inspiring keynote talks, all very different from each other. The first was given by Jennifer Golbeck (University of Maryland). While Jennifer did her PhD on the Semantic Web in the early days of social media and Linked Data, she now focuses on user privacy and […]

ISWC 2018 Trip Report

Keynotes

There were three amazing and inspiring keynote talks, all very different from each other.

The first was given by Jennifer Golbeck (University of Maryland). While Jennifer did her PhD on the Semantic Web in the early days of social media and Linked Data, she now focuses on user privacy and consent. These are highly relevant topics to the Semantic Web community and something that we should really be considering when linking people’s personal data. While the consequences of linking scientific data might not be as scary, there are still ethical issues to consider if we do not get it right. Check out her TED talk for an abridged version of her keynote.

She also suggested that when reading a companies privacy policy, you should replace the work “privacy” with “consent” and see how it seems then.

The talk also struck an accord with the launch of the SOLID framework by Tim Berners-Lee. There was a good sales pitch of the SOLID framework from Ruben Verborgh in the afternoon of the Decentralising the Semantic Web Workshop.

The second was given by Natasha Noy (Google). Natasha talked about the challenges of being a researcher and engineering tools that support the community. Particularly where impact may only be detect 6 to 10 years down the line. She also highlighted that Linked Data is only a small fraction of the data in the world (the tip of the iceberg), and it is not appropriate to expect all data to become Linked Data.

Her most recent endeavour has been the Google Dataset Search Tool. This has been a major engineering and social endeavour; getting schema.org markup embedded on pages and building a specialist search tool on top of the indexed data. More details of the search framework are in this blog post. The current search interface is limited due to the availability of metadata; most sites only make title and description available. However, we can now start investigating how to return search results for datasets and what additional data might be of use. This for me is a really exciting area of work.

Later in the day I attended a talk on the LOD Atlas, another dataset search tool. While this gives a very detailed user interface, it is only designed for Linked Data researchers, not general users looking for a dataset.

The third keynote was given by Vanessa Evers (University of Twente, The Netherlands). This was in a completely different domain, social interactions with robots, but still raised plenty of questions for the community. For me the challenge was how to supply contextualised data.

Knowledge Graph Panel

The other big plenary event this year was the knowledge graph panel. The panel consisted of representatives from Microsoft, Facebook, eBay, Google, and IBM, all of whom were involved with the development of Knowledge Graphs within their organisation. A major concern for the Semantic Web community is that most of these panelists were not aware of our community or the results of our work. Another concern is that none of their systems use any of our results, although it sounds like several of them use something similar to RDF.

The main messages I took from the panel were

  • Scale and distribution were key

  • Source information is going to be noisy and challenging to extract value from

  • Metonymy is a major challenge

This final point connects with my work on contextualising data for the task of the user [1, 2] and has reinvigorated my interest in this research topic.

Final Thoughts

This was another great ISWC conference, although many familiar faces were missing.

There was a great and vibrant workshop programme. My paper [3] was presented during the Enabling Open Semantic Science workshop (SemSci 2018) and resulted in a good deal of discussion. There were also great keynotes at the workshop from Paul Groth (slides) and Yolanda Gil which I would recommend anyone to look over.

I regret not having gone to more of the Industry Track sessions. The one I did make was very inspiring to see how the results of the community are being used in practice, and to get insights into the challenges faced.

The conference banquet involved a walking dinner around the Monterey Bay Aquarium. This was a great idea as it allowed plenty of opportunities for conversations with a wide range of conference participants; far more than your standard banquet.

Here are some other takes on the conference:

I also managed to sneak off to look for the sea otters.

[1] [doi] Colin R. Batchelor, Christian Y. A. Brenninkmeijer, Christine Chichester, Mark Davies, Daniela Digles, Ian Dunlop, Chris T. A. Evelo, Anna Gaulton, Carole A. Goble, Alasdair J. G. Gray, Paul T. Groth, Lee Harland, Karen Karapetyan, Antonis Loizou, John P. Overington, Steve Pettifer, Jon Steele, Robert Stevens, Valery Tkachenko, Andra Waagmeester, Antony J. Williams, and Egon L. Willighagen. Scientific Lenses to Support Multiple Views over Linked Chemistry Data. In The Semantic Web – ISWC 2014 – 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I, page 98–113, 2014.
[Bibtex]
@inproceedings{BatchelorBCDDDEGGGGHKLOPSSTWWW14,
abstract = {When are two entries about a small molecule in different datasets the same? If they have the same drug name, chemical structure, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied.
In this paper, we present an approach to enable applications to choose the equivalence criteria to apply between datasets. Thus, supporting multiple dynamic views over the Linked Data. For chemical data, we show that multiple sets of links can be automatically generated according to different equivalence criteria and published with semantic descriptions capturing their context and interpretation. This approach has been applied within a large scale public-private data integration platform for drug discovery. To cater for different use cases, the platform allows the application of different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.},
author = {Colin R. Batchelor and
Christian Y. A. Brenninkmeijer and
Christine Chichester and
Mark Davies and
Daniela Digles and
Ian Dunlop and
Chris T. A. Evelo and
Anna Gaulton and
Carole A. Goble and
Alasdair J. G. Gray and
Paul T. Groth and
Lee Harland and
Karen Karapetyan and
Antonis Loizou and
John P. Overington and
Steve Pettifer and
Jon Steele and
Robert Stevens and
Valery Tkachenko and
Andra Waagmeester and
Antony J. Williams and
Egon L. Willighagen},
title = {Scientific Lenses to Support Multiple Views over Linked Chemistry
Data},
booktitle = {The Semantic Web - {ISWC} 2014 - 13th International Semantic Web Conference,
Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part {I}},
pages = {98--113},
year = {2014},
url = {http://dx.doi.org/10.1007/978-3-319-11964-9_7},
doi = {10.1007/978-3-319-11964-9_7},
}
[2] [doi] Alasdair J. G. Gray. Dataset Descriptions for Linked Data Systems. IEEE Internet Computing, 18(4):66–69, 2014.
[Bibtex]
@article{Gray14,
abstract = {Linked data systems rely on the quality of, and linking between, their data sources. However, existing data is difficult to trace to its origin and provides no provenance for links. This article discusses the need for self-describing linked data.},
author = {Alasdair J. G. Gray},
title = {Dataset Descriptions for Linked Data Systems},
journal = {{IEEE} Internet Computing},
volume = {18},
number = {4},
pages = {66--69},
year = {2014},
url = {http://dx.doi.org/10.1109/MIC.2014.66},
doi = {10.1109/MIC.2014.66},
}
[3] Alasdair J. G. Grayg. Using a Jupyter Notebook to perform a reproducible scientific analysis over semantic web sources. In Enabling Open Semantic Science, Monterey, California, USA, 2018. Executable version: https://mybinder.org/v2/gh/AlasdairGray/SemSci2018/master?filepath=SemSci2018%20Publication.ipynb
[Bibtex]
@InProceedings{Gray2018:jupyter:SemSci2018,
abstract = {In recent years there has been a reproducibility crisis in science. Computational notebooks, such as Jupyter, have been touted as one solution to this problem. However, when executing analyses over live SPARQL endpoints, we get different answers depending upon when the analysis in the notebook was executed. In this paper, we identify some of the issues discovered in trying to develop a reproducible analysis over a collection of biomedical data sources and suggest some best practice to overcome these issues.},
author = {Alasdair J G Grayg},
title = {Using a Jupyter Notebook to perform a reproducible scientific analysis over semantic web sources},
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note = {Executable version: https://mybinder.org/v2/gh/AlasdairGray/SemSci2018/master?filepath=SemSci2018%20Publication.ipynb},
url = {http://ceur-ws.org/Vol-2184/paper-02/paper-02.html},
OPTannote = {}
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First steps with Jupyter Notebooks

At the 2nd Workshop on Enabling Open Semantic Sciences (SemSci2018), colocated at ISWC2018, I presented the following paper (slides at end of this post): Title: Using a Jupyter Notebook to perform a reproducible scientific analysis over semantic web sources Abstract: In recent years there has been a reproducibility crisis in science. Computational notebooks, such as […]

At the 2nd Workshop on Enabling Open Semantic Sciences (SemSci2018), colocated at ISWC2018, I presented the following paper (slides at end of this post):

Title: Using a Jupyter Notebook to perform a reproducible scientific analysis over semantic web sources

Abstract: In recent years there has been a reproducibility crisis in science. Computational notebooks, such as Jupyter, have been touted as one solution to this problem. However, when executing analyses over live SPARQL endpoints, we get different answers depending upon when the analysis in the notebook was executed. In this paper, we identify some of the issues discovered in trying to develop a reproducible analysis over a collection of biomedical data sources and suggest some best practice to overcome these issues.

The paper covers my first attempt at using a computational notebook to publish a data analysis for reproducibility. The paper provokes more questions than it answers and this was the case in the workshop too.

One of the really great things about the paper is that you can launch the notebook, without installing any software, by clicking on the binder button below. You can then rerun the entire notebook and see whether you get the same results that I did when I ran the analysis over the various datasets.

NAR Database Paper

The new year started with a new publication, an article in the 2018 NAR Database issue about the IUPHAR Guide to Pharmacology Database. Simon D. Harding, Joanna L. Sharman, Elena Faccenda, Chris Southan, Adam J. Pawson, Sam Ireland, Alasdair J. G. Gray, Liam Bruce, Stephen P. H. Alexander, Stephen Anderton, Clare Bryant, Anthony P. Davenport, […]

The new year started with a new publication, an article in the 2018 NAR Database issue about the IUPHAR Guide to Pharmacology Database.

  • Simon D. Harding, Joanna L. Sharman, Elena Faccenda, Chris Southan, Adam J. Pawson, Sam Ireland, Alasdair J. G. Gray, Liam Bruce, Stephen P. H. Alexander, Stephen Anderton, Clare Bryant, Anthony P. Davenport, Christian Doerig, Doriano Fabbro, Francesca Levi-Schaffer, Michael Spedding, Jamie A. Davies, and {NC-IUPHAR}. The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY. Nucleic Acids Research, 46(D1):D1091-D1106, 2018. doi:10.1093/nar/gkx1121
    [BibTeX] [Abstract] [Download PDF]

    The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, www.guidetopharmacology.org) and its precursor IUPHAR-DB, have captured expert-curated interactions between targets and ligands from selected papers in pharmacology and drug discovery since 2003. This resource continues to be developed in conjunction with the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS). As previously described, our unique model of content selection and quality control is based on 96 target-class subcommittees comprising 512 scientists collaborating with in-house curators. This update describes content expansion, new features and interoperability improvements introduced in the 10 releases since August 2015. Our relationship matrix now describes ∼9000 ligands, ∼15 000 binding constants, ∼6000 papers and ∼1700 human proteins. As an important addition, we also introduce our newly funded project for the Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb, www.guidetoimmunopharmacology.org). This has been ‘forked’ from the well-established GtoPdb data model and expanded into new types of data related to the immune system and inflammatory processes. This includes new ligands, targets, pathways, cell types and diseases for which we are recruiting new IUPHAR expert committees. Designed as an immunopharmacological gateway, it also has an emphasis on potential therapeutic interventions.

    @Article{Harding2018-GTP,
    abstract = {The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, www.guidetopharmacology.org) and its precursor IUPHAR-DB, have captured expert-curated interactions between targets and ligands from selected papers in pharmacology and drug discovery since 2003. This resource continues to be developed in conjunction with the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS). As previously described, our unique model of content selection and quality control is based on 96 target-class subcommittees comprising 512 scientists collaborating with in-house curators. This update describes content expansion, new features and interoperability improvements introduced in the 10 releases since August 2015. Our relationship matrix now describes ∼9000 ligands, ∼15 000 binding constants, ∼6000 papers and ∼1700 human proteins. As an important addition, we also introduce our newly funded project for the Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb, www.guidetoimmunopharmacology.org). This has been ‘forked’ from the well-established GtoPdb data model and expanded into new types of data related to the immune system and inflammatory processes. This includes new ligands, targets, pathways, cell types and diseases for which we are recruiting new IUPHAR expert committees. Designed as an immunopharmacological gateway, it also has an emphasis on potential therapeutic interventions.},
    author = {Harding, Simon D and Sharman, Joanna L and Faccenda, Elena and Southan, Chris and Pawson, Adam J and Ireland, Sam and Gray, Alasdair J G and Bruce, Liam and Alexander, Stephen P H and Anderton, Stephen and Bryant, Clare and Davenport, Anthony P and Doerig, Christian and Fabbro, Doriano and Levi-Schaffer, Francesca and Spedding, Michael and Davies, Jamie A and , {NC-IUPHAR}},
    title = {The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY},
    journal = {Nucleic Acids Research},
    year = {2018},
    volume = {46},
    number = {D1},
    pages = {D1091-D1106},
    month = jan,
    OPTnote = {},
    OPTannote = {},
    url = {http://dx.doi.org/10.1093/nar/gkx1121},
    doi = {10.1093/nar/gkx1121}
    }

My involvement came from Liam Bruce’s honours project. Liam developed the RDB2RDF mappings that convert the existing relational content into an RDF representation. The mappings are executed using the Morph-RDB R2RML engine.

To ensure that we abide by the FAIR data principles, we also generate machine processable metadata descriptions of the data that conform to the HCLS Community Profile.

Below is an altmetric donut so you can see what people are saying about the paper.

ISWC2017 Papers

I have had two papers accepted within the events that make up ISWC2017. My PhD student Qianru Zhou has been working on using RDF stream processing to detect anomalous events through telecommunication network messages. The particular scenario in our paper that will be presented at the Web Stream Processing workshop focuses on detecting a disaster such as […]

I have had two papers accepted within the events that make up ISWC2017.

My PhD student Qianru Zhou has been working on using RDF stream processing to detect anomalous events through telecommunication network messages. The particular scenario in our paper that will be presented at the Web Stream Processing workshop focuses on detecting a disaster such as the capsizing of the Eastern Star on the Yangtze River [1].

The second paper is a poster in the main conference that provides an overview of the Bioschemas project where we are identifying the Schema.org markup that is of primary importance for life science resources. Hopefully the paper title will pull the punters in for the session [2].

[1] Qianru Zhou, Stephen McLaughlin, Alasdair J. G. Gray, Shangbin Wu, and Chengxiang Wang. Lost Silence: An emergency response early detection service through continuous processing of telecommunication data streams. In Web Stream Processing 2017, 2017.
[Bibtex]
@InProceedings{ZhouEtal2017:LostSilence:WSP2017,
abstract = {Early detection of significant traumatic events, e.g. terrorist events, ship capsizes, is important to ensure that a prompt emergency response can occur. In the modern world telecommunication systems can and do play a key role in ensuring a successful emergency response by detecting such incidents through significant changes in calls and access to the networks. In this paper a methodology is illustrated to detect such incidents immediately (with the delay in the order of milliseconds), by processing semantically annotated streams of data in cellular telecommunication systems. In our methodology, live information of phones' positions and status are encoded as RDF streams. We propose an algorithm that processes streams of RDF annotated telecommunication data to detect abnormality. Our approach is exemplified in the context of capsize of a passenger cruise ship but is readily translatable to other incidents. Our evaluation results show that with properly chosen window size, such incidents can be detected effectively.},
author = {Qianru Zhou and Stephen McLaughlin and Alasdair J G Gray and Shangbin Wu and Chengxiang Wang},
title = {Lost Silence: An emergency response early detection service through continuous processing of telecommunication data streams},
OPTcrossref = {},
OPTkey = {},
booktitle = {Web Stream Processing 2017},
year = {2017},
OPTeditor = {},
OPTvolume = {},
OPTnumber = {},
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[2] Alasdair J. G. Gray, Carole Goble, Rafael C. Jimenez, and The Bioschemas Community. Bioschemas: From Potato Salad to Protein Annotation. In ISWC 2017 Poster Proceedings, Vienna, Austria, 2017. Poster
[Bibtex]
@InProceedings{grayetal2017:bioschemas:iswc2017,
abstract = {The life sciences have a wealth of data resources with a wide range of overlapping content. Key repositories, such as UniProt for protein data or Entrez Gene for gene data, are well known and their content easily discovered through search engines. However, there is a long-tail of bespoke datasets with important content that are not so prominent in search results. Building on the success of Schema.org for making a wide range of structured web content more discoverable and interpretable, e.g. food recipes, the Bioschemas community (http://bioschemas.org) aim to make life sciences datasets more findable by encouraging data providers to embed Schema.org markup in their resources.},
author = {Alasdair J G Gray and Carole Goble and Rafael C Jimenez and {The Bioschemas Community}},
title = {Bioschemas: From Potato Salad to Protein Annotation},
OPTcrossref = {},
OPTkey = {},
booktitle = {ISWC 2017 Poster Proceedings},
year = {2017},
OPTeditor = {},
OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTpages = {},
OPTmonth = {},
address = {Vienna, Austria},
OPTorganization = {},
OPTpublisher = {},
note = {Poster},
OPTannote = {}
}

ISWC2017 Papers

I have had two papers accepted within the events that make up ISWC2017. My PhD student Qianru Zhou has been working on using RDF stream processing to detect anomalous events through telecommunication network messages. The particular scenario in our paper that will be presented at the Web Stream Processing workshop focuses on detecting a disaster such as […]

I have had two papers accepted within the events that make up ISWC2017.

My PhD student Qianru Zhou has been working on using RDF stream processing to detect anomalous events through telecommunication network messages. The particular scenario in our paper that will be presented at the Web Stream Processing workshop focuses on detecting a disaster such as the capsizing of the Eastern Star on the Yangtze River [1].

The second paper is a poster in the main conference that provides an overview of the Bioschemas project where we are identifying the Schema.org markup that is of primary importance for life science resources. Hopefully the paper title will pull the punters in for the session [2].

[1] Qianru Zhou, Stephen McLaughlin, Alasdair J. G. Gray, Shangbin Wu, and Chengxiang Wang. Lost Silence: An emergency response early detection service through continuous processing of telecommunication data streams. In Web Stream Processing 2017, 2017.
[Bibtex]
@InProceedings{ZhouEtal2017:LostSilence:WSP2017,
abstract = {Early detection of significant traumatic events, e.g. terrorist events, ship capsizes, is important to ensure that a prompt emergency response can occur. In the modern world telecommunication systems can and do play a key role in ensuring a successful emergency response by detecting such incidents through significant changes in calls and access to the networks. In this paper a methodology is illustrated to detect such incidents immediately (with the delay in the order of milliseconds), by processing semantically annotated streams of data in cellular telecommunication systems. In our methodology, live information of phones' positions and status are encoded as RDF streams. We propose an algorithm that processes streams of RDF annotated telecommunication data to detect abnormality. Our approach is exemplified in the context of capsize of a passenger cruise ship but is readily translatable to other incidents. Our evaluation results show that with properly chosen window size, such incidents can be detected effectively.},
author = {Qianru Zhou and Stephen McLaughlin and Alasdair J G Gray and Shangbin Wu and Chengxiang Wang},
title = {Lost Silence: An emergency response early detection service through continuous processing of telecommunication data streams},
OPTcrossref = {},
OPTkey = {},
booktitle = {Web Stream Processing 2017},
year = {2017},
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OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTpages = {},
OPTmonth = {},
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OPTorganization = {},
OPTpublisher = {},
OPTnote = {},
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}
[2] Alasdair J. G. Gray, Carole Goble, Rafael C. Jimenez, and The Bioschemas Community. Bioschemas: From Potato Salad to Protein Annotation. In ISWC 2017 Poster Proceedings, Vienna, Austria, 2017. Poster
[Bibtex]
@InProceedings{grayetal2017:bioschemas:iswc2017,
abstract = {The life sciences have a wealth of data resources with a wide range of overlapping content. Key repositories, such as UniProt for protein data or Entrez Gene for gene data, are well known and their content easily discovered through search engines. However, there is a long-tail of bespoke datasets with important content that are not so prominent in search results. Building on the success of Schema.org for making a wide range of structured web content more discoverable and interpretable, e.g. food recipes, the Bioschemas community (http://bioschemas.org) aim to make life sciences datasets more findable by encouraging data providers to embed Schema.org markup in their resources.},
author = {Alasdair J G Gray and Carole Goble and Rafael C Jimenez and {The Bioschemas Community}},
title = {Bioschemas: From Potato Salad to Protein Annotation},
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booktitle = {ISWC 2017 Poster Proceedings},
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OPTnumber = {},
OPTseries = {},
OPTpages = {},
OPTmonth = {},
address = {Vienna, Austria},
OPTorganization = {},
OPTpublisher = {},
note = {Poster},
OPTannote = {}
}

Interoperability and FAIRness through a novel combination of Web technologies

New paper [1] on using Semantic Web technologies to publish existing data according to the FAIR data principles [2]. Abstract: Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein […]

New paper [1] on using Semantic Web technologies to publish existing data according to the FAIR data principles [2].

Abstract: Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved at the level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.

[1] [doi] Mark D. Wilkinson, Ruben Verborgh, Luiz Olavo {Bonino da Silva Santos}, Tim Clark, Morris A. Swertz, Fleur D. L. Kelpin, Alasdair J. G. Gray, Erik A. Schultes, Erik M. van Mulligen, Paolo Ciccarese, Arnold Kuzniar, Anand Gavai, Mark Thompson, Rajaram Kaliyaperumal, Jerven T. Bolleman, and Michel Dumontier. Interoperability and FAIRness through a novel combination of Web technologies. PeerJ Computer Science, 3:e110, apr 2017.
[Bibtex]
@article{Wilkinson2017-FAIRness,
abstract = {Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.},
author = {Wilkinson, Mark D. and Verborgh, Ruben and {Bonino da Silva Santos}, Luiz Olavo and Clark, Tim and Swertz, Morris A. and Kelpin, Fleur D.L. and Gray, Alasdair J.G. and Schultes, Erik A. and van Mulligen, Erik M. and Ciccarese, Paolo and Kuzniar, Arnold and Gavai, Anand and Thompson, Mark and Kaliyaperumal, Rajaram and Bolleman, Jerven T. and Dumontier, Michel},
doi = {10.7717/peerj-cs.110},
issn = {2376-5992},
journal = {PeerJ Computer Science},
month = {apr},
pages = {e110},
publisher = {PeerJ Inc.},
title = {{Interoperability and FAIRness through a novel combination of Web technologies}},
url = {https://peerj.com/articles/cs-110},
volume = {3},
year = {2017}
}
[2] [doi] Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino {da Silva Santos}, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra Gonzalez-Beltran, Alasdair J. G. Gray, Paul Groth, Carole Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A. C. {‘t Hoen}, Rob Hooft, Tobias Kuhn, Ruben Kok, Joost Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, Rene van Schaik, Susanna-Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, and Barend Mons. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3:160018, 2016.
[Bibtex]
@article{Wilkinson2016,
abstract = {There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the {FAIR} Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the {FAIR} Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the {FAIR} Principles, and includes the rationale behind them, and some exemplar implementations in the community.},
author = {Wilkinson, Mark D and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and {da Silva Santos}, Luiz Bonino and Bourne, Philip E and Bouwman, Jildau and Brookes, Anthony J and Clark, Tim and Crosas, Merc{\`{e}} and Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo, Chris T and Finkers, Richard and Gonzalez-Beltran, Alejandra and Gray, Alasdair J.G. and Groth, Paul and Goble, Carole and Grethe, Jeffrey S and Heringa, Jaap and {'t Hoen}, Peter A.C and Hooft, Rob and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott J and Martone, Maryann E and Mons, Albert and Packer, Abel L and Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and van Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz, Morris A and Thompson, Mark and van der Lei, Johan and van Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and Mons, Barend},
doi = {10.1038/sdata.2016.18},
issn = {2052-4463},
journal = {Scientific Data},
month = mar,
pages = {160018},
publisher = {Macmillan Publishers Limited},
title = {{The FAIR Guiding Principles for scientific data management and stewardship}},
url = {http://www.nature.com/articles/sdata201618},
volume = {3},
year = {2016}
}

New Paper: Reproducibility with Administrative Data

Our journal article [1] looks at encouraging good practice to enable reproducible analysis of data analysis workflows. This is a result of a collaboration between social scientists and a computer scientist with the ADRC-Scotland. Abstract: Powerful new social science data resources are emerging. One particularly important source is administrative data, which were originally collected for organisational […]

Our journal article [1] looks at encouraging good practice to enable reproducible analysis of data analysis workflows. This is a result of a collaboration between social scientists and a computer scientist with the ADRC-Scotland.

Abstract: Powerful new social science data resources are emerging. One particularly important source is administrative data, which were originally collected for organisational purposes but often contain information that is suitable for social science research. In this paper we outline the concept of reproducible research in relation to micro-level administrative social science data. Our central claim is that a planned and organised workflow is essential for high quality research using micro-level administrative social science data. We argue that it is essential for researchers to share research code, because code sharing enables the elements of reproducible research. First, it enables results to be duplicated and therefore allows the accuracy and validity of analyses to be evaluated. Second, it facilitates further tests of the robustness of the original piece of research. Drawing on insights from computer science and other disciplines that have been engaged in e-Research we discuss and advocate the use of Git repositories to provide a useable and effective solution to research code sharing and rendering social science research using micro-level administrative data reproducible.

[1] [doi] C. J. Playford, V. Gayle, R. Connelly, and A. J. Gray, “Administrative social science data: The challenge of reproducible research,” Big Data & Society, vol. 3, iss. 2, 2016.
[Bibtex]
@Article{Playford2016BDS,
abstract = {Powerful new social science data resources are emerging. One particularly important source is administrative data, which were originally collected for organisational purposes but often contain information that is suitable for social science research. In this paper we outline the concept of reproducible research in relation to micro-level administrative social science data. Our central claim is that a planned and organised workflow is essential for high quality research using micro-level administrative social science data. We argue that it is essential for researchers to share research code, because code sharing enables the elements of reproducible research. First, it enables results to be duplicated and therefore allows the accuracy and validity of analyses to be evaluated. Second, it facilitates further tests of the robustness of the original piece of research. Drawing on insights from computer science and other disciplines that have been engaged in e-Research we discuss and advocate the use of Git repositories to provide a useable and effective solution to research code sharing and rendering social science research using micro-level administrative data reproducible.},
author = {Christopher J Playford and Vernon Gayle and Roxanne Connelly and Alasdair JG Gray},
title = {Administrative social science data: The challenge of reproducible research},
journal = {Big Data \& Society},
year = {2016},
OPTkey = {},
volume = {3},
number = {2},
OPTpages = {},
month = dec,
url = {http://journals.sagepub.com/doi/full/10.1177/2053951716684143},
doi = {10.1177/2053951716684143},
OPTnote = {},
OPTannote = {}
}