Abstract—Wikidata is a world readable and writable knowledge base maintained by the Wikimedia Foundation. It offers the opportunity to collaboratively construct a fully open access knowledge graph spanning biology, medicine, and all other domains of knowledge. To meet this potential, social and technical challenges must be overcome - many of which are familiar to the biocuration community. These include community ontology building, high precision information extraction, provenance, and license management. By working together with Wikidata now, we can help shape it into a trustworthy, unencumbered central node in the Semantic Web of biomedical data.
Building a Biomedical Knowledge Garden Benjamin Good
Describes the tribulations of building a large biomedical knowledge graph. Provides a comparison between the UMLS and Wikidata in terms of content and structure. Concludes with the idea of anchoring the knowledge graph in Wikidata items and properties.
Wikidata: Verifiable, Linked Open Knowledge That Anyone Can EditDario Taraborelli
Slides for my September 23 talk on Wikidata and WikiCite – NIH Frontiers in Data Science lecture series.
Persistent URL: https://dx.doi.org/10.6084/m9.figshare.3850821
Building a Biomedical Knowledge Garden Benjamin Good
Describes the tribulations of building a large biomedical knowledge graph. Provides a comparison between the UMLS and Wikidata in terms of content and structure. Concludes with the idea of anchoring the knowledge graph in Wikidata items and properties.
Wikidata: Verifiable, Linked Open Knowledge That Anyone Can EditDario Taraborelli
Slides for my September 23 talk on Wikidata and WikiCite – NIH Frontiers in Data Science lecture series.
Persistent URL: https://dx.doi.org/10.6084/m9.figshare.3850821
Making it Easier, Possibly Even Pleasant, to Author Rich Experimental MetadataMichel Dumontier
Biomedical researchers will remain stymied in their ability to take full advantage of the Big Data revolution if they can never find the datasets that they need to analyze, if there is lack of clarity about what particular datasets contain, and if data are insufficiently described.
CEDAR, an NIH BD2K Center of Excellence, aims to develop methods and tools to vastly ease the burden of authoring good experimental metadata, and to maximally use this information to zero in on datasets of interest.
Semantic web technologies offer a potential mechanism for the representation and integration of thousands of biomedical databases. Many of these databases offer cross-references to other data sources, but these are generally incomplete and prone to error. In this paper, we conduct an empirical analysis of the link structure of life science Linked Data, obtained from the Bio2RDF project. Three different link graphs for datasets, entities and terms are characterized by degree, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we utilize the symmetry and transitivity of entity links to build a benchmark and evaluate several popular entity matching approaches. Our findings indicate that the life science data network can help find hidden links, can be used to validate links, and may offer a mechanism to integrate a wider set of resources to support biomedical knowledge discovery.
With its focus on investigating the basis for the sustained existence
of living systems, modern biology has always been a fertile, if not
challenging, domain for formal knowledge representation and automated
reasoning. With thousands of databases and hundreds of ontologies now
available, there is a salient opportunity to integrate these for
discovery. In this talk, I will discuss our efforts to build a rich
foundational network of ontology-annotated linked data, develop
methods to intelligently retrieve content of interest, uncover
significant biological associations, and pursue new avenues for drug
discovery. As the portfolio of Semantic Web technologies continue to
mature in terms of functionality, scalability, and an understanding of
how to maximize their value, researchers will be strategically poised
to pursue increasingly sophisticated KR projects aimed at improving
our overall understanding of human health and disease.
bio: Dr. Michel Dumontier is an Associate Professor of Medicine
(Biomedical Informatics) at Stanford University. His research aims to
find new treatments for rare and complex diseases. His research
interest lie in the publication, integration, and discovery of
scientific knowledge. Dr. Dumontier serves as a co-chair for the World
Wide Web Consortium Semantic Web in Health Care and Life Sciences
Interest Group (W3C HCLSIG) and is the Scientific Director for
Bio2RDF, a widely used open-source project to create and provide
linked data for life sciences.
Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. This document describes a consensus among participating stakeholders in the Health Care and the Life Sciences domain on the description of datasets using the Resource Description Framework (RDF). This specification meets key functional requirements, reuses existing vocabularies to the extent that it is possible, and addresses elements of data description, versioning, provenance, discovery, exchange, query, and retrieval.
Bio2RDF is an open-source project that offers a large and
connected knowledge graph of Life Science Linked Data. Each dataset is expressed using its own vocabulary, thereby hindering integration, search, query, and browse data across similar or identical types of data. With growth and content changes in source data, a manual approach to maintain mappings has proven untenable. The aim of this work is to develop a (semi)automated procedure to generate high quality mappings
between Bio2RDF and SIO using BioPortal ontologies. Our preliminary results demonstrate that our approach is promising in that it can find new mappings using a transitive closure between ontology mappings. Further development of the methodology coupled with improvements in
the ontology will offer a better-integrated view of the Life Science Linked Data
Model organisms such as budding yeast provide a common platform to interrogate and understand cellular and physiological processes. Knowledge about model organisms, whether generated during the course of scientific investigation, or extracted from published articles, are made available by model organism databases (MODs) such as the Saccharomyces Genome Database (SGD) for powerful, data-driven bioinformatic analyses. Integrative platforms such as InterMine offer a standard platform for MOD data exploration and data mining. Yet, today’s bioinformatic analyses also requires access to a significantly broader set of structured biomedical data, such as what can be found in the emerging network of Linked Open Data (LOD). If MOD data could be provisioned as FAIR (Findable, Accessible, Interoperable, and Reusable), then scientists could leverage a greater amount of interoperable data in knowledge discovery.
The goal of this proposal is to increase the utility of MOD data by implementing standards-compliant data access interfaces that interoperate with Linked Data. We will focus our efforts on developing interfaces for data access, data retrieval, and query answering for SGD. Our software will publish InterMine data as LOD that are semantically annotated with ontologies and be retrieved using standardized formats (e.g. JSON-LD, Turtle). We will facilitate the exploration of MOD data for hypothesis testing, by implementing efficient query answering using Linked Data Fragments, and by developing a set of graphical user interfaces to search for data of interest, explore connections, and answer questions that leverage the wider LOD network. Finally, we will develop a locally and cloud-deployable image to enable the rapid deployment of the proposed infrastructure. Our efforts to increase interoperability and ease of deployment for biomedical data repositories will increase research productivity and reduce costs associated with data integration and warehouse maintenance.
Our access to scientific information has changed in ways that were hardly imagined even by the early pioneers of the internet. The immense quantities of data and the array of tools available to search and analyze online content continues to expand while the pace of change does not appear to be slowing. ChemSpider is one of the chemistry community’s primary online public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data tens of thousands of chemists every day and it serves as the foundation for many important international projects to integrate chemistry and biology data, facilitate drug discovery efforts and help to identify new chemicals from under the ocean. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of the solutions that it helps to enable. We will also discuss the possibilities it offers in the domain of crowdsourcing and open data sharing. The future of scientific information and communication will be underpinned by these efforts, influenced by increasing participation from the scientific community and facilitated collaboration and ultimately accelerate scientific progress.
How open data contribute to improving the world. The life science use case. The technical, social, ethical issues.
This was a talk given within the iGEM 2020 programme by the London Imperial College students group (https://2020.igem.org/Team:Imperial_College), in a webinar organised by the SOAPLab group on the topic of Ethics of Automation. Excellent Dr Brandon Sepulvado was the other speaker of the day.
Automatic Extraction of Knowledge from the LiteratureTheContentMine
Published on May 11, 2016 by PMR
ContentMine tools (and the Harvest alliance) can be used to search the literature for knowledge, especially in biomedicine. All tools are Open and shortly we shall be indexing the complete daily scholarly literature
SciDataCon 2014 Data Papers and their applications workshop - NPG Scientific ...Susanna-Assunta Sansone
Part of the SciDataCon14 workshop on "Data Papers and their applications" run by myself and Brian Hole to help attendees understand current data-publishing journals and trends and help them understand the editorial processes on NPG's Scientific Data and Ubiquity's Open Health Data.
Can machines understand the scientific literature?petermurrayrust
A presentation to Cambridge MPhil Computational Biology. 2020-11-11 . Presenters Peter Murray-Rust, Shweata Hegde and Ambreen Hamadani from https://github.com/petermr/openvirus .
This chunk is PMR with a large break in the middle for SH and AH talks.
I cover Global Challenges, knowledge equity, semantics of scientific articles, Wikidata, Data Extraction from images, and ethics/politics.
Answer: Yes, technically. No, politically as the Publisher-Academic Complex will block it.
Amanuens.is HUmans and machines annotating scholarly literaturepetermurrayrust
about 10,000 scholarly articles ("papers") are published each day. Amanuens.is is a symbiont of ContentMine and Hypothes.is (both Shuttleworth projects/Fellows) which annotates theses using an array of controlled vocabularies ("dictionaries"). The results, in semantic form are used to annotate the original material. The talk had live demos and used plant chemistry as the examples
Use of ContentMine tools on the Open Access subset of EuropePubMedCentral to discover new knowledge about the Zika virus.
Three slides have embedded movies - these do not show in slideshare and a first pass of this can be seen as a single file at https://vimeo.com/154705161
Automatic Extraction of Knowledge from the Literaturepetermurrayrust
ContentMine tools (and the Harvest alliance) can be used to search the literature for knowledge, especially in biomedicine. All tools are Open and shortly we shall be indexing the complete daily scholarly literature
High throughput mining of the scholarly literature; talk at NIHpetermurrayrust
The scientific and medical literature contains huge amounts of valuable unused information. This talk shows how to discover it, extract, re-use and interpret it. Wikidata is presented as a key new tool and infrastructure. Everyone can become involved. However some of the barriers to use are sociopolitical and these are identified and discussed.
Automatic Extraction of Knowledge from Biomedical literaturepetermurrayrust
a plenary lecture to Cochrane Collaboration in Birmingham, on the value of automatically extracting knowledge. Covers the Why? How? What? Who? and problems and invites collaboration
Making it Easier, Possibly Even Pleasant, to Author Rich Experimental MetadataMichel Dumontier
Biomedical researchers will remain stymied in their ability to take full advantage of the Big Data revolution if they can never find the datasets that they need to analyze, if there is lack of clarity about what particular datasets contain, and if data are insufficiently described.
CEDAR, an NIH BD2K Center of Excellence, aims to develop methods and tools to vastly ease the burden of authoring good experimental metadata, and to maximally use this information to zero in on datasets of interest.
Semantic web technologies offer a potential mechanism for the representation and integration of thousands of biomedical databases. Many of these databases offer cross-references to other data sources, but these are generally incomplete and prone to error. In this paper, we conduct an empirical analysis of the link structure of life science Linked Data, obtained from the Bio2RDF project. Three different link graphs for datasets, entities and terms are characterized by degree, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we utilize the symmetry and transitivity of entity links to build a benchmark and evaluate several popular entity matching approaches. Our findings indicate that the life science data network can help find hidden links, can be used to validate links, and may offer a mechanism to integrate a wider set of resources to support biomedical knowledge discovery.
With its focus on investigating the basis for the sustained existence
of living systems, modern biology has always been a fertile, if not
challenging, domain for formal knowledge representation and automated
reasoning. With thousands of databases and hundreds of ontologies now
available, there is a salient opportunity to integrate these for
discovery. In this talk, I will discuss our efforts to build a rich
foundational network of ontology-annotated linked data, develop
methods to intelligently retrieve content of interest, uncover
significant biological associations, and pursue new avenues for drug
discovery. As the portfolio of Semantic Web technologies continue to
mature in terms of functionality, scalability, and an understanding of
how to maximize their value, researchers will be strategically poised
to pursue increasingly sophisticated KR projects aimed at improving
our overall understanding of human health and disease.
bio: Dr. Michel Dumontier is an Associate Professor of Medicine
(Biomedical Informatics) at Stanford University. His research aims to
find new treatments for rare and complex diseases. His research
interest lie in the publication, integration, and discovery of
scientific knowledge. Dr. Dumontier serves as a co-chair for the World
Wide Web Consortium Semantic Web in Health Care and Life Sciences
Interest Group (W3C HCLSIG) and is the Scientific Director for
Bio2RDF, a widely used open-source project to create and provide
linked data for life sciences.
Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. This document describes a consensus among participating stakeholders in the Health Care and the Life Sciences domain on the description of datasets using the Resource Description Framework (RDF). This specification meets key functional requirements, reuses existing vocabularies to the extent that it is possible, and addresses elements of data description, versioning, provenance, discovery, exchange, query, and retrieval.
Bio2RDF is an open-source project that offers a large and
connected knowledge graph of Life Science Linked Data. Each dataset is expressed using its own vocabulary, thereby hindering integration, search, query, and browse data across similar or identical types of data. With growth and content changes in source data, a manual approach to maintain mappings has proven untenable. The aim of this work is to develop a (semi)automated procedure to generate high quality mappings
between Bio2RDF and SIO using BioPortal ontologies. Our preliminary results demonstrate that our approach is promising in that it can find new mappings using a transitive closure between ontology mappings. Further development of the methodology coupled with improvements in
the ontology will offer a better-integrated view of the Life Science Linked Data
Model organisms such as budding yeast provide a common platform to interrogate and understand cellular and physiological processes. Knowledge about model organisms, whether generated during the course of scientific investigation, or extracted from published articles, are made available by model organism databases (MODs) such as the Saccharomyces Genome Database (SGD) for powerful, data-driven bioinformatic analyses. Integrative platforms such as InterMine offer a standard platform for MOD data exploration and data mining. Yet, today’s bioinformatic analyses also requires access to a significantly broader set of structured biomedical data, such as what can be found in the emerging network of Linked Open Data (LOD). If MOD data could be provisioned as FAIR (Findable, Accessible, Interoperable, and Reusable), then scientists could leverage a greater amount of interoperable data in knowledge discovery.
The goal of this proposal is to increase the utility of MOD data by implementing standards-compliant data access interfaces that interoperate with Linked Data. We will focus our efforts on developing interfaces for data access, data retrieval, and query answering for SGD. Our software will publish InterMine data as LOD that are semantically annotated with ontologies and be retrieved using standardized formats (e.g. JSON-LD, Turtle). We will facilitate the exploration of MOD data for hypothesis testing, by implementing efficient query answering using Linked Data Fragments, and by developing a set of graphical user interfaces to search for data of interest, explore connections, and answer questions that leverage the wider LOD network. Finally, we will develop a locally and cloud-deployable image to enable the rapid deployment of the proposed infrastructure. Our efforts to increase interoperability and ease of deployment for biomedical data repositories will increase research productivity and reduce costs associated with data integration and warehouse maintenance.
Our access to scientific information has changed in ways that were hardly imagined even by the early pioneers of the internet. The immense quantities of data and the array of tools available to search and analyze online content continues to expand while the pace of change does not appear to be slowing. ChemSpider is one of the chemistry community’s primary online public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data tens of thousands of chemists every day and it serves as the foundation for many important international projects to integrate chemistry and biology data, facilitate drug discovery efforts and help to identify new chemicals from under the ocean. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of the solutions that it helps to enable. We will also discuss the possibilities it offers in the domain of crowdsourcing and open data sharing. The future of scientific information and communication will be underpinned by these efforts, influenced by increasing participation from the scientific community and facilitated collaboration and ultimately accelerate scientific progress.
How open data contribute to improving the world. The life science use case. The technical, social, ethical issues.
This was a talk given within the iGEM 2020 programme by the London Imperial College students group (https://2020.igem.org/Team:Imperial_College), in a webinar organised by the SOAPLab group on the topic of Ethics of Automation. Excellent Dr Brandon Sepulvado was the other speaker of the day.
Automatic Extraction of Knowledge from the LiteratureTheContentMine
Published on May 11, 2016 by PMR
ContentMine tools (and the Harvest alliance) can be used to search the literature for knowledge, especially in biomedicine. All tools are Open and shortly we shall be indexing the complete daily scholarly literature
SciDataCon 2014 Data Papers and their applications workshop - NPG Scientific ...Susanna-Assunta Sansone
Part of the SciDataCon14 workshop on "Data Papers and their applications" run by myself and Brian Hole to help attendees understand current data-publishing journals and trends and help them understand the editorial processes on NPG's Scientific Data and Ubiquity's Open Health Data.
Can machines understand the scientific literature?petermurrayrust
A presentation to Cambridge MPhil Computational Biology. 2020-11-11 . Presenters Peter Murray-Rust, Shweata Hegde and Ambreen Hamadani from https://github.com/petermr/openvirus .
This chunk is PMR with a large break in the middle for SH and AH talks.
I cover Global Challenges, knowledge equity, semantics of scientific articles, Wikidata, Data Extraction from images, and ethics/politics.
Answer: Yes, technically. No, politically as the Publisher-Academic Complex will block it.
Amanuens.is HUmans and machines annotating scholarly literaturepetermurrayrust
about 10,000 scholarly articles ("papers") are published each day. Amanuens.is is a symbiont of ContentMine and Hypothes.is (both Shuttleworth projects/Fellows) which annotates theses using an array of controlled vocabularies ("dictionaries"). The results, in semantic form are used to annotate the original material. The talk had live demos and used plant chemistry as the examples
Use of ContentMine tools on the Open Access subset of EuropePubMedCentral to discover new knowledge about the Zika virus.
Three slides have embedded movies - these do not show in slideshare and a first pass of this can be seen as a single file at https://vimeo.com/154705161
Automatic Extraction of Knowledge from the Literaturepetermurrayrust
ContentMine tools (and the Harvest alliance) can be used to search the literature for knowledge, especially in biomedicine. All tools are Open and shortly we shall be indexing the complete daily scholarly literature
High throughput mining of the scholarly literature; talk at NIHpetermurrayrust
The scientific and medical literature contains huge amounts of valuable unused information. This talk shows how to discover it, extract, re-use and interpret it. Wikidata is presented as a key new tool and infrastructure. Everyone can become involved. However some of the barriers to use are sociopolitical and these are identified and discussed.
Automatic Extraction of Knowledge from Biomedical literaturepetermurrayrust
a plenary lecture to Cochrane Collaboration in Birmingham, on the value of automatically extracting knowledge. Covers the Why? How? What? Who? and problems and invites collaboration
This is the slide eck that we used when we raised $1.2 million from investors for the angel round of IMSafer, back in 2006. The original company name was Collabarent.
Update on the gene wiki project, introduction to knowledge.bio semantic search application, introduction to biobranch.org collaborative decision tree creator
This presentation describes two modes of web-based knowledge acquisition in the domain of bioinformatics. "Pull" models such as social tagging systems that engage passive altruism and "push" models such as the Mechanical Turk that actively guide and incentivise the knowledge acquisition process.
Microtask crowdsourcing for disease mention annotation in PubMed abstractsBenjamin Good
Microtask crowdsourcing for disease mention annotation in PubMed abstracts
Benjamin M. Good, Max Nanis, Andrew I. Su
Identifying concepts and relationships in biomedical text enables knowledge to be applied in computational analyses that would otherwise be impossible. As a result, many biological natural language processing (BioNLP) projects attempt to address this challenge. However, the state of the art in BioNLP still leaves much room for improvement in terms of precision, recall and the complexity of knowledge structures that can be extracted automatically. Expert curators are vital to the process of knowledge extraction but are always in short supply. Recent studies have shown that workers on microtasking platforms such as Amazon’s Mechanical Turk (AMT) can, in aggregate, generate high-quality annotations of biomedical text.
Here, we investigated the use of the AMT in capturing disease mentions in Pubmed abstracts. We used the recently published NCBI Disease corpus as a gold standard for refining and benchmarking the crowdsourcing protocol. After merging the responses from 5 AMT workers per abstract with a simple voting scheme, we were able to achieve a maximum f measure of 0.815 (precision 0.823, recall 0.807) over 593 abstracts as compared to the NCBI annotations on the same abstracts. Comparisons were based on exact matches to annotation spans. The results can also be tuned to optimize for precision (max = 0.98 when recall = 0.23) or recall (max = 0.89 when precision = 0.45). It took 7 days and cost $192.90 to complete all 593 abstracts considered here (at $.06/abstract with 50 additional abstracts used for spam detection).
This experiment demonstrated that microtask-based crowdsourcing can be applied to the disease mention recognition problem in the text of biomedical research articles. The f-measure of 0.815 indicates that there is room for improvement in the crowdsourcing protocol but that, overall, AMT workers are clearly capable of performing this annotation task.
Dagens Næringslivs overgang til Lucene/Solr søkCominvent AS
Foredrag på GoOpen, Oslo, 2011 (Norwegian language)
NHST Media Group lager nettsidene for bl.a. Dagens Næringsliv, Dagens IT og en rekke engelskspråklige bransjeaviser. Systemutvikler Hans Jørgen Hoel og søke-arkitekt Jan Høydahl forteller om prosessen etter at det ble besluttet å erstatte søkeløsningen fra FAST med fri programvare Apache Solr. Vi vil forsøke å besvare bl.a.: Hvilke utfordringer møtte vi som følge av forskjeller i de to plattformene? Hvorfor bygde vi vårt eget søkerammeverk? Har det nye søket innfridd forventningene?
Se også www.goopen.no, www.cominvent.com og www.nhst.no og Twitter hashtag #GoOpen
Gene Wiki and Mark2Cure update for BD2KBenjamin Good
An introduction to the Gene Wiki project with an emphasis on the use of the new WikiData project. Also describes mark2cure, a citizen science initiative oriented on biomedical text mining.
BioThings API: Building a FAIR API Ecosystem for Biomedical KnowledgeChunlei Wu
My talk at NCI's CBIIT speaker series:
https://wiki.nci.nih.gov/display/CBIITSpeakers/2019/01/02/Jan+16%2C+Chunlei+Wu%2C+BioThings+API
A companion blog post: https://ncip.nci.nih.gov/blog/the-network-of-biothings/
See more details about BioThings project at http://biothings.io.
Scott Edmunds flashtalk on "Rewarding Reproducibility and Method Publishing the GigaScience Way" from Beyond the PDF 2 "Making it Happen" session. 20/3/13
Making Data FAIR on WikiData - Andra WaagmeesterOpenAIRE
Making Data FAIR on WikiData presented by Andra Waagmeester during the OpenAIRE workshop Services to support FAIR data, Vienna: https://www.openaire.eu/openaire-workshop-making-services-fair-vienna-april-24th-2019
RDMkit, a Research Data Management Toolkit. Built by the Community for the ...Carole Goble
https://datascience.nih.gov/news/march-data-sharing-and-reuse-seminar 11 March 2022
Starting in 2023, the US National Institutes of Health (NIH) will require institutes and researchers receiving funding to include a Data Management Plan (DMP) in their grant applications, including the making their data publicly available. Similar mandates are already in place in Europe, for example a DMP is mandatory in Horizon Europe projects involving data.
Policy is one thing - practice is quite another. How do we provide the necessary information, guidance and advice for our bioscientists, researchers, data stewards and project managers? There are numerous repositories and standards. Which is best? What are the challenges at each step of the data lifecycle? How should different types of data? What tools are available? Research Data Management advice is often too general to be useful and specific information is fragmented and hard to find.
ELIXIR, the pan-national European Research Infrastructure for Life Science data, aims to enable research projects to operate “FAIR data first”. ELIXIR supports researchers across their whole RDM lifecycle, navigating the complexity of a data ecosystem that bridges from local cyberinfrastructures to pan-national archives and across bio-domains.
The ELIXIR RDMkit (https://rdmkit.elixir-europe.org (link is external)) is a toolkit built by the biosciences community, for the biosciences community to provide the RDM information they need. It is a framework for advice and best practice for RDM and acts as a hub of RDM information, with links to tool registries, training materials, standards, and databases, and to services that offer deeper knowledge for DMP planning and FAIR-ification practices.
Launched in March 2021, over 120 contributors have provided nearly 100 pages of content and links to more than 300 tools. Content covers the data lifecycle and specialized domains in biology, national considerations and examples of “tool assemblies” developed to support RDM. It has been accessed by over 123 countries, and the top of the access list is … the United States.
The RDMkit is already a recommended resource of the European Commission. The platform, editorial, and contributor methods helped build a specialized sister toolkit for infectious diseases as part of the recently launched BY-COVID project. The toolkit’s platform is the simplest we could manage - built on plain GitHub - and the whole development and contribution approach tailored to be as lightweight and sustainable as possible.
In this talk, Carole and Frederik will present the RDMkit; aims and context, content, community management, how folks can contribute, and our future plans and potential prospects for trans-Atlantic cooperation.
Data policy must be partnered with data practice. Our researchers need to be the best informed in order to meet these new data management and data sharing mandates.
This presentation was provided by Violeta Ilik of Northwestern University during the NISO Virtual Conference held on Feb 15, 2017, entitled Institutional Repositories: Ensuring Yours is Populated, Useful and Thriving. The DOI for this presentation is http://dx.doi.org/10.18131/G3VP6R
Scott Edmunds slides for class 8 from the HKU Data Curation (module MLIM7350 from the Faculty of Education) course covering open science and data publishing
HathiTrust Research Center Secure CommonsBeth Plale
Introduces HTRC secure commons, expanded secure infrastructure and services for text mining of HT digital data. Shows results comparing n-gram discovery using Solr full text index and a framework using mapReduce. Compute time over 1 million digital volumes is 1 day with 1024 cores. Weaknesses of Solr in n-gram identification are explored.
This is an overview of the Data Biosphere Project, its goals, its architecture, and the three core projects that form its foundation. We also discuss data commons.
CINECA webinar slides: Data Gravity in the Life Sciences: Lessons learned fro...CINECAProject
We live in an era of cloud computing. Many of the services in the life sciences are keenly planning cloud transformations, seeking to create globally distributed ecosystems of harmonised data based on standards from organisations like GA4GH. CINECA faces similar challenges, gathering cohort datasets from all over the globe, many of which are pinned in place, due to their size, legal restrictions, or other considerations. But is “bringing compute to the data” always the right choice? In this webinar, based on experiences from the Human Cell Atlas Data Coordination Platform and other projects from EMBL-EBI, we will explore the concept of “data gravity”: The idea that whilst there are forces that may hold data in one place, there are others that require it to be mobile. We’ll consider how effectively planning a cloud strategy requires consideration of the gravity of datasets, and the impact it may have on team skills required, incentives for good practice, and storage and compute costs.
The CINECA webinar series aims to discuss ways to address common challenges and share best practices in the field of cohort data analysis, as well as distribute CINECA project results. All CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions. Please note that all webinars are recorded and available for posterior viewing. CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions.
This webinar took place on 12th November 2020 and is part of the CINECA webinar series.
For previous and upcoming CINECA webinars see:
https://www.cineca-project.eu/webinars
Integrating Pathway Databases with Gene Ontology Causal Activity ModelsBenjamin Good
The Gene Ontology (GO) Consortium (GOC) is developing a new knowledge representation approach called ‘causal activity models’ (GO-CAM). A GO-CAM describes how one or several gene products contribute to the execution of a biological process. In these models (implemented as OWL instance graphs anchored in Open Biological Ontology (OBO) classes and relations), gene products are linked to molecular activities via semantic relationships like ‘enables’, molecular activities are linked to each other via causal relationships such as ‘positively regulates’, and sets of molecular activities are defined as ‘parts’ of larger biological processes. This approach provides the GOC with a more complete and extensible structure for capturing knowledge of gene function. It also allows for the representation of knowledge typically seen in pathway databases.
Here, we present details and results of a rule-based transformation of pathways represented using the BioPAX exchange format into GO-CAMs. We have automatically converted all Reactome pathways into GO-CAMs and are currently working on the conversion of additional resources available through Pathway Commons. By converting pathways into GO-CAMs, we can leverage OWL description logic reasoning over OBO ontologies to infer new biological relationships and detect logical inconsistencies. Further, the conversion helps to increase standardization for the representation of biological entities and processes. The products of this work can be used to improve source databases, for example by inferring new GO annotations for pathways and reactions and can help with the formation of meta-knowledge bases that integrate content from multiple sources.
Pathways2GO: Converting BioPax pathways to GO-CAMsBenjamin Good
Presentation at the Gene Ontology Consortium Annual Meeting. Describing the automatic conversion of biochemical pathways in the Reactome Knowledge Base into the Gene Ontology 'Causal Activity Model' representation.
When the Heart BD2K grant was originally written. We proposed to build something called “Big Data World” to help advance citizen science, scientific crowdsourcing and science education – especially in bioinformatics. This past year, this idea has become Science Game Lab ( https://sciencegamelab.org ) . A collaboration between the Su laboratory at Scripps Research, Playmatics LLC, and recently the creators of WikiPathways.
(Poster) Knowledge.Bio: an Interactive Tool for Literature-based Discovery Benjamin Good
PubMed now indexes roughly 25 million articles and is growing by more than a million per year. The scale of this “Big Knowledge” repository renders traditional, article-based modes of user interaction unsatisfactory, demanding new interfaces for integrating and summarizing widely distributed knowledge. Natural language processing (NLP) techniques coupled with rich user interfaces can help meet this demand, providing end-users with enhanced views into public knowledge, stimulating their ability to form new hypotheses.
Knowledge.Bio provides a Web interface for exploring the results from text-mining PubMed. It works with subject, predicate, object assertions (triples) extracted from individual abstracts and with predicted statistical associations between pairs of concepts. While agnostic to the NLP technology employed, the current implementation is loaded with triples from the SemRep-generated SemmedDB database and putative gene-disease pairs obtained using Leiden University Medical Center’s ‘Implicitome’ technology.
Users of Knowledge.Bio begin by identifying a concept of interest using text search. Once a concept is identified, associated triples and concept-pairs are displayed in tables. These tables have text-based and semantic filters to help refine the list of triples to relations of interest. The user then selects relations for insertion into a personal knowledge graph implemented using cytoscape.js. The graph is used as a note-taking or ‘mind-mapping’ structure that can be saved offline and then later reloaded into the application. Clicking on edges within a graph or on the ‘evidence’ element of a triple displays the abstracts where that relation was detected, thus allowing the user to judge the veracity of the statement and to read the underlying articles.
Knowledge.Bio is a free, open-source application that can provide, deep, personal, concise, shareable views into the “Big Knowledge” scattered across the biomedical literature.
Application: http://knowledge.bio
Source code: https://bitbucket.org/sulab/kb1/
Building a massive biomedical knowledge graph with citizen scienceBenjamin Good
The life sciences are faced with a rapidly growing array of technologies for measuring the molecular states of living things. From sequencing platforms that can assemble the complete genome sequence of a complex organism involving billions of nucleotides in a few days to imaging systems that can just as rapidly churn out millions of snapshots of cells, biology is truly faced with a data deluge. To translate this information into new knowledge that can guide the search for new medicines, biomedical researchers increasingly need to build on the existing knowledge of the broad community. Prior knowledge can help guide searches through the masses of new data. Unfortunately, most biomedical knowledge is represented solely in the text of journal articles. Given that more than a million such articles are published every year, the challenge of using this knowledge effectively is substantial. Ideally, knowledge such as the interrelations between genes, drugs and diseases would be represented in a knowledge graph that enabled queries like: “show me all the genes related to this disease or related to any drugs used to treat this disease”. Systems exist that attempt to extract this information automatically from text, but the quality of their output remains far below what can be obtained by human readers. We are developing a new platform that taps the language comprehension abilities of citizen scientists to help excavate a queryable knowledge graph from the biomedical literature. In proof-of-concept experiments, we have demonstrated that lay-people are capable of extracting meaningful information from complex biological text. The information extracted using this community intelligence framework can surpass the efforts of individual experts in quality while also offering the potential to achieve massive scale. In this presentation we will describe the results of early experiments and introduce our prototype citizen science platform: http://mark2cure.org.
Branch: An interactive, web-based tool for building decision tree classifiersBenjamin Good
A crucial task in modern biology is the prediction of complex phenotypes, such as breast cancer prognosis, from genome-wide measurements. Machine learning algorithms can sometimes infer predictive patterns, but there is rarely enough data to train and test them effectively and the patterns that they identify are often expressed in forms (e.g. support vector machines, neural networks, random forests composed of 10s of thousands of trees) that are highly difficult to understand. In addition, it is generally unclear how to include prior knowledge in the course of their construction.
Decision trees provide an intuitive visual form that can capture complex interactions between multiple variables. Effective methods exist for inferring decision trees automatically but it has been shown that these techniques can be improved upon via the manual interventions of experts. Here, we introduce Branch, a new Web-based tool for the interactive construction of decision trees from genomic datasets. Branch offers the ability to: (1) upload and share datasets intended for classification tasks (in progress), (2) construct decision trees by manually selecting features such as genes for a gene expression dataset, (3) collaboratively edit decision trees, (4) create feature functions that aggregate content from multiple independent features into single decision nodes (e.g. pathways) and (5) evaluate decision tree classifiers in terms of precision and recall. The tool is optimized for genomic use cases through the inclusion of gene and pathway-based search functions.
Branch enables expert biologists to easily engage directly with high-throughput datasets without the need for a team of bioinformaticians. The tree building process allows researchers to rapidly test hypotheses about interactions between biological variables and phenotypes in ways that would otherwise require extensive computational sophistication. In so doing, this tool can both inform biological research and help to produce more accurate, more meaningful classifiers.
A prototype of Branch is available at http://biobranch.org/
The Cure: Making a game of gene selection for breast cancer survival predictionBenjamin Good
Background: Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility and biological interpretability. Methods that take advantage of structured prior knowledge (e.g. protein interaction networks) show promise in helping to define better signatures but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes previously unheard of.
Objective: The main objective of this study was to test the hypothesis that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from players of an open, Web-based game. We envisioned capturing knowledge both from the player’s prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game.
Methods: We developed and evaluated an online game called “The Cure” that captured information from players regarding genes for use in predictors of breast cancer survival. Information gathered from game play was aggregated using a voting approach and used to create rankings of genes. The top genes from these rankings were evaluated using annotation enrichment analysis, comparison to prior predictor gene sets, and by using them to train and test machine learning systems for predicting 10-year survival.
Results: Between its launch in Sept. 2012 and Sept. 2013, The Cure attracted more than 1,000 registered players who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data showed significant enrichment for genes known to be related to key concepts such as Cancer, Disease Progression, and Recurrence (P < 1.1e-07). In terms of the accuracy of models trained using them, these gene sets provided comparable performance to gene sets generated using other methods including those used in commercial tests. The Cure is available at http://genegames.org/cure/
Poster: Microtask crowdsourcing for disease mention annotation in PubMed abst...Benjamin Good
Benjamin M. Good, Max Nanis, Andrew I. Su
Identifying concepts and relationships in biomedical text enables knowledge to be applied in computational analyses that would otherwise be impossible. As a result, many biological natural language processing (BioNLP) projects attempt to address this challenge. However, the state of the art in BioNLP still leaves much room for improvement in terms of precision, recall and the complexity of knowledge structures that can be extracted automatically. Expert curators are vital to the process of knowledge extraction but are always in short supply. Recent studies have shown that workers on microtasking platforms such as Amazon’s Mechanical Turk (AMT) can, in aggregate, generate high-quality annotations of biomedical text.
Here, we investigated the use of the AMT in capturing disease mentions in Pubmed abstracts. We used the recently published NCBI Disease corpus as a gold standard for refining and benchmarking the crowdsourcing protocol. After merging the responses from 5 AMT workers per abstract with a simple voting scheme, we were able to achieve a maximum f measure of 0.815 (precision 0.823, recall 0.807) over 593 abstracts as compared to the NCBI annotations on the same abstracts. Comparisons were based on exact matches to annotation spans. The results can also be tuned to optimize for precision (max = 0.98 when recall = 0.23) or recall (max = 0.89 when precision = 0.45). It took 7 days and cost $192.90 to complete all 593 abstracts considered here (at $.06/abstract with 50 additional abstracts used for spam detection).
This experiment demonstrated that microtask-based crowdsourcing can be applied to the disease mention recognition problem in the text of biomedical research articles. The f-measure of 0.815 indicates that there is room for improvement in the crowdsourcing protocol but that, overall, AMT workers are clearly capable of performing this annotation task.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Lateral Ventricles.pdf very easy good diagrams comprehensive
Opportunities and challenges presented by Wikidata in the context of biocuration
1. Opportunities and challenges
presented by Wikidata in the
context of biocuration
Benjamin Good
BioCreative, Corvalis Oregon 2016
@bgood
bgood@scripps.edu
http://www.slideshare.net/goodb
3. Is to data
as Wikipedia is to text
“Giving more people more access to more knowledge”
A free and open repository of knowledge
• Initiated by WikiMedia Germany
• In transition to the WikiMedia Foundation
• Not a grant funded ‘project’… as stable as Wikipedia
5. Elements of the kb are called ‘items’
https://www.wikidata.org/wiki/Q146
6. Items are unique concepts,
used to link different language
Wikipedias together
Q146
Af:Kat
En:cat
Als:Hauskatze
Ang:Catte
Av:Keto
7. Items are described by “statements” that link
together to form the language-independent
wikidata knowledge graph
Cat
Domesticated
Animal
Animal
Subclass Of
Subclass Of
Animalia
Taxon name
Kingdom
Taxon rank
9. Item: Q414043
RELN
Genomic start: 103471784
GenLoc assembly:
GRCh38
Stated in:
Ensembl Release 83
Retrieved:
19 January 2016
Value (numeric)
Property
Claim Qualifiers
References
https://www.wikidata.org/wiki/Q414043
Statement
Genomic position for Reelin gene
10. Item: Q414043
RELN
Encodes: Reelin (protein) Stated in:
NCBI homo sapiens
annotation release 107
Retrieved:
19 January 2016
Value (item)
Property
Claim Qualifiers
References
https://www.wikidata.org/wiki/Q414043
Statement
Linking the Reelin gene to a protein it encodes
11. Item: Q13561329
Reelin
Cell component: dendrite
Determination method:
• ISS (Sequence or structural
Similarity)
• IEA (Electronic annotation)
Stated in:
Uniprot
Retrieved:
21 March 2016
Value (item)
Property
Claim Qualifiers
References
https://www.wikidata.org/wiki/Q13561329
Statement
Gene ontology annotation for Reelin protein
with evidence codes modeled as qualifiers
13. Inter-item links form a giant knowledge graph
Everything is connected
Reelin, Heart disease,
Barack Obama,
everything..
https://query.wikidata.org
SPARQL endpoint for Wikidata
14. Sample of current biomedical content
• All human, mouse genes and proteins (swissprot)
• All Gene Ontology terms
• All Human Disease Ontology terms
• All FDA approved drugs
• 109 reference microbial genomes
Burgstaller-Muelbacher et al (2016) Database
Mitraka et al (2015) Semantic Web Applications for the Life Sciences
Putman et al (2016) Database
15. http://tinyurl.com/biowiki-sparql
Sample queries that are currently possible:
• “GO cellular localization annotations for Reelin with
evidence code ISS”
• “Diseases treated by Metformin”
• “Diseases that might be treated by Metformin”
http://query.wikidata.org
16. Example question: repurposing Metformin
http://tinyurl.com/zem3oxz
Metformin
?disease
interacts
with
protein
SLC22A3encoded by genetic
association
Might
treat ?
Solute carrier
family 22
member 3
SLC22A3
prostate
cancer
18. API
Flatfiles
The dominant paradigm for open biocuration
API
Flatfiles
Your
Database
Your
Database
Your
Databasexrefs
Your
Database
Pain points
• API or flatfile parsing
• Ambiguous or non-existent xrefs
• Persistence of funding
• Too much information to curate
My Web
Application
My Database
My Database Curators
My Research Grants
$
Biomedical
knowledge
19. A new paradigm for open biocuration?
My
Application
Our Database?
Our Database Curators
And our community
Biomedical
knowledge
My
Application
My
Application
My Research Grants
$
Reducing the pain
• Reduces API/parser proliferation
• Forces up-front integration
• Facilitates coordination
• Ensures that if funding is lost,
data is not
• Invites community input
20. A new platform for open biocuration?
My
Application
Our Database Curators
And our community
Biomedical
knowledge
My
Application
My
Application
My Research Grants
$
• SPARQL = a common
API for accessing
content
• 1 endpoint to
maintain…
• Its working
21. The first application built on wikidata is Wikipedia
Our Database Curators
And our community
Biomedical
knowledge
Our
Applications
Our
Applications
Su, Schriml, Pavlidis R01 Grant…
$
24. Impact of wikidata on Wikipedia
Gene Wiki
Version 1.
{{GNF_Protein_box | Name = Reelin| image = |
image_source = | PDB = {{PDB2|4AD9}} | HGNCid = 18512 |
MGIid = | Symbol = LACTB2 | AltSymbols =; CGI-83 |
IUPHAR = | ChEMBL = | OMIM = None | ECnumber = |
Homologene = 9349 | GeneAtlas_image1 = |
GeneAtlas_image2 = | GeneAtlas_image3 = |
Protein_domain_image = | Function =
{{GNF_GO|id=GO:0005515 |text = protein binding}}
{{GNF_GO|id=GO:0016787 |text = hydrolase activity}}
{{GNF_GO|id=GO:0046872 |text = metal ion binding}} |
Component = {{GNF_GO|id=GO:0005739 |text =
mitochondrion}} | Process = {{GNF_GO|id=GO:0008152
|text = metabolic process}} | Hs_EntrezGene = 51110 |
Hs_Ensembl = ENSG00000147592 | Hs_RefseqmRNA =
NM_016027 | Hs_RefseqProtein = NP_057111 |
Hs_GenLoc_db = hg38 | Hs_GenLoc_chr = 8 |
Hs_GenLoc_start = 70635318 | Hs_GenLoc_end = 70669174
| Hs_Uniprot = Q53H82 | Mm_EntrezGene = 212442 |
Mm_Ensembl = ENSMUSG00000025937 |
Mm_RefseqmRNA = NM_145381 | Mm_RefseqProtein =
NP_663356 | Mm_GenLoc_db = mm10 | Mm_GenLoc_chr =
1 | Mm_GenLoc_start = 13623330 | Mm_GenLoc_end =
13660546 | Mm_Uniprot = Q99KR3 | path = PBB/51110}}
=
Gene Wiki
Version 2.
{{Infobox gene}}
• All data in
Wikidata
• 1 Lua script works
for all genes
=
(1 of these for every gene)
25. Wikidata use increasing on Wikipedia
• https://en.wikipedia.org/wiki/Category:
Templates_using_data_from_Wikidata
• 81 templates indicate that they use it
29. Challenges
• Community ontology building
• Establishing computable trust
• Expanding the knowledge base
“Dogs and cats living together!
Mass hysteria!”
(leave that for ICBO)
BioCreative
Challenges?
30. ‘Statements’ on Wikidata
2013 2016
100M
Statements
Bad
Good
Ugly
60M
20M
https://tools.wmflabs.org/wikidata-todo/stats.php
31. Computable trust
RELN
Genomic start: 103471784
GenLoc assembly:
GRCh38
Claim
Add References
1. Add references
2. Check that references concur
with the claim or not
3. Estimate ‘truthiness’ of claim
4. Provide humans with
sources to follow up.
• References can come from databases,
articles in PubMed, etc.
BadUgly
Good
32. Expanding the knowledge base
RELN
? ?
?
New Claims
• Given external knowledge
source (text or database)
• Create claims and
references automatically
with very high precision
• Allow for human verification
PMID: 77901
PMID: 523070
33. Unique characteristics Wikidata w/ regard to
IE tasks.
• 16,000+ ‘active’ editors and growing
• Could be a powerful crowdsourcing resource
• Must be kept involved or will block progress
• Constrained data model and some limits on content type
• CC0 requirement
34. One known attempt: “StrepHit”
• Individual Engagement Grant (IEG) from the Wikimedia Foundation
(30k, Start Jan. 2016)
• Goal to:
• “Generate trust and reliability over Wikidata content”
• “Alleviate the burden of manual curation” (sounds familiar, right?)
• Ended up working on Biographical and Soccer data…
36. ‘Primary Sources’ optional userscript that
wikidata users can install.
Approving a suggested reference for the claim
that came from German Wikipedia
https://www.wikidata.org/wiki/Wikidata:Primary_sources_tool
37. The Wikidata Game(s) (= microtasks…)
https://tools.wmflabs.org/wikidata-game/
https://tools.wmflabs.org/wikidata-game/distributed/
Code for making your own!
38. StrepHit, Primary Sources, Wikidata games…
All works in progress
https://meta.wikimedia.org/wiki/Grants:IEG/StrepHit:_Wikidata_Statements_Validation_via_References/Renewal
40. Acknowledgements
Gene Wikidata Team
Andra Waagmeester (Micelio)
Sebastian Burgstaller (Scripps)
Tim Putman (Scripps)
Elvira Mitraka (U Maryland)
Julia Turner (Scripps)
Justin Leong (UBC)
Lynn Schriml (U Maryland)
Paul Pavlidis (UBC)
Andrew Su (Scripps)
Ginger Tsueng (Scripps)
Contact
bgood@scripps.edu
@bgood on twitter
Adapted logo
Su Laboratory at TSRI The 16,950 other active editors of
Wikidata and especially the 693 that
joined last month and the 809 that
joined the month before that and
the 721 that joined the month
before that..
This work was supported by the US National Institute of Health
(grants GM089820 and U54GM114833) and by the Scripps
Translational Science Institute with an NIH-NCATS Clinical and
Translational Science Award (CTSA; 5 UL1 TR001114).
41.
42. Social controls
• Anyone can
• Add or edit labels, descriptions, statements, references etc. on existing items
• Create new items
• Link items to Wikipedia articles
• Query using https://query.wikidata.org
• Read and write small numbers of edits with
https://www.wikidata.org/w/api.php
• Propose a new property
• Request a bot account for high-volume automated editing
Here be dragons..
43. Properties (as of April 10, 2016)
• 2196 active properties
• 114 new properties that have been proposed but not yet approved
Proposal
https://www.wikidata.org/wiki/Wikidata:Property_proposal
44. After proposal, community discussion
• Each property is left open
for discussion by anyone
until
• An administrator or other
person blessed with the
power either creates it or
decides not to create it
based on the discussion
• People that enjoy ontology
arguments needed here!
Lengthy (cut-off) discussion of proposal for ‘extinct’ property
47. Proposal discussions
• Can not be avoided
• The discussions are long and tiring but important
• Many of the people involved are quite experienced
• All are trying to make something great
• Persistence and patience required
I will spend a good portion of the talk explaining what wikidata is. With that, all of you smart people can start thinking on your own about how it might influence your work. Of course I will provide some possible ideas.
Labels and descriptions in many languages
about 25 million items, 100 million statements, resulting in about 1 billions triples in the sparql endoint
What it is, to what can we do with it ?
This is the central point I want to make. Wikidata can be used to to build knowledge-based applications, lowering the barrier to entry for building apps and reducing challenges of downstream data integration.
Before coming back to this, I will explain why.
This is the central point I want to make. Wikidata can be used to to build knowledge-based applications, lowering the barrier to entry for building apps and reducing challenges of downstream data integration.
May
By mixing the data into wikidata, we reduce API proliferation, easing application formation.
Over 1 billion triples
Fast
20-30 queries per second,
Avg about 6 seconds to answer queries
Stable since around September 2015
By mixing the data into wikidata, we reduce API proliferation, easing application formation.
Over 1 billion triples
Fast
Stable since around September 2015
This is the first application of the work that we have done
By mixing the data into wikidata, we reduce API proliferation, easing application formation.
Over 1 billion triples
Fast
Stable since around September 2015
Now that we have some ideas about how it can be used, consider the problems with it ways that the NLP community might help solve them.
ontology – define properties and patterns for their use
Trust – given a claim recorded on wikidata, verify that it matches (or conflicts with) statements made in other sources and provide references to those sources.
Data – add more
Given a claim, validate or invalidate and provide a reference
Could easily come up with many thousands of claims in the biomedical domain of the ugly or bad nature.
Lexicographical analysis
Relation extraction
Frame semantics
Machine learning
Very ambitious goal of producing both the “A box and the T box” (ie both identifying new properties and extracting relations using them)
Tool provided by Google project for loading data from Freebase that requires a human ‘thumbs up’.
currently StrepHit team is proposing to shift their work to improving this tool
https://meta.wikimedia.org/wiki/Grants:IEG/StrepHit:_Wikidata_Statements_Validation_via_References/Renewal
https://meta.wikimedia.org/wiki/Grants_talk:IEG/StrepHit:_Wikidata_Statements_Validation_via_References#Support_from_ContentMine
By mixing the data into wikidata, we reduce API proliferation, easing application formation.
Over 1 billion triples
Fast
Stable since around September 2015