Exploiting Minimal Semantics based on Naming Conventions
Pekka Aarnio, Seppo Sierla and Kari Koskinen
Department of Automation and Systems Technology, Aalto University, Otaniementie 17, Espoo, Finland
Keywords: Industrial terminology standards, Standard harmonization, Ontology matching, Concept naming rules.
Abstract: Harmonization of terminologies used in industrial standards has been widely understood to be necessary for
better interoperability of industrial information systems. Partial automation of the terminology comparison
and matching phases of this process is necessary, in order to reduce the workload of human experts.
Terminology dictionaries have been developed by various national or international organizations and for
different contexts, so their taxonomy structure and lexical content can be very different. Further, because
they cannot be considered as true ontologies, advanced ontology matching techniques are not directly
applicable. The goal of our research was to develop a lightweight element matching approach based on
structural similarities of concept names. This approach is applicable, when similar naming conventions and
rules have been applied during the development of both terminology dictionaries. In this paper, we present a
new ElemMatcher method and demonstrate its application to the harmonization of PSK standards with ISO
15926-4. PSK Standardisation is an association of Finnish industry.
Interoperability of industrial systems and
applications during the entire lifecycle of a plant is a
fundamental issue when targeting internal and
external efficiency, reliability and flexibility of
production and high quality products and services.
One of its main aspects concerns information
integration and data exchange between internal and
external applications. The benefits of better
interoperability are obvious and widely understood,
and several industrial standards have been developed
to manage these issues.
Several national and international plant model or
product model standards with overlapping context
exist, yet they use their own terminology
dictionaries, resulting in interoperability problems.
Industrial terminology (dictionary) standards
provide terminology for product model standards.
They identify and specify names of classes and
properties and meaning in definitions, descriptions
and remarks. Terminology standards are often
extended as classification standards, i.e. taxonomies.
The harmonization of several overlapping standards
as deeply as possible and selecting a wide scope
international terminology dictionary standard as a
common shared integration standard has been
proposed as a solution by the European industrial
network Orchid (CEN Orchid Roadmap).
Our research goal is to develop a lightweight
matching method ElemMatcher (EM) for industrial
cases, in which source and target terminology
standards are very dissimilar and contain very little
semantic information that could be exploited by
advanced ontology matchers. As a proof of concept,
EM was applied to the harmonization of Finnish
national PSK standards (PSK Standardisation) with
ISO 15926-4, Reference data, (ISO 15926-4). This
harmonization process has been started, following
Orchid roadmap (CEN Orchid Roadmap) guidelines,
in PSK Standardisation, which is an association of
Finnish industry closely co-operating with the
official Finnish Standards Association SFS.
The EM matching method applies structuring
rules derived from the general naming principles and
rules specified in two terminology work standards
(ISO 860:2007), (ISO 704:2000) and in a metadata
registries standard (ISO/IEC 11179-5:2010).
Terminology work standards document
principles that should be followed in the formation
of concept names. The main idea behind the EM
Aarnio P., Sierla S. and Koskinen K..
Naming Conventions.
DOI: 10.5220/0003640803900395
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 390-395
ISBN: 978-989-8425-80-5
2011 SCITEPRESS (Science and Technology Publications, Lda.)
approach has been crystallized by the transparency
principle: “a concept name (designation) is
considered transparent when the concept it
designates can be inferred, at least partially, without
a definition or an explanation. In other words, the
meaning of a name can be deduced from its parts.
(ISO 704:2000)
In the ISO/IEC 11179-5 “Naming and
identification principles” standard (ISO/IEC 11179-
5:2010), naming convention has been defined as a
set of rules for creating names and their associations.
Prescriptive conventions should be documented by
semantic, syntactic, lexical and uniqueness rules.
This standard defines also the basic general structure
and different term parts of a concept name.
The rest of the paper is organized as follows.
Section 2 presents briefly different ontology
matching techniques and related work. Section 3
describes the EM approach. The results of a
matching case are presented in Section 4 and Section
5 reports some conclusions.
A harmonization process includes a comparison and
matching phase that can be at least partially
automated. Several different methods have been
developed for entity matching originating from
database schema matching techniques. Today, state-
of-the-art technology is called ontology matching.
The goal of ontology matching is to find the
relationships between entities expressed in different
ontologies (Euzenat 2007).
Most matching approaches exploit at least
element-level lexical information and calculate
string distances of entity (concept or property)
labels. Structural methods try to extract similarity
features from hierarchy structures (taxonomies) or
from the attribute structures of concepts (product
models). In addition, advanced approaches exist that
can exploit also other kinds of common semantic
information. Language based methods can be
applied, if textual data is available. Extrinsic
methods exploit some external knowledge base (e.g.
WordNet) in order to reveal additional semantic
information concerning the entities to be matched.
Extensional methods try to find similarities between
instance data. A survey of several matching
approaches is presented in (Rahm & Bernstein,
2001), (Shvaiko & Euzenat, 2005).
Semantic techniques are perhaps the most recent
direction of matching approaches. It requires that
concept systems to be matched are true ontologies or
that semantics have been represented formally.
Examples of state-of-the-art approaches that include
semantic methods in their method suite are ASMOV
(Jean-Marya, 2009) and S-Match (Giunchiglia,
2007). S-Match has been categorized as a schema-
based semantic matching approach. It can apply
element level string-based methods, structural
methods and extrinsic methods that can exploit
external dictionaries. As a result, it is applicable not
only for ontology but also for lightweight ontology
and schema matching (Giunchiglia, et.al. 2009).
Applications of ontology matching techniques in
industry are still uncommon. One of the main
reasons for this might be that “there is no integrated
solution that is clear success, which is robust
enough to be the basis for future development, and
which is usable by non expert users” (Shvaiko 2008,
p. 1165). Furthermore, studies made by (Lauser, et.
al. 2008) reveal that the success of matching
techniques is largely case dependent.
Recent research on industrial applications of
ontology matching (Uslar & Rohjans, 2009),
(Fiorentini, et. al. 2009), (Zan, et. al. 2010) targets
industrial standards in ontology form or information
and product model standards for which ontology
matching techniques are applicable, whereas our
goal is the harmonization of industrial terminology
The EM approach exploits the minimal common
semantic information hidden in the structure of
concept names. This implicit information, when
decoded, can provide a hint of the relative position
of a concept in a generic hierarchy, which further
enables to infer possible relations between different
concepts. The EM matching process includes five
main steps:
1. Normalization
2. Quasi-synonymization
3. Equivalence matching
4. Name structuring
5. Hierarchy relationship search
3.1 Normalization
Before actual matching algorithms can be applied,
some pre-processing is needed. In this case,
hierarchy structures need to be first reduced into a
flat list of concept names followed by a
normalization phase. The purpose of the
Exploiting Minimal Semantics based on Naming Conventions
normalization phase is to transform the entity strings
into a common normal form in order to eliminate
syntactic differences between entities in different
lists. The other normalization operations applied are
a combination of those proposed in (Euzenat 2007)
and (Leukal 2006).
3.2 Quasi-synonymization
The next processing step is quasi-synonymization
(q-syn). A term word of a source name can be
replaced with its q-syn, after which this modified
copy of the name element is added into the source
list. The prefix quasi has been used, because these
common term word pairs are only potential
synonyms in an industrial context. The look-up table
of q-syn term pairs is filled beforehand in an ad-hoc
manner. For instance, this table contained the
following term pairs: maximum – upper limit; size –
diameter; operation – operating; electrical –
3.3 Equivalence Matching
The actual matching phase begins with a search of
equivalence relationships between source and target
concepts. The most obvious argument supporting
entity equivalence is the full string equality of their
names. A simple string-based matching method was
adequate in this case, since only full similarity of
strings was accepted as proof of concept
equivalence. The produced alignment set is labelled
with “A=B”.
3.4 Name Structuring
In order to find hierarchy relationships between
entity names, the EM method analyzes the inherent
structure of the names. The applied naming
conventions determine this structure. According to
the general naming rules that have been defined in
terminology work standards (see Chapter 2), the
names of class and property concepts can be
composed of more than one term word.
The most important name part is the root term
carrying the main meaning of the concept. When the
target concept is an equipment class, the root term is
of type object class term and in the case of an
equipment property concept it is of type property
term. Besides, the root term can be preceded by, one
or more qualifier terms that specialize or constrain
the basic meaning declared by the root term. In
addition, a representation term is a word, or a
combination of words, that semantically represent
the data type (value domain) of a data element.
(ISO/IEC 11179-5:2010)
One extra category has been defined for the EM
method. The names of properties can have an
additional object class term that represents a
constraint for the domain scope of that property. If
such a term is the last term in a name phrase,
separated from the root term by one of predefined
prepositions, it is categorized as a scope qualifier
term. Altogether, five term part categories are
considered by EM method:
1. object class terms
2. property terms
3. qualifier terms
4. scope terms
5. representation terms
The following list contains some examples of
concept name structuring. The first two are
equipment class names and the last two are property
names. Qualifier terms have been separated by curly
brackets and representation terms by square
brackets. A slash separates the preposition and scope
qualifiers from the root term (underlined).
1. {Tube heat} exchanger
2. {Piston} compressor
3. {Bearing inlet} pressure
/of oil
4. {Manufacturer
} [name]
3.5 Hierarchy Relationships
A necessary condition for finding any
correspondence relations is that both names under
comparison have the same root term, i.e. root term
equality. If this condition is true, the type of the
found correspondence relationship can be defined
based on the structuring information of the both
entity names. The following rules were applied (the
source and target names are assumed to be otherwise
similar, apart from the differences stated by the
If the source name has one preceding qualifier
more than the target name, the target entity is a
direct super class of the source entity (label “A<B”).
If the source name has one preceding qualifier
less than the target name, the target entity is a direct
sub-class of the source entity (label “A>B”) .
If the names differ only in their scope term
(representation term) parts, an equality with scope
(equality with representation) relationship will be
assumed (label: “As=B”, “Ar=B).
If both names have the same number of qualifier
terms, but the first terms are different, a sibling rela-
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
tionship is assumed (label “A--B”).
Figure 1 presents three different correspondence
relations that can be found for the source concept
“centrifugal pump” applying equality matching and
the simple rules above. Table 1 lists some
relationships found for property concepts using the
above rules.
Figure 1: A concept name structuring and three possible
semantic correspondence relations.
Table 1: Correspondence relations between properties.
At the beginning of the harmonization process, two
PSK standards were selected to be harmonized with
the initial set of ISO 15926-4 Reference Data
(2010). The first of them, PSK5965 Equipment
Classes and Subclasses, (321) was matched against
the equipment class sets of ISO 15926-4 (5465) and
the second standard, PSK5980 Data Element
Dictionary, (353properties) against the property sets
of ISO 15926-4 (1986).
The source and target entity sets have many
differences. ISO 15926-4 (target set) contains in
total ten times more entities than the PSK standards,
and those entities have been categorized into several
context collections. The taxonomy structure is also
different. ISO 15926-4 has a deep generalization
hierarchy at least up to eight levels, whereas the
number of levels in the PSK5965 standard has been
limited to two (main class – subclass). Furthermore,
only ISO 15926-4 includes textual descriptions for
equipment classes and properties.
4.1 Comparing Alignment Results
The matching tasks were first carried out using the
EM tool. In addition, the S-Match tool (S-M) was
used for the same tasks, in order to evaluate the
capabilities of an extrinsic method using general
language WordNet dictionary (WordNet) and a
structural method in this special case. The S-M
approach applies a name structuring algorithm, in
which it represents a concept node as a logical
expression of the meanings of its term words. The
local meaning of an atomic concept is its natural
language meaning defined in WordNet (Giunchiglia,
et. al. 2007).
Figure 2: Number of PSK equipment classes (equip) and
properties (prop) for which an equality match with ISO
15926-4 was found by EM and S-M tools.
4.1.1 Equality Relations
Figure 2 presents the number of source (PSK)
concepts that were found to have an equality (eq)
relation with at least one target (ISO) concept. It
indicates that all matches (100;39) found by the EM
tool were correct ones, i.e. true positives (dark and
light green solid). S-M tool found only a few more
correct matches (compared with the EM method
without q-syn step) by browsing WordNet synsets;
in total for (89;36) PSK entities.
However, the S-M tool found also several false
positives. Post-analysis of the results revealed that
about one half of those mismatches were actually
close synonyms (blue texture dashed) in a natural
language context (WordNet), but cannot be
considered equal in an industrial context.
Furthermore, there were a large set of source
classes (40;6) for which S-M found matches that
were clearly incorrect (white dashed). Some of these
false positive results are related to the internal
representation of the concepts as logical expressions.
The order of the term words in a multiword concept
does not have any impact on interpretation of this
expression. In contrast, the algorithm used in EM
takes into account the order of the term words in a
concept name so that this kind of mismatch is not
PSK:Property Rel. ISO:Property
Diameterofdrum As=B Diameter
Frequencyofvibrator As=B Frequency
Bearinginletpressureofoil As<B Inletpressure
Manufacturer A=Br Manufacturername
EMEquip SMEguip EMProp SMProp
Exploiting Minimal Semantics based on Naming Conventions
4.1.2 Matching Quality Measures
Matching quality can be indicated by quality
measures. The discovered alignment sets are
compared against a reference set containing all the
correct correspondence relations. In this case, the
reference set is the final refined matching set
produced by the industrial expert group. The
matching correctness measure is precision, defined
as the percentage of discovered correct
correspondences in the entire extracted alignment
set. The completeness measure of matching is recall,
defined as the percentage of discovered correct
correspondences in the reference alignment set. The
F-measure is computed as a harmonic mean of
precision and recall. All these measures vary in the
[0,1] range. (Euzenat, 2007).
Figure 3: Matching quality measures of the equality
alignment sets produced by EM method (EM-A), EM
method without q-syn (EM-B) and S-M method (S-M).
These quality measures for equality alignment
sets are presented in Figure 3. The results of S-M
tool should be compared with the EM results
without the ad-hoc q-syn step (EM-B). This
comparison reveals that the recall measures (middle
bar) are almost the same (0.65; 0.67), but the
precision (left bar) of the S-M method is much lower
(1.0; 0.38).
A separate test case was generated for evaluating
the feasibility of a structural method for a case, in
which the hierarchy structures are very different.
The S-M tool was used in matching the source file
(PSK5965) with one of the target category files (ISO
Rotating), both in their full hierarchy format. The
quality measures of this test case (S-M Hier) are
presented in Figure 4, together with flat structure test
cases (EM B, S-M Flat). These results indicate that
the extra structural information did not provide any
advantage in this special case. On the contrary, the
recall measure was notably decreased.
Figure 4: Matching quality measures of the equality
alignment sets produced by EM method (EM-B), S-M
method with flat hierarchy (S-M flat) and S-M method
with full hierarchy (S-M Hier).
4.1.3 Super Class and Subclass Relations
The primary purpose of the EM name structuring
algorithm is to find hierarchy correspondence
relations with good precision. These relations
include direct super class (A<B), direct sub-class
(A>B) and sibling class (A- -B) relations. The
following Table 2 lists the total number of some of
these correspondence relations (alignments).
Table 2: Total numbers of alignments found by the EM
method and S-M method.
The results of the EM tool are compared with
those produced by S-M tool. The S-M approach
deduces semantic relations between entities by
logical inference. Therefore, these relations are:
equivalence (A=B), more general (A<<<B), more
specific (A>>>B) and disjoint () (Giunchiglia,
et.al., 2007).
These alignment sets have not been fully
checked for validity by the industrial expert group.
However, a partial analysis indicated that the spot
checked alignments found by EM are true positives,
whereas S-M has also found many clearly incorrect
ones. The main reason for the high precision of the
EM alignment sets is due to the fact that the name
structuring rules comply with the naming
conventions applied during terminology
PSK5965‐ ISO159264
PSK5965‐ ISORotating
F0.5‐ meas.
A=B A<B A>B A‐‐B
ElemMatch 100 174 361 922
A=B A<<<B A>>>B
SMatch 226 2651 8281
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
The harmonization of terminologies used in
industrial standards has been widely understood to
be necessary for better interoperability of industrial
information systems. Partial automation of the
comparison and matching phases of the
harmonization process is considered necessary, in
order to reduce the workload of human experts and
to speed up the process. However, advanced
ontology matching methods are not directly
applicable, because terminology dictionaries are not
true ontologies and may differ greatly in their
taxonomy structure and lexical content. We have
developed a lightweight element level matching
approach to address this problem. It is based on
general concept name structuring rules defined in
terminology work standards. This approach is
applicable, when similar naming conventions have
been applied.
This ElemMatcher approach was applied to an
industrial terminology matching case in the first
phase of the PSK - ISO 15926-4 harmonization
process. The matching results indicate high
matching precision for the equality alignment set
and good precision of the other alignment sets.
Additional experiments using advanced structural
and extrinsic methods that exploit only general
purpose dictionaries showed that no advantage was
gained in this case study of industrial terminology
standards harmonization.
CEN Orchid Roadmap – Standardising information in the
plant engineering supply chain. Parts 1-3. [referenced
2011-04-15]. Available at: http://www.cen.eu/CEN/
sectors/sectors/isss/workshops/Pages/workshop orchi
Euzenat, J. Shvaiko, P., 2007. Ontology Alignment.
Springer. ISBN 978-3-540-49611-3.
Fiorentini, X., Rachuri, S., Ray, S., Sriram, R., 2009.
Towards a method for harmonizing information
standards. 5th Annual IEEE Conference on
Automation Science and Engineering. Bangalore,
India, August 22-25, 2009.
Giunchiglia, F., Yatskevich, M., Shvaiko, P., 2007.
Semantic Matching: Algorithms and Implementation.
Technical Report # DIT-07-001, Department of
Information and communication Technology,
University of Trento.
Giunchiglia, F., Dutta, B., and Maltese, V., 2009. Faceted
Lightweight Ontologies. Conceptual Modeling:
Foundations and Applications. Alex Borgida, Vinay
Chaudhri, Paolo Giorgini, Eric Yu (Eds.) LNCS,Vol.
5600, Springer, pp 36-51.
ISO 704:2000,Terminology work — Principles and
methods. [referenced 2011-04-15]. Available at:
ISO 860:2007 Terminology work – Harmonization of
concepts and terms. [referenced 2011-04-15].
Available at: http://www.iso.org/iso/store.htm
ISO 15926:2007, Industrial automation systems and
integration — Integration of life-cycle data for process
plants including oil and gas production facilities.
[referenced 2011-04-15]. Available at: http://www.iso.
ISO 15926-4:2010, Industrial automation systems and
integration — Integration of life-cycle data for process
plants including oil and gas production facilities - Part
4: Initial reference data. [referenced 2011-04-15]. (ed1
files available at: http://ng.tc184-sc4.org/index.cfm?
ISO/IEC 11179-5:2010 Information technology -
Metadata registries. Part 5: Naming and identification
principles. [referenced 2011-04-15]. Available at:
Jean-Marya, Y., Shironoshitaa, E., Kabuka, M., 2009.
Ontology matching with semantic verication. Web
Semantics: Science, Services and Agents on the
WorldWideWeb 7. Elsevier. 235–251
Lauser, B., et al., (2008), ‘Comparing human and
automatic thesaurus mapping approaches in the
agricultural domain’. In Proceedings of the 2008
International Conference on Dublin Core and
Metadata Applications, pp. 43–53.
Leukel, J., 2006. Controlling Property Growth in Product
Classification Schemes: A data management
Approach. Enterprise Information Systems: 8th
International Conference, ICEIS 2006.
PSK Standardisation. [website]. [referenced 2011-04-15].
Available at: http://www.psk-standardisointi.fi/
Rahm, E., Bernstein, P., 2001. A survey of approaches to
automatic schema matching. T.he VLDB Journal 10,
Springer. pp. 334–350
Shvaiko, P., Euzenat, J. 2005. A Survey of Schema-Based
Matching Approaches LNCS 3730, Springer. pp. 146–
Shvaiko, P., Euzenat, J., 2008. Ten Challenges for
Ontology Matching. LNCS 5332, Springer. pp. 1164–
Uslar, M., Rohjans, S., 2009. Ontology-based Integration
of the Heterogeneous Standards IEC 61970 and
61850. In: Proceedings of International ETG-
Kongress 2009. VDE Verlag Gmbh. Paper 1.59.
WordNet - A Lexical Database for English. Princeton
University. [referenced 2011-04-15] http://wordnet.
Zhan, P., Jayaram, U., Kim, O., Zhu, L., 2010. Knowledge
Representation and Ontology Mapping Methods for
Product Data in Engineering Applications. Journal of
Computing and Information Science in Engineering.
June 2010, Vol. 10.
Exploiting Minimal Semantics based on Naming Conventions