=Paper=
{{Paper
|id=Vol-1383/paper23
|storemode=property
|title=SKOS as a Key Element in Enterprise Linked Data Strategies
|pdfUrl=https://ceur-ws.org/Vol-1383/paper23.pdf
|volume=Vol-1383
|dblpUrl=https://dblp.org/rec/conf/semweb/Blumauer14
}}
==SKOS as a Key Element in Enterprise Linked Data Strategies==
https://ceur-ws.org/Vol-1383/paper23.pdf
SKOS as a Key Element in
Enterprise Linked Data Strategies
Andreas Blumauer
Semantic Web Company GmbH, Mariahilfer Straße 70/8,
1070 Vienna, Austria
a.blumauer@semantic-web.at
Abstract. The challenges in implementing linked data technologies in
enterprises are not limited to technical issues only. Projects like these deal also
with organisational hurdles to be crossed, for instance the development of
employee skills in the area of knowledge modelling and the implementation of
a linked data strategy which foresees a cost-effective and sustainable
infrastructure of high-quality and linked knowledge graphs. SKOS is able to
play a key role in enterprise linked data strategies due to its relative simplicity
in parallel with its ability to be mapped and extended by other controlled
vocabularies, ontologies, entity extraction services and linked open data.
1 Introduction
The use of semantic web methodologies and technologies has been perceived to be an
appealing solution approach for various issues in enterprise information management
and data integration [1][2]. Amongst others, the following application scenarios are
typically discussed in the context of enterprise linked data: content enrichment and
content augmentation, integrated views on distributed data (enterprise mashups),
knowledge visualisation, smart assistants and search-driven applications. In all cases,
linked data graphs build the basis for such applications, thus the following question is
most central for a linked data strategy: How can an enterprise create and maintain
knowledge graphs in a sustainable way, whereas the corresponding processes should
be as cost-effective as possible and the resulting graphs should be of quality grades
which are acceptable for enterprise information services.
1.1 Creating Knowledge Graphs with the Simple Knowledge Organization
System (SKOS)
Since the Simple Knowledge Organization System (SKOS) has become a W3C
recommendation in 2009 [3], several scenarios to make use of SKOS ontologies have
been described [4][5] and many discussions around key design principles such as
"minimal ontological commitment" have been led [6]. The increasing use of SKOS
can be documented by two key facts:
1. SKOS concept is among over 108,000 classes the most used RDF class in
the Linked Open Data cloud1
2. When NISO has published ISO 25964 – the international standard for
thesauri and interoperability with other vocabularies, one of the main efforts
was to reach the goal of interoperability with SKOS and other schemas2
The usage of SKOS as a starting point to create knowledge graphs in enterprises has
in parallel to its relative simplicity (in contrast to other ontology languages like OWL)
one other main advantage: It has been accepted broadly as a standard and is well
understood by various stakeholders (database engineers, information professionals,
knowledge managers), thus little force is needed to overcome the resistance to the
introduction of something new like SKOS based vocabularies.
1.2 SKOS as a nucleus of large enterprise knowledge graphs
The scope of a full-blown enterprise knowledge graph is much broader than a
taxonomy would be able to cover. When taking a closer look on it, we will find all
kinds of categorized and annotated legacy data and documents, additional schemas
which describe various business objects, their specific relations and attributes, and
linked data graphs from third-party sources, especially from the linked open data
cloud [7]. Additionally, a large knowledge graph will contain a lot of mappings
between resources from different (named) graphs. What role can SKOS based graphs
play in this complex information system?
When starting with SKOS thesauri to describe all kinds of ‘things’ (or ‘business
objects’), their names and relations to each other, we don’t have to think about classes
or any kind of restrictions or axioms yet. This makes it easy to build a first robust
layer of business semantics on top of distributed and heterogeneous information
sources. SKOS is based on RDF, thus an extension by additional schemas (classes and
properties) is feasible out-of-the-box, at least from a technical point of view. Either
custom schemas or already existing ones like FOAF, ORG or schema.org can be used
to put additional semantics on top of a SKOS based knowledge graph.
For example, a video game which has been created initially as a SKOS concept with
the preferred label ‘SimCity’ and as a narrower concept of another SKOS concept
labeled with ‘Video Game’ will be classified in a second step by
http://schema.org/SoftwareApplication which is a subclass of
http://schema.org/CreativeWork, whereas both are an RDF class derived
from schema.org [8]. By using additional schemas, we can express more specific
semantics around SKOS graphs and we can map them more accurately with already
existing database schemas.
1 http://stats.lod2.eu/rdf_classes?sort=overall (accessed on September 15, 2014)
2 http://www.niso.org/schemas/iso25964/#skos (accessed on September 15, 2014)
In addition, when linking SKOS concepts with resources from linked data graphs like
Geonames or DBpedia, we can harvest vast amounts of facts around concepts, e.g.
birth dates, number of employees, longitude, latitude, etc.
When asking the principal question, why haven’t we started the modelling process
with a more complex schema from the very beginning, we should consider the
following two aspects:
x Domain experts are one of the most valuable resources when creating
enterprise knowledge graphs. They most often have no or little expertise
with ontology modelling. Thus, they feel more comfortable with a bottom-up
approach which starts with concrete instances of classes, and not with a
rather abstract schema.
x Although the ontology of SKOS offers only a few ways to express semantics
explicitly, the implicit semantics of a SKOS thesaurus is rich enough in
many cases to be made available explicitly and machine-readable by
applying additional ontologies. One of the most principal design patterns of a
linked data architect should be, to convert implicit semantics of existing
information sources into explicit semantic models based on standards, not
the other way around!
2 Integrating Knowledge Graphs in Enterprise Information
Systems
Enterprise information systems benefit from using knowledge graphs in different
ways. The following three scenarios shall illustrate some options:
1. Knowledge graphs are browsed by end-users and serve as a knowledge base.
In order to make company knowledge better accessible, interfaces should be
integrated in popular platforms like Microsoft SharePoint or
Atlassian Confluence. As an example for this scenario serves Semantic
Knowledge Base for SharePoint3 which is based on standards-compliant
semantic knowledge graphs providing a user interface seamlessly integrated
in SharePoint.
2. Knowledge graphs are used to link and to index information from various
sources. In many cases it will be used for automatic tagging of
enterprise information. A knowledge graph can also be used for concept-
based search and to generate more complex queries than usually used by
simple full-text search. Examples and online-demos for this approach can be
found at the PoolParty Semantic Integrator4 site.
3 http://www.semantic-sharepoint.com/?page_id=11 (accessed on September 16, 2014)
4
http://www.poolparty.biz/portfolio-item/semantic-integrator/ (accessed on September 16,
2014)
3. Knowledge graphs can also be used in enterprises to analyse and to visualise
complex contexts and correlations between business objects 5. As part of a
linked data strategy, components like these should be standard-compliant to
increase the chance of being reused all over the places which
increases usability. In this concrete example mentioned above, only a
SPARQL-endpoint [9] is required to deploy the user application.
3 Conclusion
Knowledge graphs play a central role when establishing linked data based enterprise
information systems. Application scenarios are manifold, but creation,
maintenance and the actual use of it can be a tedious process. In this paper we
described some strategies to develop linked data infrastructures which have turned out
to be practically applicable. The use of SKOS to get started with knowledge graphs is
one of the key elements in an enterprise linked data strategy.
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recommendation, 15.
5
For example, visit http://vocabulary.semantic-web.at/semweb/2184.visual to find a visual
representation of 'Linked Data'