Date: Fri, 29 Mar 2024 08:05:27 -0400 (EDT)
Message-ID: <1625697018.30356.1711713927657@lyrasis1-roc-mp1>
Subject: Exported From Confluence
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How VI=
VO differs from a spreadsheet
VIVO stores data as RDF individuals =E2=80=93 entities that ar=
e instances of OWL classes and that relate to each other throug=
h OWL object properties and have attributes represented as OWL datatype pro=
perty statements. Put very simply,
- Classes are types =E2=80=93 Person, Event, Book, Organization
- Individuals are instances of types =E2=80=93 Joe DiMaggio, the 201=
4 AAAS Conference, Origin of Species, or the National Science Foundation
- Object properties express relationships between to individual entities,=
whether of the same or different types =E2=80=93 a book has a chapter=
, a person attends an event
- Datatype properties (data properties for short) express simple attribut=
e relationships for one individual =E2=80=93 a time, date, short strin=
g, or full page of text
Every class, property, and individual has a URI that serves as an identi=
fier but is also resolvable on the Web as Linked=
Data.
A triple in RDF has a subject, a predicate, and an object =E2=80=93=
think back to sentence diagramming in junior high school if you go back th=
at far.
So far all this would fit in a spreadsheet =E2=80=93 one row per st=
atement, but never more than 3 columns.
This may not be the most useful analogy, however, since you can't say ve=
ry much in a single, 3-part statement and your data will be much more compl=
ex than that. A person has a first name, middle name, and lastname; a=
nd a title, a position linking them to a department; many research interest=
s; sometimes hundreds of publications, and so on. In a spreadsheet wo=
rld you can keep adding columns to represent more attributes, but that soon=
breaks down.
But let's stay simple and say you only want to load basic directory info=
rmation in VIVO =E2=80=93 name, title, department, email address, and =
phone number.
name |
title |
department |
email |
phone |
Sally Jones |
Department Chair |
Entomology |
sj24@university.edu |
888 777-6666 |
Ruth Ginsley |
Professor |
Classics |
rbg12@university.edu |
888 772-1357 |
Sam Snead |
Therapist |
Health Services |
ss429@university.edu |
888 772-7831 |
Piece of cake =E2=80=93 until you have a person with 2 (or 6)=
positions (it happens). Or two offices and hence two work phone numb=
ers.
VIVO breaks data apart in chunks of information that belong together in =
much the same way that relational databases store information about differe=
nt types of things in different tables. There's no right or wrong way=
to do it, but VIVO stores the person independently of the position and the=
department =E2=80=93 the position has information a person's title an=
d their beginning and ending date, while the department will be connected t=
o multiple people through their positions but also to grants, courses, and =
other information.
VIVO even stores a person's detailed name and contact information as a <=
a class=3D"external-link" href=3D"http://www.w3.org/TR/vcard-rdf/" rel=3D"n=
ofollow">vCard, a W3C standard ontology that itself contains multiple c=
hunks of information. More on this later.
Storing information in small units removes the need to specify how many =
'slots' to allow in the data model while also allowing information to be as=
sembled in different ways for different purposes =E2=80=93 a familiar =
concept from the relational database world, but accomplished through an eve=
n more granular structure of building blocks =E2=80=93 the RDF triple.=
There are other important differences as well =E2=80=93 if you =
want to learn more, we recommend The Semantic Web for the Working Ontologist, by Dean Allemang and =
Jim Hendler.
Where=
VIVO data typically comes from
It's perfectly possible, if laborious, to add all data to VIVO through i=
nteractive editing. For a small research institution this may be the prefer=
red method, and many VIVO institutions employ students or staff to add and =
update information for which no reliable system of record exists. If =
VIVO has been hooked up to the institutional single sign-on, self-editing b=
y faculty members or researchers has been used effectively, especially if b=
asic information has been populated and the focus of self editing is on pop=
ulating research interests, teaching statements, professional service, or o=
ther more straightforward information.
This approach does not scale well to larger institutions, and full relia=
nce on researchers do editing brings its own problems of training, consiste=
ncy in data entry, and motivating people to keep content up to date. Many V=
IVOs are supported through libraries that are more comfortable providing ca=
rrots than sticks and want the VIVO outreach message to focus on positive b=
enefits vs. threats about stale content or mandates to enter content for an=
nual reporting purposes.
VIVO is all about sharing local data both locally and globally. Mu=
ch of the local data typically resides in "systems of record" =E2=80=
=93 formerly entirely locally hosted and often homegrown, but more recently=
starting to migrate to open source software (e.g, Kuali) or to cloud s=
olutions.
These systems of record are often silos used for a defined set of busine=
ss purposes such as personnel and payroll, grants administration, course re=
gistration and management, an institutional repository, news and communicat=
ions, event calendar(s), or extension. Even when the same software pl=
atform is used, local metadata requirements and functional customizations m=
ay make any data source unique.
For this reason, coupled with variations in local technology skills and =
support environments, the VIVO community has not developed cookie cutter, o=
ne-size-fits-all solutions to ingest.
Here's a rough outline of the approach we recommend for people new to VI=
VO when they start thinking about data:
- Look at other VIVOs to see what data other people have loaded and how i=
t appears
- Learn the basics about RDF and the Semantic Web (there's an excellent <=
a class=3D"external-link" href=3D"http://workingontologist.org/" rel=3D"nof=
ollow">book) =E2=80=93 write 10 lines of RDF
- Download and install VIVO on a PC or Mac, most easily through the lates=
t VIVO Vagrant instance
- Add one person, a position, a department, and some keywords using the V=
IVO interactive editor
- Export the data as RDF and study it =E2=80=93 what did you think y=
ou were entering, and how different is the structure that VIVO creates?
- Add three more people, their positions, and a couple more departments. =
Try exporting again to get used to the RDF VIVO is creating.
- Try typing a small RDF file with a few new records, using the same URI =
as VIVO created if you are repeating the same entity. Load that RDF into VI=
VO through the Add/Remove RDF command on the Site Admin menu =E2=80=93=
does it look right? If not, double check your typing.
- Repeat this with a publication to learn about the Authorship structure&=
nbsp;=E2=80=93 enter by hand, study, hand edit a couple of new records, ing=
est, test
- Don't be afraid to start over with a clean database =E2=80=93 you =
are learning, not going directly to production.
- When you start to feel comfortable, think about what data you want to s=
tart with =E2=80=93 perhaps people and their positions and titles. Don=
't start with the most challenging and complex data first.
- Work with a tool like Karma to learn semantic model=
ing and produce RDF, however simple, without writing your own code
- Load the data from Karma into your VIVO =E2=80=93 does it look rig=
ht? If not, look at the differences in the RDF =E2=80=93 it may =
be subtle
- Then start looking more deeply at the different ingest methods describe=
d later in this section of the wiki and elsewhere
Cleaning d=
ata prior to loading
If you have experience with data you'll no doubt be familiar with having=
to clean your data before importing it into software designed to show that=
data publicly. There will be duplicates, uncontrolled name variants, missi=
ng data, mis-spellings, capitalization differences, oddball characters, int=
ernational differences in names, broken links, and very likely other proble=
ms.
VIVO is not smart enough to fix these problems since it in almost all ca=
ses has no means of distinguishing an important subtle variation from an er=
ror. With the Semantic Web, 'inference' does not refer to the ability to se=
cond-guess intent, nor 'reasoning' to invoking artificial intelligence.
Furthermore, it's almost always easier to fix dirty data before it goes =
into VIVO than to find and fix it after loading.
There's another important consideration =E2=80=93 the benefit of fi=
xing data at its source. Putting data into a VIVO makes that data muc=
h more discoverable through both searching and browsing. People will find e=
rrors that are most likely hidden in the source system of record =E2=
=80=93 but if you fix them in VIVO, they won't be correct in the source sys=
tem, and the next ingest runs the risk of overwriting changes.
There are many ways to clean data, but in the past few years Open Refin=
e has emerged offering quite remarkable capabilities including the abil=
ity to develop changes interactively that can then be saved as scripts to r=
un repeatedly on larger batches.
Matc=
hing against data already in VIVO
The first data ingest from a clean slate is the easiest one. After=
the first time there's a high likelihood that updates will refer to many o=
f the same people, organizations, events, courses, publications, etc.  =
;You don't want to introduce duplicates, so you need to check new informati=
on against what's already there.
Sometimes this is straightforward because you have a unique identifier t=
o definitively distinguish new data from existing =E2=80=93 your insti=
tutional identifier for people, but also potentially an ORCID iD, a DOI=
a> on a publication, or a FundRef registry<=
/a> identifier.
Often the matching is more challenging =E2=80=93 is 'Smith, D' a ma=
tch with Delores Smith or David Smith? Names will be misspelled, abbr=
eviated, changed over time =E2=80=93 and while a lot can be done with =
relatively simple matching algorithms, this remains an area of research, an=
d of homegrown, ad-hoc solutions embedded in local workflows.
One way to check each new entry is to query VIVO, directly or through a =
derivative SPARQL endpoint using Fuseki or other tool, to see the identifie=
r or name matches data already in VIVO. As VIVO scales, there can be perfor=
mance reasons for extracting a list of names of people, organizations, and =
other common data types for ready reference during an ingest process.  =
;It's also sometimes possible to develop an algorithm for URI construction =
that is reliably repeatable and hence avoids the need to query =E2=80=
=93 for example, if you embed the unique identifier for a person in their V=
IVO URI through some simple transformation, you should be able to predict a=
person's URI without querying VIVO. If that person has not yet been added =
to VIVO, adding them through a later ingest is not a problem as long as the=
same URI would have been generated. This requires that the identifie=
r embedded in the URI is not private or sensitive (e.g., not the U.S. socia=
l security number) and will not change.
One caution here =E2=80=93 it's important to think carefully about =
the default namespace you use for URIs in VIVO if you want linked data requ=
ests to work =E2=80=93 please see A simple installation and look for the basic settings of the V=
itro.defaultNamespace.
Doing f=
urther cleanup once in VIVO
VIVO's interactive editing can be helpful in fixing problems in relative=
ly small datasets but it's important to remember that if data originate out=
side of VIVO and are not corrected at the source, subsequent updates will l=
ikely re-introduce the error.
It's also common to have discrepancies in the source data =E2=80=93=
for example, the naming conventions and identifiers used for departments i=
n a personnel database vs. those used in a grants administration system. Th=
ere is a command to merge two individuals in VIVO, specifying which URI to =
retain, but that will combine the statements associated with both, leading =
to duplicate labels and potentially other duplicates.
VIVO has only limited support for owl:sameAs reasoning due to the perfor=
mance implications of having to query for all statements about more than on=
e URI whenever rendering information about any one URI declared to be sameA=
s another.
Many VIVO installations have developed workflows for checking VIVO data,=
ranging from broken link checkers to nightly SPARQL queries to detect malf=
ormed data such as publications without authors, people without identifiers=
, 'orphaned' dates no longer referenced by any property statements, and so =
forth. These tools have been discussed on previous Apps and Tools Interest Group cal=
ls that have been recorded and uploaded to YouTube.
Major options including the Harvester, semantic ingest tools such as Kar=
ma, and XSLT
Alternative approaches to ingest and making ingest repeatable
Challenges in the data, in workflow, in working incrementally, in modeli=
ng, and in migration
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