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</div>
</div>
</div>
<!-- <div id="architecture" class="uk-container uk-section">
<div class="uk-padding-small">
<h2 class="uk-text-center">Architecture</h2>
<div class="uk-flex uk-flex-center">
<div class="uk-width-4-5@m">
<h3 class="uk-margin-medium-top portal-color">How we build it</h3>
<div>
<p>
OpenAIRE collects metadata records from more than 70K scholarly communication sources from all over the
world, including Open Access institutional repositories, data archives, journals.
All the metadata records (i.e. descriptions of research products) are put together in a data lake,
together
with records from Crossref, Unpaywall, ORCID, Grid.ac, and information about projects provided by national
and international funders.
Dedicated inference algorithms applied to metadata and to the full-texts of Open Access publications
enrich
the content of the data lake with links between research results and projects, author affiliations,
subject
classification, links to entries from domain-specific databases.
Duplicated organisations and results are identified and merged together to obtain an open, trusted, public
resource enabling explorations of the scholarly communication landscape like never before.
</p>
</div>
</div>
</div>
<div class="uk-flex uk-flex-center uk-inline uk-margin-medium-top">
<img [src]="'assets/graph-assets/about/architecture/'+architectureImage"
class="uk-width-4-5 architecture-image">
<a class="uk-position-absolute uk-transform-center uk-padding" style="left: 27%; top: 51.5%"
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</a>
</div>
<div id="tabs_card"
class="uk-margin-xlarge-top uk-padding-small">
<div class="uk-card uk-card-default uk-card-body architecture-card">
<ul #tabs uk-tab class="uk-tab">
<li><a>Aggregation</a></li>
<li><a>Deduplication</a></li>
<li><a>Enrichment</a></li>
<li><a>Post-Cleaning</a></li>
<li><a>Indexing</a></li>
<li><a>Stats Analysis</a></li>
</ul>
<ul class="uk-switcher uk-margin">
<li>
<div class=" uk-margin-large-top uk-text-small">
<img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"
src="assets/graph-assets/about/architecture/aggregation.png" alt="Aggregation">
<div class="uk-margin-bottom uk-margin-medium-right uk-text-small lines-18"
[class.multi-line-ellipsis]="!aggregationReadMore">
<div>
OpenAIRE collects metadata records from a variety of content providers as described in
<a href="https://www.openaire.eu/aggregation-and-content-provision-workflows" target="_blank">https://www.openaire.eu/aggregation-and-content-provision-workflows</a>.
<br><br>
OpenAIRE aggregates metadata records describing objects of the research life-cycle from content
providers compliant to the
<a href="https://guidelines.openaire.eu" target="_blank">OpenAIRE guidelines</a>
and from entity registries (i.e. data sources offering authoritative lists of entities, like
OpenDOAR,
re3data, DOAJ, and funder databases).
After collection, metadata are transformed according to the OpenAIRE internal metadata model, which
is
used to generate the final OpenAIRE Research Graph that you can access from the OpenAIRE portal and
the
APIs.
<br><br>
The transformation process includes the application of cleaning functions whose goal is to ensure
that
values are harmonised according to a common format (e.g. dates as YYYY-MM-dd) and, whenever
applicable,
to a common controlled vocabulary.
The controlled vocabularies used for cleansing are accessible at
<a href="http://api.openaire.eu/vocabularies"
target="_blank">http://api.openaire.eu/vocabularies</a>.
Each vocabulary features a set of controlled terms, each with one code, one label, and a set of
synonyms.
If a synonym is found as field value, the value is updated with the corresponding term.
Also, the OpenAIRE Research Graph is extended with other relevant scholarly communication sources
that
are too big to be integrated via the “normal” aggregation mechanism: DOIBoost (which merges
Crossref,
ORCID, Microsoft Academic Graph, and Unpaywall), and ScholeXplorer, one of the Scholix hubs offering
a
large set of links between research literature and data.
</div>
</div>
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<a (click)="aggregationReadMore = true" class="custom-explore-toggle">Read more<span uk-icon="chevron-down"></span></a>
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</div>
</div>
</li>
<li>
<div class="uk-margin-bottom uk-text-small">
<ul class="uk-subnav button-tab" uk-switcher>
<li><a>Clustering</a></li>
<li><a>Matching & Election</a></li>
</ul>
<ul class="uk-switcher uk-margin align-list">
<li>
<img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"
src="assets/graph-assets/about/architecture/deduplication.svg" alt="Deduplication">
<div class="uk-margin-bottom uk-margin-medium-right uk-text-small lines-18"
[class.multi-line-ellipsis]="!dedupClusteringReadMore">
<div>
<div>
Clustering is a common heuristics used to overcome the N x N complexity required to match
all
pairs of objects to identify the equivalent ones.
The challenge is to identify a clustering function that maximizes the chance of comparing
only
records that may lead to a match, while minimizing the number of records that will not be
matched while being equivalent.
Since the equivalence function is to some level tolerant to minimal errors (e.g. switching
of
characters in the title, or minimal difference in letters), we need this function to be not
too
precise (e.g. a hash of the title), but also not too flexible (e.g. random ngrams of the
title).
On the other hand, reality tells us that in some cases equality of two records can only be
determined by their PIDs (e.g. DOI) as the metadata properties are very different across
different versions and no clustering function will ever bring them into the same cluster.
To match these requirements OpenAIRE clustering for products works with two functions:
</div>
<ul class="portal-circle">
<li>
<div>DOI: the function generates the DOI when this is provided as part of the record
properties;
</div>
</li>
<li>
<div>
Title-based function: the function generates a key that depends on
(i) number of significant words in the title (normalized, stemming, etc.),
(ii) module 10 of the number of characters of such words, and
(iii) a string obtained as an alternation of the function prefix(3) and suffix(3) (and
vice
versa) o the first 3 words (2 words if the title only has 2). For example, the title
“Entity
deduplication in big data graphs for scholarly communication” becomes “entity
deduplication
big data graphs scholarly communication” with two keys key “7.1entionbig” and
“7.1itydedbig”
(where 1 is module 10 of 54 characters of the normalized title.
</div>
</li>
</ul>
<div>
To give an idea, this configuration generates around 77Mi blocks, which we limited to 200
records each (only 15K blocks are affected by the cut), and entails 260Bi matches. Matches
in
a
block are performed using a “sliding window” set to 80 records. The records are sorted
lexicographically on a normalized version of their titles. The 1st record is matched against
all
the 80 following ones, then the second, etc. for an NlogN complexity.
</div>
</div>
</div>
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</li>
<li>
<img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"
src="assets/graph-assets/about/architecture/deduplication.svg" alt="Deduplication">
<div class="uk-margin-bottom uk-margin-medium-right uk-text-small lines-18"
[class.multi-line-ellipsis]="!dedupMatchingAndElectionReadMore">
<div>
<div>
Once the clusters have been built, the algorithm proceeds with the comparisons.
Comparisons are driven by a decisional tree that:
</div>
<ul class="uk-list">
<li class="uk-margin-small-bottom">
<div>
<span class="portal-color">1.</span> Tries to capture equivalence via PIDs: if records
share
a PID then they are equivalent
</div>
</li>
<li class="uk-margin-small-bottom">
<div>
<span class="portal-color">2.</span> Tries to capture difference:
</div>
<ul class="uk-list">
<li class="uk-margin-small-bottom">
<div>
<span class="portal-color">a.</span>
If record titles contain different “numbers” then they are different (this rule is
subject to different feelings, and should be fine-tuned);
</div>
</li>
<li class="uk-margin-small-bottom">
<div>
<span class="portal-color">b.</span>
If record contain different number of authors then they are different;
</div>
</li>
<li class="uk-margin-small-bottom">
<div>
<span class="portal-color">c.</span>
Note that different PIDs do not imply different records, as different versions may
have
different PIDs.
</div>
</li>
</ul>
</li>
<li>
<div><span class="portal-color">3.</span> Measures equivalence:</div>
<ul class="uk-list portal-circle">
<li>
<div>
The titles of the two records are normalised and compared for similarity by applying
the
Levenstein distance algorithm.
The algorithm returns a number in the range [0,1], where 0 means “very different”
and
1
means “equal”.
If the distance is greater than or equal 0,99 the two records are identified as
duplicates.
</div>
</li>
<li>
<div>Dates are not regarded for equivalence matching because different versions of the
same records should be merged and may be published on different dates, e.g.
pre-print
and published version of an article.
</div>
</li>
</ul>
</li>
</ul>
<div>
Once the equivalence relationships between pairs of records are set, the groups of
equivalent
records are obtained (transitive closure, i.e. “mesh”).
From such sets a new representative object is obtained, which inherits all properties from
the
merged records and keeps track of their provenance.
The ID of the record is obtained by appending the prefix “dedup_” to the MD5 of the first ID
(given their lexicographical ordering).
A new, more stable function to generate the ID is under development, which exploits the DOI
when
one of the records to be merged includes a Crossref or a DataCite record.
</div>
</div>
</div>
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</li>
</ul>
</div>
</li>
<li>
<div class="uk-margin-bottom uk-text-small">
<ul class="uk-subnav button-tab uk-grid uk-grid-small" uk-switcher>
<li><a>Mining</a></li>
<li><a>Bulk tagging/ Deduction</a></li>
<li><a>Propagation</a></li>
</ul>
<ul class="uk-switcher uk-margin">
<li>
<img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"
src="assets/graph-assets/about/architecture/enrichment.svg" alt="Enrichment">
<div class="uk-margin-bottom uk-margin-medium-right uk-text-small lines-18"
[class.multi-line-ellipsis]="!enrichmentMiningReadMore">
<div>
The OpenAIRE Research Graph is enriched by links mined by OpenAIRE’s full-text mining
algorithms
that scan the plaintexts of publications for funding information, references to datasets,
software URIs, accession numbers of bioetities, and EPO patent mentions.
Custom mining modules also link research objects to specific research communities, initiatives
and infrastructures.
In addition, other inference modules provide content-based document classification, document
similarity, citation matching, and author affiliation matching.
<br><br>
<span class="portal-color">Project mining</span>
in OpenAIRE text mines the full-texts of publications in order to extract matches to funding
project codes/IDs.
The mining algorithm works by utilising
(i) the grant identifier, and
(ii) the project acronym (if available) of each project.
The mining algorithm:
(1) Preprocesses/normalizes the full-texts using several functions, which depend on the
characteristics of each funder (i.e., the format of the grant identifiers), such as stopword
and/or punctuation removal, tokenization, stemming, converting to lowercase; then
(2) String matching of grant identifiers against the normalized text is done using database
techniques; and
(3) The results are validated and cleaned using the context near the match by looking at the
context around the matched ID for relevant metadata and positive or negative words/phrases, in
order to calculate a confidence value for each publication->project link.
A confidence threshold is set to optimise high accuracy while minimising false positives, such
as matches with page or report numbers, post/zip codes, parts of telephone numbers, DOIs or
URLs, accession numbers.
The algorithm also applies rules for disambiguating results, as different funders can share
identical project IDs; for example, grant number 633172 could refer to H2020 project EuroMix
but
also to Australian-funded NHMRC project “Brain activity (EEG) analysis and brain imaging
techniques to measure the neurobiological effects of sleep apnea”.
Project mining works very well and was the first Text & Data Mining (TDM) service of OpenAIRE.
Performance results vary from funder to funder but precision is higher than 98% for all
funders
and 99.5% for EC projects.
Recall is higher than 95% (99% for EC projects), when projects are properly acknowledged using
project/grant IDs.
<br><br>
<span class="portal-color">Dataset extraction</span>
runs on publications full-texts as described in “High pass text-filtering for Citation
matching”, TPDL 2017[1].
In particular, we search for citations to datasets using their DOIs, titles and other metadata
(i.e., dates, creator names, publishers, etc.).
We extract parts of the text which look like citations and search for datasets using database
join and pattern matching techniques.
Based on the experiments described in the paper, precision of the dataset extraction module is
98.5% and recall is 97.4% but it is also probably overestimated since it does not take into
account corruptions that may take place during pdf to text extraction.
It is calculated on the extracted full-texts of small samples from PubMed and arXiv.
<br><br>
<span class="portal-color">Software extraction</span>
runs also on parts of the text which look like citations.
We search the citations for links to software in open software repositories, specifically
github, sourceforge, bitbucket and the google code archive.
After that, we search for links that are included in Software Heritage (SH,
https://www.softwareheritage.org) and return the permanent URL that SH provides for each
software project.
We also enrich this content with user names, titles and descriptions of the software projects
using web mining techniques.
Since software mining is based on URL matching, our precision is 100% (we return a software
link
only if we find it in the text and there is no need to disambiguate).
As for recall rate, this is not calculable for this mining task.
Although we apply all the necessary normalizations to the URLs in order to overcome usual
issues
(e.g., http or https, existence of www or not, lower/upper case), we do not calculate cases
where a software is mentioned using its name and not by a link from the supported software
repositories.
<br><br>
<span class="portal-color">For the extraction of bio-entities</span>, we focus on Protein Data
Bank (PDB) entries.
We have downloaded the database with PDB codes and we update it regularly.
We search through the whole publication’s full-text for references to PDB codes.
We apply disambiguation rules (e.g., there are PDB codes that are the same as antibody codes
or
other issues) so that we return valid results.
Current precision is 98%.
Although it's risky to mention recall rates since these are usually overestimated, we have
calculated a recall rate of 98% using small samples from pubmed publications.
Moreover, our technique is able to identify about 30% more links to proteins than the ones
that
are tagged in Pubmed xmls.
<br><br>
<span class="portal-color">Other text-mining modules</span> include mining for links to EPO
patents, or custom mining modules for linking research objects to specific research
communities,
initiatives and infrastructures, e.g. COVID-19 mining module.
Apart from text-mining modules, OpenAIRE also provides a document classification service that
employs analysis of free text stemming from the abstracts of the publications.
The purpose of applying a document classification module is to assign a scientific text one or
more predefined content classes.
In OpenAIRE, the currently used taxonomies are arXiv, MeSH (Medical Subject Headings), ACM and
DDC (Dewey Decimal Classification, or Dewey Decimal System).
</div>
</div>
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</li>
<li>
<img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"
src="assets/graph-assets/about/architecture/enrichment.svg" alt="Enrichment">
<div class="uk-margin-bottom uk-margin-medium-right uk-text-small">
The Deduction process (also known as “bulk tagging”) enriches each record with new information
that
can be derived from the existing property values.
<br><br>
As of September 2020, three procedures are in place to relate a research product to a research
initiative, infrastructure (RI) or community (RC) based on:
<ul class="portal-circle">
<li>subjects (2.7M results tagged)</li>
<li>Zenodo community (16K results tagged)</li>
<li>the data source it comes from (250K results tagged)</li>
</ul>
The list of subjects, Zenodo communities and data sources used to enrich the products are
defined
by
the managers of the community gateway or infrastructure monitoring dashboard associated with the
RC/RI.
</div>
</li>
<li>
<img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"
src="assets/graph-assets/about/architecture/enrichment.svg" alt="Enrichment">
<div class="uk-margin-bottom uk-margin-medium-right uk-text-small lines-18"
[class.multi-line-ellipsis]="!enrichmentPropagationReadMore">
<div>
This process “propagates” properties and links from one product to another if between the
two
there is a “strong” semantic relationship.
<br><br>
As of September 2020, the following procedures are in place:
<ul class="portal-circle">
<li>
Propagation of the property “country” to results from institutional repositories:
e.g. publication collected from an institutional repository maintained by an italian
university will be enriched with the property “country = IT”.
</li>
<li>
Propagation of links to projects: e.g. publication linked to project P “is supplemented
by”
a dataset D.
Dataset D will get the link to project P.
The relationships considered for this procedure are “isSupplementedBy” and
“supplements”.
</li>
<li>
Propagation of related community/infrastructure/initiative from organizations to
products
via affiliation relationships: e.g. a publication with an author affiliated with
organization O.
The manager of the community gateway C declared that the outputs of O are all relevant
for
his/her community C.
The publication is tagged as relevant for C.
</li>
<li>
Propagation of related community/infrastructure/initiative to related products: e.g.
publication associated to community C is supplemented by a dataset D.
Dataset D will get the association to C.
The relationships considered for this procedure are “isSupplementedBy” and
“supplements”.
</li>
<li>
Propagation of ORCID identifiers to related products, if the products have the same
authors:
e.g. publication has ORCID for its authors and is supplemented by a dataset D. Dataset D
has
the same authors as the publication. Authors of D are enriched with the ORCIDs available
in
the publication.
The relationships considered for this procedure are “isSupplementedBy” and
“supplements”.
</li>
</ul>
</div>
</div>
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</li>
</ul>
</div>
</li>
<li>
<div class="uk-text-small uk-margin-large-top">
<img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"
src="assets/graph-assets/about/architecture/post_cleaning.svg" alt="Post Cleaning">
<div class="uk-margin-bottom uk-margin-medium-right">
<p>
The aggregation processes are continuously running and apply vocabularies as they are in a given
moment of time.
It could be the case that a vocabulary changes after the aggregation of one data source has
finished, thus the aggregated content does not reflect the current status of the controlled
vocabularies.
<br><br>
In addition, the integration of ScholeXplorer and DOIBoost and some enrichment processes applied on
the raw and on the de-duplicated graph may introduce values that do not comply with the current
status of the OpenAIRE controlled vocabularies.
For these reasons, we included a final step of cleansing at the end of the workflow materialisation.
The output of the final cleansing step is the final version of the OpenAIRE Research Graph.
</p>
</div>
</div>
</li>
<li>
<div class="uk-text-small uk-margin-large-top">
<img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"
src="assets/graph-assets/about/architecture/indexing.svg" alt="Indexing">
<div class="uk-margin-bottom uk-margin-medium-right">
<p>
The final version of the OpenAIRE Research Graph is indexed on a Solr server that is used by the
OpenAIRE portals (EXPLORE, CONNECT, PROVIDE) and APIs, the latter adopted by several third-party
applications and organizations, such as:
</p>
<ul class="portal-circle">
<li class="uk-margin-small-bottom">
<span class="portal-color">EOSC</span>
--The OpenAIRE Research Graph APIs and Portals will offer to the EOSC an Open Science Resource
Catalogue, keeping an up to date map of all research results (publications, datasets, software),
services, organizations, projects, funders in Europe and beyond.
</li>
<li class="uk-margin-small-bottom">
<span class="portal-color">DSpace & EPrints</span>
repositories can install the OpenAIRE plugin to expose OpenAIRE compliant metadata records via
their
OAI-PMH endpoint and offer to researchers the possibility to link their depositions to the funding
project, by selecting it from the list of project provided by OpenAIRE
</li>
<li>
<span class="portal-color">EC participant portal (Sygma - System for Grant Management)</span>
uses the OpenAIRE API in the “Continuous Reporting” section.
Sygma automatically fetches from the OpenAIRE Search API the list of publications and datasets in
the
OpenAIRE Research Graph that are linked to the project.
The user can select the research products from the list and easily compile the continuous
reporting
data of the project.
</li>
</ul>
</div>
</div>
</li>
<li>
<div class="uk-text-small uk-margin-large-top">
<img
class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image uk-padding-large uk-padding-remove-top uk-padding-remove-horizontal"
src="assets/graph-assets/about/architecture/stats_analysis.svg" alt="Stats Analysis">
<div class="uk-margin-bottom uk-margin-medium-right">
<p>
The OpenAIRE Research Graph is also processed by a pipeline for extracting the statistics and
producing
the charts for funders, research initiative, infrastructures, and policy makers that you can see on
MONITOR.
Based on the information available on the graph, OpenAIRE provides a set of indicators for
monitoring
the funding and research impact and the uptake of Open Science publishing practices,
such as Open Access publishing of publications and datasets, availability of interlinks between
research
products, availability of post-print versions in institutional or thematic Open Access repositories,
etc.
</p>
</div>
</div>
</li>
</ul>
</div>
</div>
<div class="uk-padding-small uk-margin-top">
<h6>References</h6>
<ul class="uk-text-small portal-circle">
<li>
<a href="https://aka.ms/msracad" target="_blank">Microsoft Academic Graph</a>
which is made available under the ODC Attribution License.<br>
For more information on Microsoft Academic Graph please also read
<a href="https://docs.microsoft.com/en-us/academic-services/graph/resources-faq" target="_blank">here</a>.
</li>
<li>
<a href="https://www.openaire.eu/aggregation-and-content-provision-workflows" target="_blank">https://www.openaire.eu/aggregation-and-content-provision-workflows</a>
</li>
</ul>
<a class="portal-link uk-icon-link uk-text-small uk-text-bold uk-text-uppercase" routerLink="/resources/references">
See all references <icon name="arrow_right" class="uk-margin-small-left"></icon>
</a>
</div>
</div>
</div> -->
<div id="metrics" class="uk-container uk-container-large uk-section">
<div class="uk-padding-small">
<h2 class="uk-text-center uk-margin-medium-bottom">Data & Metrics</h2>

4
src/app/app.component.ts
View File

@ -68,12 +68,10 @@ export class AppComponent implements OnInit, OnDestroy {
new MenuItem("apis", "APIs", "/develop/overview.html", "", false, [], null, {}, [
new MenuItem("", "Overview", "/develop/overview.html", "", false, [], null, {}),
new MenuItem("", "Authentication", "/develop/authentication.html", "", false, [], null, {}),
new MenuItem("", "Bulk Access", "/develop/graph-dumps.html", "", false, [], null, {}),
new MenuItem("", "Selective Access", "/develop/api.html", "", false, [], null, {}),
new MenuItem("", "Response Metadata Format", "/develop/response-metadata-format.html", "", false, [], null, {}),
new MenuItem("references", "References", "", "/resources/references", false, [], null, {}),
]),
new MenuItem("publications", "Publications", "/doc/category/publications", "", false, [], null, {}, [], []),
new MenuItem("publications", "Publications", "/doc/category/publications", "", false, [], null, {})
]
},
{

10
src/app/app.routing.ts
View File

@ -11,11 +11,11 @@ const appRoutes: Routes = [
isHome: true
}, canDeactivate: [PreviousRouteRecorder]
},
{
path: 'resources',
loadChildren: () => import('./resources/resources.module').then(m => m.ResourcesModule),
canDeactivate: [PreviousRouteRecorder]
},
// {
// path: 'resources',
// loadChildren: () => import('./resources/resources.module').then(m => m.ResourcesModule),
// canDeactivate: [PreviousRouteRecorder]
// },
{
path: 'support',
loadChildren: () => import('./contact/contact.module').then(m => m.ContactModule),

2
src/app/openaireLibrary

@ -1 +1 @@
Subproject commit e837d55e2793cc7feb2da90a070d71854a298009
Subproject commit 8f2fce6a19c92c88b3cd6617d9d6b1983768ec47

6
src/sitemap.xml
View File

@ -32,18 +32,12 @@
</url>
<url>
<loc>https://graph.openaire.eu/develop/graph-dumps-old.html</loc>
</url>
<url>
<loc>https://graph.openaire.eu/develop/bulk-projects.html</loc>
</url>
<url>
<loc>https://graph.openaire.eu/develop/api.html</loc>
</url>
<url>
<loc>https://graph.openaire.eu/develop/response-metadata-format.html</loc>
</url>
<url>
<loc>https://graph.openaire.eu/resources/references</loc>
</url>
<url>
<loc>https://graph.openaire.eu/support</loc>

8
src/styles.css
View File

@ -159,3 +159,11 @@ ul.uk-text-small.portal-circle li:before {
a.number:hover, a.number:focus, a.number:active {
color: #4687e6;
}
/* Navigation bar tweaks - Graph only */
.uk-navbar-dropdown-nav .uk-nav-sub li a {
color: rgba(255,255,255,.7);
}
.uk-navbar-dropdown-nav .uk-nav-sub li a:hover {
color: #fff;
}