XKOS: Extending SKOS for Describing Statistical
Classifications
Franck Cotton, Daniel W. Gillman, and Yves Jaques
Institut National de la Statistique et des Études Économiques, Paris, France
franck.cotton@insee.fr
US Bureau of Labor Statistics, Washington, USA
Gillman.Daniel@bls.gov
UN Population Fund
jaques@unfpa.org
Abstract.A statistical classification scheme is used in statistics to place units in
one and only one category from that scheme. These categories are used as units
of analysis; dimensions in databases, tables, and time series; criteria for subdi-
viding or aggregating populations; etc.
SKOS provides a simple model for rendering a statistical classification scheme
in Linked Open Data (LOD) format. However, it is too simple for some purpos-
es, so XKOS (eXtended Knowledge Organization System) was designed to ex-
tend it in order to allow richer representations of statistical classifications.
This paper describes the statistical needs to address and the results of the XKOS
design work, including some examples on well-known statistical classifications.
Concrete implementations of the vocabulary are also provided, with examples
of real use cases.
Keywords. SKOS, XKOS, classification, scheme, statistics, linked data.
1 Introduction
This paper contains a brief description of the eXtended Knowledge Organization Sys-
tem (XKOS) and a rationale for why it was developed. In particular, there is a focus
on describing statistical classifications with XKOS. XKOS is an extension of the
Simple Knowledge Organization System (SKOS) [1] applicable to the needs of statis-
tical offices and social science data users. As we show in this paper, some limitations
in SKOS leave it inadequate to the task of describing statistical classifications.
XKOS is designed to fill these gaps.
The original SKOS is used widely in LOD applications, as seen in the SKOS Im-
plementation Report1. As a result, a group was formed at the Dagstuhl Workshops
held at Schloβ Dagstuhl2 in Germany on Semantic Statistics for Social, Behavioural,
1
http://www.w3.org/2006/07/SWD/SKOS/reference/20090315/implementation.html
2
http://www.dagstuhl.de
and Economic Sciences: Leveraging the DDI3 Model for the Linked Data Web in
September 20114 and October 20125to look at the suitability of using SKOS in the
statistical data community for LOD work [2].
In this paper, we provide introductory remarks to set the stage for discussion, pro-
vide a short primer on statistical classifications, describe limitations of SKOS to sta-
tistical classifications, and lay out the extensions to SKOS that form the XKOS speci-
fication. In particular, we show how the semantics of classification systems in our
own offices are represented more faithfully by extending SKOS with XKOS through
the use of examples. In a last section, we give examples of concrete implementations
of the vocabulary.
2 Statistical Classifications
A statistical classification scheme (SCS) is representable as a SKOS Concept Scheme
and used for statistical purposes. In the next section, we illustrate the need for exten-
sive semantics to account for the meanings conveyable in SCSs. Here, we say what
an SCS is used for by statistical organizations.
An SCS is a hierarchical skos:ConceptScheme which includes concepts, associated
codes (numeric string labels), short textual names (also labels), definitions, and longer
descriptions that include rules for their use. Examples of SCSs used in the US statis-
tical agencies are
• Standard Occupational Classification6 (SOC)
• North American Standard Industrial Classification7 (NAICS)
By a hierarchy, we mean a system of concepts where each has zero or one parents and
zero or more children. The root, or top concept, has no parent; the leaves, or bottom
concepts, have no children; and the rest have one parent and one or more children.
An SCS may be a flat list (i.e., one level) or a hierarchy, and if it is a hierarchy
then it is with the added proviso that all its concepts are grouped into levels. In every
SCS, the root concept is implicit and is often referred to as the defining concept for
the SCS. All the concepts in one level are the same number of relationships away
from the root, and this number is known as the depth of the level. All the concepts at
each level are mutually exclusive and exhaustive (ME&E), meaning each unit can be
classified to one and only one concept per level. For example, the first level in
NAICS is known as Sectors, and these sectors are the broadest industry categories.
From the perspective of SKOS, the relation between a concept and its parent is one
of the broader than and narrower than kinds, depending which direction one is look-
3
http://www.ddialliance.org
4
http://www.dagstuhl.de/en/program/calendar/evhp/?semnr=11372
5
http://www.dagstuhl.de/en/program/calendar/evhp/?semnr=12422
6
http://www.bls.gov/soc
7
http://www.census.gov/eos/www/naics/
ing. A parent is a broader concept, whereas a child is narrower. We have more to say
about this in the next section.
Each level is defined by its own concept, as the collection of concepts at a particu-
lar level have a common overall meaning. For instance, the first level of NAICS be-
low the root (i.e., industry or economic activity) is the sector, and Government is one
of the sectors.
The most important use of a classification scheme is to classify and organize units
within some domain, for example business establishments by industry. NAICS is
used for this in the US, but due to limitations in coverage for some geographic areas,
establishment sizes, or NAICS concepts, not enough data might be available to report
meaningfully at all levels. So, the lowest level with meaningful data in most of the
concepts is used. However, what determines “meaningful” is a statistical considera-
tion, not germane to SKOS, and out of scope for this paper. The interested reader can
consult a text on statistical sampling [3].
Practically speaking, the rows in tables8 and the dimensions used to specify meas-
ures in a time series (for instance the US Consumer Price Index for specific expendi-
tures and municipalities9) are major uses of SCSs in data dissemination. Tables and
time series report aggregated data that are classified by SCSs. Some of the SCSs may
be hierarchies, so one level is chosen to report data. If the data are sparse at that level,
then there is a danger that data about individuals (people or businesses) is recovera-
ble. In this case, the next higher level is used, and the data are aggregated some more
into the broader categories. This is an important reason for the hierarchical design of
SCSs.
Another use of SCSs is in data collection. The possible answers to questions on a
form or in a questionnaire are the categories in SCSs. When the range of answers
covers all that are possible, and since answer choices must be mutually exclusive, the
ME&E criterion for SCS is satisfied. Another way SCSs are used in data collection is
through classifying textual responses. For instance, the US American Community
Survey asks respondents to briefly describe their jobs, and these descriptions are clas-
sified to NAICS and SOC. The levels selected are based on the detail the questions
are designed to elicit. The US Survey of Occupational Injuries and Illnesses asks
respondents to describe incidents in their workplaces that resulted in loss of work
time. These incidents are classified into the nature of the injury or illness, the affected
body part, the source of the malady, and the event that caused it (see section 3.2).
Statistical agencies that manage SCSs sometimes make versions to reflect changes
in the subject matter domain, and these versions are separate SCSs. However, they
belong to the same family, and that is known as a classification. For instance, NAICS
is updated every 5 years, and each version (a separate SCS) is known by its year.
A model for describing and managing SCSs developed by the international statis-
tical community can be found in [4].
8
http://www.bls.gov/news.release/empsit.a.htm
9
http://www.bls.gov/cpi/cpid1305.pdf
3 SKOS Limitation
3.1 SKOS and What Is Missing
SKOS provides a means for representing knowledge organization systems using RDF,
and this makes the use of SKOS applicable to SCSs. It is beyond the scope of this
paper to provide a detailed description of SKOS. We direct the interested reader to
the SKOS web site10. However, SKOS contains the following basic ideas, whose
definitions we paraphrase here:
• Concept Scheme – any knowledge organization system (including SCSs)
• Concept – any abstract idea or unit of thought
• Definition – formal statement conveying the meaning of a concept
• Label – lexical representation for a concept, may be preferred or alternate; provides
means to communicate the concept
• Notation – a symbolic notation for the concept (such as a code) that is typically
data-typed.
• Semantic Relation – broad category for relations between concepts, such as broad-
er than, narrower than, and related to (these relations can include relations to con-
cepts found in other concept schemes).
The basic ideas listed above are the minimum required to describe an SCS. We can
account for the scheme itself (concept scheme), all its underlying concepts with con-
cept, what each concept means (definition), the labels and codes associated with a
category (label / notation), and relationships with its parent and all its children (se-
mantic relation).
SKOS is based on ISO 25964-1 [5]. This standard describes three basic kinds of
hierarchical relations between concepts: generic, partitive, and instantiation.
Interestingly, SKOS provides only the more generic broader than and narrower
than, which are often referred to in more technical settings as super-ordinate and sub-
ordinate, respectively. Both the generic and partitive relations are specializations of
broader than / narrower than. In the SKOS Primer11, this simplification is acknowl-
edged.
SKOS also specifies an association relation between concepts, but this is not made
any more detailed. Possible detail might includesequential, temporal, and causal
relations. The sequential relation refers to ideas where one is the antecedent of the
other, either temporally or spatially, such as between production and consumption.
The specialized temporal relation is based on time, such as between spring and sum-
mer. Finally, the causal relation relates cause and effect, such as the detonation of a
hydrogen bomb and nuclear fall-out. See [6] for further explanation of the relations-
described here and above.
10
http://www.w3.org/2004/02/skos/
11
http://www.w3.org/TR/skos-primer/
Because levels have a concept associated with them and they have depth (from the
root), there is no satisfactory way to account for them in SKOS. Thus, we need to add
the notion of level. It is a kind of skos:Collection.
3.2 Examples
Below are some examples that illustrate the need for the extensions we have identi-
fied above:
1. The US Standard Occupational Classification System12 (SOC – 2012)
Take, for example
27-2000 – Entertainers and Performers, Sports and Related Workers
27-2040 – Musicians, Singers, and Related Workers
27-2042 – Musicians and Singers
The appropriate relation between 27-2000 and 27-2040 is generic, i.e.Musicians,
Singers and Related Workers is a specialization of Entertainers and Performers,
Sports and Related Workers. The same relation is found between 27-2040 and 27-
2042, i.e.Musicians and Singers is a specialization of Musicians, Singers and Related
Workers. So, the generic relation is needed to specify the semantics of the US SOC.
2. The US Occupational Injury and Illness Classification13 (OIICS – 2012)
Occupational injury and illness is a four-facet classification: nature, body part,
source, and event. In the body part facet, for example
3– Trunk
31 – Chest
313 – Heart
315 – Lungs
32 – Back, including spine, spinal cord
321 – Thoracic
322 – Lumbar
Going from broad to lower detail in this snippet of the body part classification illu-
strates the partitive relation. The chest and back are parts of the trunk. The heart and
lungs are part of the chest. Finally, the thoracic and lumbar regions are part of the
back and spine. Note that it would not be proper to use the generic relation here.
Therefore, the partitive relation is needed to specify the semantics of the US OIICS.
12
http://www.bls.gov/soc/
13
http://www.bls.gov/iif/oshoiics.htm
3. The US American Time Use Survey — Activity Coding Lexicons14, last updated in
2011.
The classification is a hierarchy, but some activity categories depend on what has
occurred before. For instance,
04 – Caring For & Helping non-Household Members
0401 – Caring For & Helping non-Household Children
040104 – Arts & Crafts with non-Household Children
040112 – Dropping Off/Picking Up non-Household Children
Dropping off non-household children is a sequential activity related, in this exam-
ple, to having supervised arts-and-crafts activities (or some other activity in the 04
group) previously. So, there are associations between some pairs of activities within
this classification, though they are not intrinsic to the SCS. In this case, the sequen-
tial or possibly the temporal relation is needed to convey the additional semantics that
some activities depend on the triggering of other prior activities.
4 XKOS
In the following, we list some of the extensions XKOS contains and guide the inter-
ested to reader to another paper that contains more detail[7].
• xkos:belongsTo is used to attach a classification scheme to its classification
• xkos:follows or its sub-property xkos:supercedes is used to relate classification
schemes that are successive versions
• xkos:classifiedUnder is used to indicate a unit is classified by some concept
• xkos:ClassificationLevel (subclass of skos:Collection) is the level; the levels of a
classification scheme are structured as an RDF List, starting with the most aggre-
gated, and the list attached to the classification scheme by the xkos:levels property.
• xkos:depth property expresses the distance of a given level from the root node of
the hierarchy
• xkos:organizedBy property can be used to record the generic name of the items of a
given level (e.g. “section”, “division”, etc.).
• explanatory notes use xkos:coreContentNote, xkos:additionalContentNote,
xkos:inclusionNote, and xkos:exclusionNote, which are sub-properties of
skos:scopeNoteand correspond to a typology of explanatory notes widely used for
statistical classifications
• xkos:ConceptAssociationis a class that can be used to represent correspondences
between classification items across SCSsthrough input or source skos:Concept(s)
and output or target skos:Concept(s).
• xkos:Correspondencegroups a set ofxkos:ConceptAssociation(s) to represent a
concordance or correspondence table between two classification schemes (for ex-
ample two versions of a classification).
14
http://www.bls.gov/tus/lexicons.htm
• xkos:specializes and xkos:generalizes represent each side of the generic relation.
• xkos:isPartOf and xkos:hasPart represent each side of the partitive relation.
• xkos:disjoint property is used to explicitly state that two given concepts do not
overlap
• xkos:causal is subdivided into the directional xkos:causes and xkos:causedBy, to
express causality
• xkos:sequential indicates that two concepts in a scheme are in a sequential relation-
ship
• xkos:succeeds and xkos:precedes are used when a sequence has a known order and
are further refined by xkos:previous and xkos:next, the immediate successor or pre-
decessor
• xkos:temporal is used when a sequence is of a temporal nature and is subdivided
into the directional xkos:before and xkos:after
5 Implementing XKOS
A first example of XKOS utilization can be found on INSEE15’s linked data site16.
The Institute published different statistical code lists and classifications there, and
notably the NAF, the French refinement of the European NACE, is one.
This publication makes use of different XKOS features, for example classification
levels, maximum-length labels and explanatory notes. This allows making useful
requests which would be more difficult, or even impossible, using only a SKOS re-
presentation. As illustration, the following SPARQL query gives the list of all the
NAF divisions, which is a very common use case for classifications.
PREFIX skos:
PREFIX xkos:
SELECT ?code ?label WHERE {
?item skos:notation ?code .
?item skos:prefLabel ?label .
?item skos:inScheme
.
?level skos:member ?item .
?level xkos:organizedBy
.
FILTER langMatches(lang(?label), 'en')
} ORDER BY ?code
A second example was created especially for this paper and is based on the interna-
tional classifications published by the United Nations Statistics Division17. We chose
15
INSEE, Institut National de la Statistique et des Études Économiques, is the French National
Statistical Institute.
16
http://rdf.insee.fr/
the ISIC (International Standard Industrial Classification), which plays a central role
in the international system of economic classifications18. The objective was to
represent in XKOS the last two revisions of the ISIC and the historical correspon-
dences between them.
The main challenge for this operation was to be able to analyze the explanatory
notes and to split them into the specific categories defined by XKOS (core or addi-
tional content, exclusions, etc.). This can be done by recognizing patterns in the note
text (“This group contains”, “This division excludes”, etc.), but, although the ISIC
notes are usually well structured, there are variations of these patterns and numerous
special cases, so that the process cannot be fully automated.For example19, the note
for division 23 combines in a single paragraph the descriptions of the core and addi-
tional contents for this division.
The ISIC is available in different formats on the UNSD web site, but for the expla-
natory notes, the only possibilities are PDF, MS Access, and the online HTML publi-
cation. Correspondences between ISIC revisions or with other classifications are also
available as HTML, or in downloadable plain text files. For the sake of simplicity and
coherence, we decided to use the HTML online publication as the reference source of
data.
As a consequence, a simple Java application was developed in order to retrieve the
data from the web site, based on the Jericho HTML parser20. Though ad hoc, this
application was designed to be modular and should be easily adaptable to other use
cases.
In a first step, which is repeated for ISIC revisions 3.1 and 4, the web pages de-
scribing the classification items are requested recursively over HTTP, starting from
the page describing the top structure, and the relevant information about classification
hierarchy, codes, labels, and notes is extracted and copied in a simple XML file. This
first step is also the occasion to make an automated categorization of the notes based
on regular expressions.
A paragraph starting with “This class also contains:” will be considered an XKOS
note of type “additional content”. Another starting with “This section includes” will
be a “core content note”, etc.Further pattern matching is then performed to verify that
two types of notes are not grouped in one paragraph (in the first example, we will
check if the text contains “exclude”, in the second we will also search if it contain
“also”). If any of these additional matches is positive, a log entry will be recorded and
manual verification will ensue.
A paragraph that does not match any predefined expressions will take the type of
its predecessor if it has one, or be categorized as “general”. Here again, the assump-
tion will be logged for further human checking, because some exceptions may occur
17
http://unstats.un.org/unsd/cr/registry/regct.asp?Lg=1
18
This system is described for example in the official NACE Rev.2 publication at
http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-07-015/EN/KS-RA-07-015-
EN.PDF, pp 13-14.
19
All reference material for the example can be accessed through the ISIC main page at
http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=27&Lg=1
20
http://jericho.htmlparser.net
(see for example Section B, where general notes can be found between the descrip-
tions of the included and excluded contents).
The code below is an extract of the XML file produced by the Java program, show-
ing a simple example of note analysis:
C X0:C 3:X
This class includes:
- manufacture of knit-
ted or crocheted wearing apparel and other made-up ar-
ticles directly into shape: pullovers, cardigans, jer-
seys, waistcoats and similar articles
- manufacture of ho-
siery, including socks, tights and panty-
hose
This class excludes:
- manufacture of knit-
ted and crocheted fabrics, see 1391
The HTML code extracted from the web pages is placed in the node. In the ISIC case, and more generally for classifications published by
the UNSD, explanatory notes are organized in
tags with a class attribute
indicating the list level, but other organizations may use different conventions.
The element gives an overall vision of the notes structure as deter-
mined by the program: in this case a “core content” (C) note is followed by an “exclu-
sion note” (X). The element values can easily be queried by XPath to
detect anomalous structures. The element completes the information by giving the start index of each part in the list:
this will be used by the following processing step.
The and elements can be edited in the XML file to
correct possible errors made by the program. As explained above, this step can be
guided by the detection of unusual sequences and bythe warnings logged by the pro-
gram. For this first experimentation, the corrections were made manually with an
XML editor, but it is easy to see how a simple GUI could be developed to improve
this step.
Once the XML files(one for ISIC Rev.4 and one for ISIC Rev.3.1) are correct, the
last step is to transform them intoXKOS representations of the two classification
schemes. XSLT transformations are the tool of choice here, and it is relatively
straightforward to design the general structure of a transformation that produces an
RDF/XML serialization of the expected result.
A much more difficult question, though, is how to transform the HTML code used
in the notes into a RDF literal: should we take only the plain text content, or should
we keep in whole or in part the note structure as it is represented in HTML?
See for example the explanatory notes for ISIC Rev.4 class 103021: the description
of the core content is organized in embedded unordered lists, and this structure is
important to understanding the description. The exclusions are shown in italics (this
can probably be left out), and include pointers to other classes (“… see 1061”) which
are not rendered as HTML links but could be desirable to capture in automated
processing oriented formats like RDF.
For the time being, we chose simplicity and put only plain text in the explanatory
notes. INSEE’s publication of the NAF usesa more refined solution developed for the
EuroVoc thesaurus [8], where the notes are typed as rdf:XMLLiteral22 and con-
tain XHTML+RDFa fragments, with links to other concepts represented through a
sub-property of dcterms:references23 (see [9] for a more complete descrip-
tion).
The structure of explanatory notes and the links that they contain are a very impor-
tant feature for statisticians, and this question of how to represent themin LOD for-
mats is clearly one of the areas where common good practice should be developed in
the statistical community.
Once the XML files corresponding to the two ISIC versions are produced, they can
be uploaded in a Sesame RDF triple store, completed by the information on corres-
pondences which is directly parsed on the UNSD web site with Jericho. The data is
then ready to be queried.Examples of interesting queries are:
PREFIX skos:
PREFIX xkos:
SELECT ?code ?label WHERE {
?class skos:inScheme
.
?class skos:notation ?code .
?class xkos:coreContentNote ?note .
FILTER regex(?note, "wholesale of office furniture")
}
This query searches ISIC Rev.4 for “wholesale of office furniture” in a core con-
tent note. It returns only the class that includes this activity (4659), whereas the
equivalent query would return two answers mixing inclusions and exclusions (4669)
if the notes were only generic SKOS scope notes.
21
http://unstats.un.org/unsd/cr/registry/regcs.asp?Cl=27&Lg=1&Co=1030
22
http://www.w3.org/TR/rdf-concepts/#section-XMLLiteral
23
See an example at http://id.insee.fr/codes/nafr2/sousClasse/27.11Z/noteExclusions
PREFIX skos:
PREFIX xkos:
xkos:
? target skos:inScheme
.
?association xkos:sourceConcept ?source
? .
?association xkos:targetConcept
xkos: ?target .
?association skos:note ?note .
FILTER regex(?note, "Repair
" of weapons")
}
This query searches the correspondences between Rev.3.1 and Rev.4 for what
w hap-
pened to the repair of weapons activity (the answer is: it moved from class 2927 to
class 3311). This is an example of query on notes concerning classification concor-
conco
dances, which is very useful for statisticians,
sta but would not be easily doablewithout
the XKOS extensions.
The figure below summarizes the overall logic of this example.
It should be noted that once the data is in XKOS format, it is easy to produce out-
ou
put formats like HTML or PDF.
PDF
6 Conclusions
We explained in this paper the rationale for XKOS and described how a simple
process could be developed to transform existing information on statistical classifica-
classific
tions in XKOS in order to be able to use this information in improved ways. This
process
ss can easily be adapted to other situations.
The paper contains a description of a Statistical Classification Systems (SCS), im-
i
portant ways they are used, limitations of SKOS in its ability to describe an SCS, and
the several ways we thought SKOS should be be extended. From the exposition, it
should be clear that the extensions account for the limitations. It is SKOS and the
extensions that we call XKOS.
Particular emphasis was placed on the ability to convey semantics, so we were
careful to add significantly more relations to XKOS. The examples in statistical of-
fices make clear the need for the additional semantics.
The work to define XKOS is not completed however. Identifying new relations is
a priority as well as building a typology of them. The biggest hurdle is to persuade
the statistical agencies to use XKOS and build a base of applications as the statistical
offices around the world move to adopt Linked Open Data principles.
Finally, we note that although XKOS was developed with the purpose of
representing statistical classifications, some elements of the vocabulary can be used
outside of the context of classifications. These new applications need further explora-
tion and possible refinement of XKOS.
Acknowledgements
The authors wish to thank the organizers of the Dagstuhl workshops – Richard Cy-
ganiak, Arofan Gregory, Wendy Thomas, and Joachim Wackerow – for their support
and encouragement in developing the XKOS ideas. The authors also wish to thank
the participants not already mentioned in the XKOS development group: Thomas
Bosh, Rob Grim,and Jannik Jensen.
References
1. Isaac, A., Summers, E.: SKOS Simple Knowledge Organization System Primer. Working
Group Note, W3C (2009), http://www.w3.org/TR/skos-primer/
2. XKOS – DDI/RDF Vocabularies, Downloaded from the Web on 19 July 2013 at
http://www.ddialliance.org/Specification/RDF#xkos
3. Sharon Lohr, Sampling: Design and Analysis, Brooks/Cole (1999)
4. Neuchâtel Terminology Model - Classification database object types and their attributes,
version 2.1, Dowloaded from the web on 19 July 2013 at
http://www1.unece.org/stat/platform/download/attachments/1431993
0/Part+I+Neuchatel_version+2_1.pdf?version=1&modificationDate=12
65695896952
5. ISO 25964-1 - Thesauri and interoperability with other Vocabularies, Part 1: Thesauri for
information retrieval, ISO (2011)
6. ISO 1087-1:2000 – Terminology work - Vocabulary, Part 1: Theory and application, ISO
(2000)
7. Cotton , F., Gillman, D., and Jaques, Y. (2013) XKOS - An RDF Vocabulary for Describ-
ing Statistical Classifications, IASSIST Quarterly (to appear)
8. EuroVoc, the EU's multilingual thesaurus,Downloaded from the Web on 19 July 2013 at
http://eurovoc.europa.eu/drupal/
9. De Smedt, J., Vatant, B.: The EUROVOC Thesaurus Ontology Schema, Downloaded from
the Web on 19 July 2013 athttp://lists.w3.org/Archives/Public/public-
esw-thes/2010Feb/att-0023/Ontology.html