Reposted Op-Ed from Wikipedia Signpost
This piece examines a key question that new Wikimedia projects such as
Wikidata
are concerned with: how to properly represent knowledge digitally at
the most basic level. There is a real danger that an inflexible,
proscriptive approach to data will severely limit the scope,
capabilities and ultimate utility of the resulting service.
At one level, the textual representation of information and knowledge
in books and online can be viewed as simply another serialisation and
packaging format for information and knowledge, optimised for human
rather than machine consumption. Within the Wikipedia community –
Wikidata and elsewhere – there is a perceived utility in using more
structured, machine-friendly formats to enable better information
sharing and computer-assisted analysis and research. However, there
remains a lot of debate about the best approach, to which I will
contribute the views I have developed over nearly a decade of research
and development projects at the Bodleian Library
[1] and before that, through my involvement with knowledge management in the commercial domain.
My first point is that
metadata and
data
are really different aspects of a continuum. In the majority of cases,
data acquires much of its meaning only in connection with its
context,
which is largely contained within so-called metadata. This is
especially true for numerical data streams, but holds even for data in
the form of text and images: when and where a text was written are often
critical elements in understanding the meaning.
[2] Data and metadata should be considered not as distinct entities but as complementary facets of a greater whole.
Secondly, there will be no single unifying metadata "standard" (or even a few such standards), so
deal with it! For example,
biosharing.org
lists just under 200 metadata standards for experimental biosciences
alone. The notion of a single standard that led to the development of
MARC, and latterly
RDA,
in the library sphere is simply not applicable to the way in which
metadata is now used within the field of academic enquiry. This means
that any solution to handling digital objects must have a mechanism for
handling a multiplicity of standards, and ideally within an individual
object – for example, bibliographic, rights and preservation metadata
may quite reasonably be encoded using different standards.
[3]
The corollary of this is that if we have such a mechanism there is no
need to abandon existing standards prematurely. This avoidance of
over-proscribing and premature decision-making will be familiar to Agile
developers. Consequently, Wikidata developers would be ill-advised to
aim for a rigid, unitary metadata model – even at a basic level,
representing knowledge is too complex and variable for such an approach.
So how do we balance this proliferation of standards with the desire for
sharing and interoperability? We can find several key areas in which a
consensus view is emerging, not through explicit standard-setting
activities but through experience and necessity. This gives us a good
indication that these are sensible points on which to base longer-term
interoperability.
- An emergent data/object model. Besides the bibliographic
entities, such as digitised texts, images and data, a number of key
types of "context-object" recur when we start to try to build more
complex systems for handling digital information. This can be seen in
such diverse areas as the specifications for TEI, Freebase, CERIF and schema.org. The most important of these elements are people, places, vocabularies/ontologies and the notion of time
dependency. Indeed, for many projects in the humanities, these objects
actually form the basis for expressing ideas and framing discourse using
the conventional bibliographic objects to provide an evidentiary base.
- Aggregations as a key organising tool for this expanded universe of digital objects. In many cases, these aggregations are also objects in their own right, representing content collections, organisations, geopolitical entities and even projects
– each potentially with a history and other attributes. An essential
characteristic of aggregations is that they need not be hierarchical,
but rather a graph capable of capturing the more unstructured, web-like
way people have of organising themselves and their knowledge.[4]
- Agreement on essential common properties. For each object
type there is usually a general consensus on a minimal set of properties
that are sufficient to both uniquely identify an object and provide
enough information to a human reader that the object is the one that
they are interested in. Often, the latter is actually a less strict
requirement as a person can use circumstantial evidence such as the
context in which an object occurs for disambiguation. While it is
desirable to try to capture contextual information systematically, we
have to accept that this is frequently not done. Sources for this common
baseline could include Dublin Core (or dcterms to be explicit) records, DataCite records, gazetteers, and name authority lists, for example.
These common properties are obviously very amenable to storage and
manipulation in a relational database. Indeed, for large-scale data
ingestion with the following clean-up, de-duplication and merging of
records/objects, this is likely to be the best tool for the job.
However, once this task has been completed and we delve into the more
varied elements of the objects, the advantages of a purely relational
database approach are less clear-cut.
Instead, we can treat each object as an independent, web-addressable
entity – which in practice is desirable in its own right as a mode of
publication and dissemination. In particular, we can use search engines
to index across heterogeneous fields –
Apache Solr excels at faceting and grouping, while
ElasticSearch
can index arbitrary XML without schemas (i.e. all of the varied
domain-specific metadata). These tools give users ways into the material
that are much easier to use and more intuitive.
The objects alone are only a part of the picture – the relationships
between objects are critical to the structure of the overall collection.
In fact, in many cases (especially in the humanities) a significant
proportion of research activity actually involves discovering, analysing
and documenting such relationships. The Semantic Web or, more
precisely, the ideas behind the
Resource Description Framework (RDF) and
linked data,
provide a mechanism for expressing these relationships in a way that is
structured, through the use of defined vocabularies, but also flexible
and extensible, through the ability to use multiple vocabularies. While
theoretically it is
possible to express
all metadata in RDF, this is not practical for performance
[5] and usability
[6] reasons, and is unnecessary.
This model of linked data, combining a mix of standardised fields and
less-structured textual content, should not be entirely unfamiliar to
people used to working with
Semantic MediaWiki,
sharing their metadata on Wikidata, or using data boxes in Wikipedia!
However, when applying this model to practical research projects it
emerges that a critical element is still lacking. Although we can
describe relationships between objects using RDF, we are limited to
making assertions of the form [subject][predicate/relationship][object] (the RDF
"triple"). In practice, relatively few statements of this form can be
considered universally and absolutely true. For example: a person may
live at a particular address but only for a certain period of time; the
copyright on a book may last for 50 years, but only in a particular
country. Essentially, what is needed is a mechanism to define the
circumstances under which a relationship can be considered valid. A
number of possible mechanisms could do this – replacing RDF triples with
"quads" that include a context object; annotation of relationships
using
OAC.
These examples are really just special cases of a more general
requirement that is of great interest to scholars. This is the ability
to qualify a relationship or assertion to capture an element of
provenance. Specifically, we need to know who made an assertion, when,
on the basis of what evidence, and under which circumstances it holds.
This may be manifested in several ways:
- Differences of scholarly opinion – it should be possible for there
to be contradictory assertions in the data relating to an object,
provided we can supply the evidence for each point of view.
- Quality of the evidence – information can be incomplete, or just
unclear if we are dealing with digitised materials. In this case we want
to capture the assumptions under which an assertion is made.
- Proximity of evidence – we may have an undated document but if we
know the biography of the author we can place some limits on probable
dates. This evidence is not intrinsic to the object but can be derived
from its context.
- Omissions – collections are usually incomplete for various reasons.
It is important to distinguish the absence of material as a result of
inactivity or specific omission from subsequent failures in collection
building.
These qualifications become especially important when we try to use
computational tools such as analytics and visualisation. Indeed,
projects such as
Mapping the Republic of Letters (Stanford University) are expending significant effort to find ways of representing uncertainty and omission in visualisations.
I believe there needs to be a subtle change in the mindset when
creating reference resources for scholarly purposes (and, arguably, more
generally). Rather than always aiming for objective statements of truth
we need to realise that a large amount of knowledge is derived via
inference from a limited and imperfect evidence base, especially in the
humanities. Thus we should aim to accurately represent
the state of knowledge about a topic, including omissions, uncertainty and differences of opinion.
Notes
- In particular, Cultures of Knowledge.
- Usefully,
most books come with a reasonable amount of metadata (author,
publisher, date, version etc.) encapsulated in the format, but this is
represents somewhat of an anomaly. Before the advent of the book and,
more recently, in online materials, metadata tends to be scarcer.
- However, I concede that it is not unreasonable to expect that things are generally encoded in XML with a defined schema.
- Our
own experience of trying to model the organisational structure of the
University of Oxford (notionally hierarchical) convinced us that this
was essential.
- RDF
databases (triple stores) currently scale to the order of billions of
triples – this limit can be reached quite easily when you consider that
the information in a MARC record for a book in a library may have well
over 100 fields.
- RDF is a very verbose format. Existing domain-specific XML formats can be much easier to read and manipulate.
