Dichev, Christo, Darina Dicheva, Boriana Ditcheva and Mike Moran. “Translation between RDF and Topic Maps: Divide and Translate.” Presented at Balisage: The Markup Conference 2008, Montréal, Canada, August 12 - 15, 2008. In Proceedings of Balisage: The Markup Conference 2008. Balisage Series on Markup Technologies, vol. 1 (2008). https://doi.org/10.4242/BalisageVol1.Dichev01.
Balisage: The Markup Conference 2008 August 12 - 15, 2008
Balisage Paper: Translation between RDF and Topic Maps: Divide and Translate
This paper addresses the issue of
sharing and integrating data across RDF and
Topic Map representations. The novel
aspect of tackling the RDF - Topic Maps
interoperability
problem is the attempt to identify the
right balance between the following key aspects:
(i) semantics-preserving data translation;
(ii) completeness of the translation; (iii)
pragmatics
and usability of the translation. The
proposed strategy towards achieving this goal is
based on
exploiting the ontological correspondence
between RDF and Topic Maps. The design focus is
placed
on a translation respecting the meaning
and the readability of the RDF - Topic Maps
translation.
The paper analyzes the feasibility of the
interoperability task, presents some requirements
derived
from this analysis, and proposes a method
for RDF - Topic Maps translation. The proposed
method is
implemented as a plug-in of the TM4L topic maps editor.
Data availability is no longer a
major problem given the sheer volume of
information on the web
and the advances in information retrieval
and indexing technology. The lack of
interoperability at the
model, metadata, and data levels presents
the major barrier to knowledge and data sharing
[Stuc02].
Many-to-many data-interchange media, such
as the web, pose new requirements for sharing and
exchange of
data. One of the most challenging among
them is the ability to make use of information
outside of its
structural origin. This requirement
implies data interchange not only on a syntactic,
but also on a
semantic and structural level.
The presented work was motivated by
the idea of creating a tool that allows effective
reuse and
integration of existing Topic Maps (TM)
and RDF data. Similarly to previous efforts in
this direction
[Lach01], [Moore01], [Ogie01],
[Cian03],
[Gars03], [Pepp06a], we aim at enhancing the
interoperability between
the two models by providing
translation bridging the two frameworks,
instead of forcing applications “to speak the
same language”.
The aim is similar but the proposed
approach is different. In previous works, the
mapping strategies
are guided by the observed equivalence
between the Topic Maps subject and RDF resource
concepts. Such a
strategy implies some sort of
subject-centered translation. In contrast, our
approach is based on the
ontological correspondence between RDF and
Topic Maps. Diverse and interesting similarities
can be
captured based on correspondences between
the ways we relate things rather than on the
correspondences
between things alone. Therefore, our
approach implies identifying similarities not
only between concepts,
but also between how statements about
those concepts are asserted. Such similarities
make the translation
between the two models more predictable.
This is a principal point in our translation
approach, which
proposes to divide the concepts to be
translated into overlapping and non-overlapping
categories and
then to conquer them by translating, with
a correspondingly focused and general
translation.
This type of work is related to
ontology and metadata mapping [Kalf03],[Klein01],
[Patel05],[Shvai05]. Metadata mapping,
in particular, involves an identification
of equivalent or nearly equivalent metadata
elements within
different metadata schemas. In contrast to
metadata mapping, translation between
independently developed
RDF and Topic Maps data can not be
accomplished by mapping between two vocabularies.
The mapping mechanism
is more complex because it involves mapping
between frameworks with extensible vocabularies
implying that
one-to-one correspondence is not generally possible.
A critical question regarding this type
of translation is whether all concepts of the
source language
can be appropriately translated to some in
the target language. Assuming that a complete
translation is
possible, a derived question is whether all
concepts from the source language should be
translated to the
target language at any price. At first, it
seems that complete and accurate translation from
the source to
the target language (if possible) should be
preferable to the translation skipping or
transforming the
meaning of some concepts. However, the
answer, in fact, depends on the purpose of the
translation and on
the selected criteria. The translation as a
process and objective involves two aspects:
semantic and
pragmatic [Usch03]. They
correspond to the following questions: Is a
concept from the source language expressible
in the target language? Is a concept
translated from the source language useful in the
target language?
The technical and pragmatic aspects of
the translation reflected by the above questions
are equally
important. They also imply a variable
strategy. Instead of a complete and faithful
translation in some cases
it might be better to chose a translation
preserving the meaning of some concepts and
modifying (slightly)
the meaning of some other in order to match
them to predefined concepts in the target
language. The translation
strategy of the source concepts should not
be independent from their role in the target. For
example, extending
a language to accommodate a foreign concept
is a challenging task from a usability
perspective. The following
observations indicate that the set of
concepts satisfying the first question is not
necessarily equivalent to
the set of concepts satisfying the second one:
Translation of conceptual
structures from the source model, even when
preserving their meaning
(e.g. a TM binary association translated
into seven RDF statements), is not very useful if
translated
structures are not recognizable to the
agents using the target model.
Translating a conceptual
structure from the source language that does not
have a counterpart in
the target language (e.g. TM variant
names), may result in translated concepts which
are meaningless and
are disregarded by the agents using the
target language.
In trying to enhance the RDF - Topic
Maps conversion we focus on identifying the right
balance between
the following key aspects: (i)
semantics-preserving translation; (ii)
completeness of the translation;
(iii) pragmatics and usability of the
translation. In contrast to the previous works on
RDF - Topic Maps
mapping, preserving the ontological
vocabulary is not a required property of our
translation approach.
Instead, the basic requirements are guided
by pragmatic and semantic considerations. As a
result, the focus
is on a translation respecting the meaning,
the utility and the readability of the translated
data.
This paper addresses the problem of RDF
- Topic Maps interoperability by analyzing the
feasibility of
this task and identifying some requirements
derived from that analysis. Based on the results
of the analysis
we propose a method for RDF - Topic Maps
(RDF2TM) translation. In contrast to metadata
mapping, a general
RDF2TM mapping goes beyond the simple data
interoperability, since it involves exchange of
conceptual knowledge
within and across the corresponding Topic
Maps and RDF models. The paper starts with a
conceptual discussion
of problems in RDF - Topic Maps
interoperability, which motivated the design
requirements of our translation.
Section 3 discuses the technical details of
the proposed approach, while the implementation
and examples are
presented in section 4. We summarize the
related works and tools addressing RDF2TM
translation in section 5
and conclude in section 6.
Semantic and Pragmatic Aspects of RDF
- Topic Maps Interoperability
Terminology
The term translation means
changing the representation
formalism while preserving the semantics.
The term transformation means
changing the representation
as well as the semantics.
Mapping means relating
similar concepts or relations from
different sources to each other by an
equivalence relation. A mapping results in a
virtual integration.
The term interoperability means
ensuring information
exchanged between two models that is
understandable in the manner intended by the
original creator.
Semantic interoperability
is the ability of systems to share
and understand information at the level of
formally defined and mutually accepted domain
concepts, enabling
machine-processable interpretation.
The terms model, data model,
language, and
format are
used interchangeably in combination with the
terms RDF and
Topic Maps (e.g. RDF model, RDF format).
When there is no danger of
confusion, the phrase RDF - Topic Maps
translation/mapping (RDF2TM)
will be used to mean RDF to Topic Maps and
Topic Maps to RDF
translation/mapping.
RDF
Schema (RDFS) extends RDF with
“schema vocabulary”.
It provides the framework to describe
application-specific classes and properties. The
proposed translation
uses RDFS for some Topic Maps constructs.
We choose the term “RDF” (in the title) based
on some traditional
associations and because a document that
uses terms from the RDFS vocabulary is still an
RDF document. In
the following the acronym RDF(S) will be
used to denote both RDF and
RDFS.
RDF and Topic Maps interoperability:
a conceptual viewpoint
As with natural languages, translating
RDF constructs into corresponding Topic Maps
constructs and
structures requires interpretation and may
involve some loss or distortion of meaning. The
interoperability
between RDF and Topic Maps languages has
been addressed by various authors[Cian03],
[Gars03], [Lach01],[Moore01],[Ogie01],
[Ogie01], with
a focus primarily of the syntactic and
semantic level; less attention has been paid to
the pragmatic and
practical aspects of the translation. In
contrast to the previous works on RDF2TM mapping,
vocabulary and
structure preservation is not the top
priority of our translation. Our basic
requirements are guided by
pragmatic and semantic considerations.
We distinguish differences between
ontology languages from differences between the
ontologies themselves.
The latter can further be divided into
differences among the things that are described
and differences in
the way things are described. The problems
associated with the semantic interoperability
between RDF and
Topic Maps are mostly due to the differences
in their original assumption of how the world is
modeled. These
differences are passed to the corresponding
modeling frameworks, which directly affects the
syntax and the
descriptive capability of each. It also
affects the mapping between the two models. The
fact that RDF and
Topic Maps differ in representation,
structure, and vocabulary leads to a great
multiplicity of matching
alternatives. In general, an RDF2TM mapping
process can not be defined by one-to-one matching
relationships
between the source and target concepts.
Typically it involves one-to-many, many-to-one,
and one-to-none
relationships. This type of matching
ambiguity may turn into semantic inconsistency
that is not resolvable
without specific domain information. Such
inconsistencies occur because the source model:
(i) differs in
compositional granularity from the target,
(ii) has extensible vocabulary along with the
target and
(iii) contains concepts that are irrelevant
according to the target ontological vocabulary.
These observations are critical in the
context of RDF — Topic Maps interoperability.
We can illustrate
the key points in comparison with
conventional programming languages. First
consider the translation of the
FORTRAN loop statements
DO 10 I = 1, 100
STATEMENT1
…..
STATEMENTn
10 CONTINUE
into the following C++ statement.
for(int i = 1; i < 101; i++)
{
statement1
…..
statementn
}
It is straightforward, because the loop
vocabulary (keywords) is predetermined and it
does not depend
on the intention of the user. The FORTRAN
loop statement DO 10 I = 1,
100
doesn’t allow variations, e.g.
PERFORM 10 I = 1, 100.
Similarly, C++ does
not support extensibility with non standard
control structures and for loop is defined in the
language. Any
change in the C++ loop vocabulary
for(int i = 1; i < 101;
i++) e.g.
into trough(int i = 1; i
< 101; i++) will violate
the syntax rules.
This results into one-to-one matching
between the two loop statements with fixed
semantics.
Now consider a similar task in our
original context: translating the following Topic
Maps binary
association [1]:
with verbalization: Lecture7 is based on the topic
Xpointer, where
Lecture7
plays a role of adaptation and
Xpointer
plays a role of source.
Can we translate this association into a
single RDF property
ex:lecture7 ext:based-on ex:Xpointer
with corresponding verbalization
Lecture 7 is based on
Xpointer?
The answer is not obvious at a syntactic
level. Firstly, the naming (vocabulary) in the
TM association
based-on(lecture7:adaptation, Xpointer : source)
is left to the user’s choice, as
opposed to the first example. For instance, it is
possible to
assert Topic Map association with different name, e.g.
but with an equivalent meaning to the
first one. More interestingly, Topic Map
associations differ
in compositional granularity from the RDF
properties. Their expression involves concepts
such as association
type and role types coupled with
role-playing topics. The Role player: Role type
constructs of the Topic Maps association do
not have a matching counterpart in the RDF
properties (i.e.
one-to-none matching). In such cases there
are three alternatives: to disregard all role
players in the
translation, to translate them into a
predefined RDF construct with a similar meaning
or to perform a
semantic preserving translation into RDF
construct intended to serve as an RDF encoding of
the TM role
types and role players. Even if we decide to
skip the Topic Maps role types translation, we
still have
to decide how to relate role players to the
subject and object of the RDF based-on property.
This will
transform the directionally neutral Topic
Maps association into uni-directional RDF
properties. A closer
look at Topic Maps association
specifications reveals that both role players
must be topics (they cannot be
strings) while the object of the RDF
based-on property can be any resource. For
example, in one RDF document
the object of the ext:based-on property may
refer to a web page describing Xpointer (single
resource)
while in another RDF document the object of
the ext:based-on property may refer to the
Xpointer concept
with a reference to book chapter divided
into sections by relevant RDF properties
(structure of resources).
Thus significant amount of the information
carried by the association
will be lost if we choose to translate
it directly into the RDF
statement
ex:lecture7 ext:based-on ex:Xpointer
is In fact such a process will be a
transformation
of a Topic Maps
association into an RDF property, since it
is changing the representation as well as the
semantics.
One point that was left implicit in the
above example is that a complete semantic mapping
is not
always the best alternative. For example, if
the Superclass-Subclass
association
is mapped directly to the RDF(S)
subClassOf property:
ex:Professor rdfs:subClassOf ex:Person
where the player of type superclass is mapped to
the object and the player
of type subclass is mapped to the
subject of the
subClassOf
property, then the role types (viz.
superclass
and subclass)
will be lost.
If we choose to ignore such a loss, the
compensation will be a direct use of the RDF
subClassOf
property with predefined semantics that captures
the
intended meaning of the Topic Maps Superclass-Subclass
association. Similar
arguments also apply to user defined
associations/properties such as based-on. However
the Topic Maps and
RDF extensibility makes finding a user
defined property with equivalent semantics a
challenging task.
In contrast, a translation of source
content, which includes concepts that do not
exist in the target
model, may result in a redundant set of
data, a set that remains unused by the agents
using the target
representation. Indeed, we can translate a
Topic Maps binary association with role types and
role players
into RDF by mapping it into a composition of
RDF properties that preserve the role concept.
Thus, it is
possible to define a translation that
captures the semantics of a TM association.
However, the interpretation
of the corresponding RDF composition
embodying the role concept is conveyed by an
external agreement for RDF.
More importantly, semantic consistency does
not imply intentional consistency. An agent
reading the target
may be unable to grasp the role concepts as
intended in the source. As a result, the original
intention
assigned to association roles may be lost
despite their faithful translation. To make this
example more
specific, consider a user-defined Topic Maps
association, employment,
involving two roles, employer and employee.
The two roles can be translated into
matching RDF constructs participating in the
translated
employment
association. For example, for each role in the
employment
association we can include a statement
whose property corresponds to the role type
(viz. employer
and
employee);
its value is a property with predicate
member that
references the role player, where referencing is
done
through an additional (e.g. referencedBy ) property.
Obviously, this will increase the complexity
of the resulted translation while
decreasing its readability. More
importantly, despite their presence in the
resulting RDF encoding, the
constructs corresponding to the employer and
employee roles may remain unused because they
correspond to
concepts defined outside of RDF - the
existing RDF applications are designed with no
assumptions relating
to query, search, display, etc., association roles.
It is even more challenging to
pragmatically integrate the TM contextual roles
(e.g. when a
person playing a role of husband in one context is
playing a role of
father in
another context) in an RDF setting, ensuring
their practical
utilization. When a topic map user applies a
scope to a particular statement, he does that
with certain
intent. The scope concept with meaning
provided by the Topic Maps data model is
designated to capture this
intention. Since there is no corresponding
term (and meaning) in the RDF vocabulary, this
intention can not
be passed to the target. A possible approach
for overcoming such problems is to extend the RDF
vocabulary
with terms for naming given RDF compositions
that correspond to particular Topic Maps concepts
(e.g. rdf:scope). However, we
believe that we should not target a translation
that enforces or assumes that RDF users and
applications should “speak Topic Maps”.
Translating in the opposite direction,
from RDF to Topic Maps, demonstrates slightly
different problems
[Gars03].
Consider, for example, a general RDF
property, relating two resources identified by
their URIs. It can be
mapped to either a TM binary association or
a TM occurrence. The correct mapping of the RDF
property to a
Topic Maps format depends on the intended
meaning of the property (e.g. employment
vs. seeAlso). On the other
hand, properties such as
rdf:type or
rdfs:label allow
us to map
them unambiguously to Topic Maps
expressions. The mapping in this particular case
is enabled by the existing
rdf:type
and rdfs:label
semantics
in the source language. However,
non-standard properties, such as rdf:is-described-by,
rdf:plays
or, rdf:exists,
do not carry predefined semantics and thus
can not be mapped based on any logical ground.
RDF does not make
any assumption about their meaning except
that they relate two resources - one playing a
role of subject and
the other a role of object. In this case a
semantic mapping to Topic Maps terms would
require a significant
human involvement. If the property rdf:is-described-by
denotes a relationship
with an information resource that is
pertinent to and enlightens a given subject, then
this property should be
translated to a TM occurrence, otherwise it
should be translated to a TM binary association.
Note that a
complete and accurate mapping will
require additional decisions to be made
about the occurrence type and the role types of
the association in
each of the corresponding decision steps. In
a situation like that, human involvement can not
be eliminated
because a correct property translation will
require an understanding of the context of the
property, so its
intended meaning and the resources it
involves can be captured. These distinctive
features of the two models
and the differences in the compositional
granularity result in a mapping ambiguity and the
need of an
additional vocabulary, such as RTM [Gars03], [Ontop03],
for explicitly
defining external meaning and intentions.
Design Requirements
Based on the above observations, we
focus on a translation that respects the meaning
while maximizing
the usability of the translation result. To
achieve this we exploit the ontological
similarities between the
models, which also allow natural integration
of the translated expressions into the target
formalism. As an
additional requirement, we chose not to
support guided translation [Gars03],
[Pepp06b], [Pepp06c]. Thus, we assume that the
source model is not annotated with specific
information for the purpose of guiding the
translation. This kind
of annotation presumes familiarity with both
models. It is typically done manually and is a
costly exercise.
A further assumption is that sharing
vocabularies across the two models is not
required. Our opinion is that
the purpose of any translation is to make
translated concepts interpretable in terms of the
native vocabulary
and to allow users not knowing a foreign
vocabulary to utilize it. In fact, the primary
role of the translation
is to make users independent of a foreign
vocabulary. The above assumptions served as a
starting point for
identifying the key requirements of the translation.
Origins of the requirements
The basic idea driving the proposed
translation requirements is that RDF and Topic
Maps represent two models
that overlap to a certain extent in their
modeling capabilities. This overlapping is at an
ontological level.
It refers to similarities in the way things
are organized, e.g. similarities in the way
things are grouped into
classes and the way things are related to
each other, to properties or to pertinent
information resources in
both models. Any strategy for dealing with
overlapping concepts implies a decision about
what to do with
non-overlapping concepts. However, the
intuition is that of the total amount of
available RDF and Topic Maps
data the portion that
is represented with non-overlapping conceptual
structures is
insignificant compared to the potentially
sharable information.
The examples discussed in section 2.2
illustrate some translation problems inherent for
extensible
frameworks such as RDF and Topic Maps. In
such data models the vocabulary is not fixed, but
is open for
defining new terms for particular
application domains. A further complication is
that when the data models
differ in compositional granularity, the
recognition of coarser from finer structures in
general can not
be done at a syntax level.
On the other hand, there are a number
of initiatives, such as Dublin Core [Beck02],
[DCMI06], FOAF, etc., that
provide fixed vocabularies with well defined
meanings. Another trend in the Semantic Web is
the rise of data
that commits to simple ontologies, like SKOS
[Miles05], OBO and BIO. There are
also a number
of predefined properties
and classes in RDF, as well as associations
in Topic Maps. The predefined constructs provide
necessary
information for accurate mapping. Since a
full automatic translation between RDF and Topic
Maps is not
feasible in general, one possibility is to
provide specialized translations among common
ontologies/schemas.
Here we can draw an analogy with natural
languages, where there is a variety of
specialized language
translations, such as in the technical,
medical, legal, and financial areas. This
analogy suggests that
a certain accuracy of the translation can be
achieved with a specialized translation. Such
domain-specific
RDF2TM translations could cover a
significant portion of the RDF and Topic Maps
data on the Web. The
remaining (smaller) portion will be the
subject of generic translation.
A set of overlapping concepts in RDF
and Topic Maps is given in Table 1. Another
example is the RDF
dialect for the Dublin Core Metadata
Initiative (DCMI) and the corresponding dialect
under development from
the Topic Maps community [Dich06],[Maic06],
[Pepp07a].
A useful set of overlapping concepts can be identified between the TM4L
Topic Maps dialect [Dich07],
[Dich06] and SKOS. Obviously,
such overlaps
with one-to-one correspondence enable a
straightforward semantics-preserving
translation and allow round tripping. Since the
overlapping concepts
allow translation with features matching the
requirements, they are made the subject of a
special (focused)
translation.
Table I
A set of overlapping concepts from the RDF and Topic Maps predefined vocabularies
RDF
Topic Maps
resource
Topic
type
Class-Instance association
subClassOf
Superclass-Subclass association
seeAlso
Occurrence
label
Name
We extend the set of overlapping
concepts to include TM binary associations and
RDF properties.
To be consistent with the description of
overlapping concepts, we restrict the Topic Maps
binary associations
to associations with all-scopes. As for
RDF, we assume that the properties relating the
subjects to the
relevant information resources
(corresponding to Topic Maps occurrences), are
identifiable from the general
properties. These restrictions may seem too
limiting for potential applications. However, our
analysis
indicates that in most applications, RDF is
used as metadata infrastructure. When the RDF
properties are
used to relate resources analogous to Topic
Maps occurrences, they use Dublin Core
(e.g. dc:Relation or dc:Source) or, for
educational applications, the LOM
vocabulary, which makes them identifiable. The
part of non identifiable
cases, then, is not so significant.
Requirements
The following is a list of requirements
guiding the proposed translation between RDF and
Topic Maps.
The data translated from the
source language must allow merging without
syntactic
inconsistencies with the data in the target language.
The translation must cover the
overlapping concepts of the two models,
minimizing the
use of concepts beyond the shared
ontological boundaries.
The translation must not change
the original meaning of the overlapping concepts.
The translation must respect
pragmatic requirements, meaning that the
translated data
is understood as intended by the agents using
the target.
The translation must be faithful
to the existing RDF(S) core classes and
properties, to the
TM core types and to the metadata standards
that have pre-existing RDF representation.
For non overlapping concepts
preference should be given to transformation into
similar
predefined concepts when possible.
Queries written against one
model must be usable with data translated from
the other.
The translation between the two
models is supposed to be unguided, that is,
no annotation for guiding the translation is assumed.
Round-tripping should be possible.
The rationale behind the second and
sixth requirements is based on our intent to
support the co-existence
of RDF and Topic Maps by enabling them to
interoperate without the need of a centralized
agreement on new
vocabulary terms or how to interpret a
translation of a particular construct. The
adopted strategy aims to make
the two models interoperate without
introducing concepts with external meaning (if
not necessary) to any of the
models. This eliminates any need of
pre-existing agreement (between the two
communities) on the interpretation
of the translated data. Such a strategy
favors exchange via data translation carrying no
external meaning and
which is faithful to the basic vocabularies of both models.
As we aim for a translation process
providing a reversible translation between the
overlapping concepts
of the two models, we need to define the
formal meaning of reversible translation.
By reversible translation we mean that
a translation of an RDF (TM) dataset to a TM
(RDF) dataset
followed by a reverse translation of the
resulting dataset to an RDF (TM) data will yield
the original
RDF (TM) dataset.
Definition. Let T1 be translation of an RDF
dataset ARDF
to a Topic Maps dataset
ATM.
Let T2
be a reverse
translation of ATM
to a dataset
ARDF.
The translations
T1 and
T2
are said to be reversible iff for any
dataset translated by T1
(T2)
the translation by T2
(T1)
of the output from T1
(T2)
generates the original dataset. That is,
T2(T1(ARDF))
≡ ARDF
and T1(T2(ATM))
≡ ATM.
Integrating Generic with Focused Translation
The purpose of defining overlapping
concepts is to enable an RDF2TM translation that
integrates generic
with focused translation in a single system.
The focused component of the translation is
intended to deal
with the constructs identified as
“overlapping concepts”. This also includes
metadata vocabularies expressed
in both RDF and Topic Maps in accordance
with recognized standards. For such constructs
the translation is
supposed to yield a semantics-preserving and
reversible translation (also referred sometimes
as “overlapping
translation”). The remaining constructs
are subject to generic translation. We claim that
such an approach
will result in a translation with an increased pragmatic value.
A significant portion of the
information in the Semantic Web comes in three
forms: metadata, taxonomies,
and lightweight ontologies. (Some authors
argue that most of the Semantic Web is metadata
[Usch03].) This means
that Superclass-Subclass,
Class-Instance, and Dublin Core
metadata
form a vocabulary with a key role in the
Semantic Web. If we enable a lossless and
faithful-to-the-original-intention
translation even only for the data expressed by
this “core vocabulary”,
that will already be a contribution to the RDF-TM interoperability.
Focused (overlapping) translation
Since the separation between
overlapping and non-overlapping concepts is based
on syntactic distinctions,
the differentiation of their translation
does not significantly increase the level of
complexity. The following
strategy for translating RDF properties into
Topic Maps associations can be used as an
illustration of this
approach.
The RDF type property is converted
to
Class-Instance
association, where the subjects of the property
aremapped to the
instance
members of the association and the objects of the
type
property are mapped to the corresponding typing
topics. The RDF(S)
subClassOf property is converted to
Superclass-Subclass
association, where the subjects of the property
are mapped
to the subclass association
members and the objects of the
subClassOf
property are mapped to the superclass
association members. RDFS label
properties are converted to Topic names (baseName).
The predefined RDF(S) properties seeAlso are converted to
Topic Maps occurrences, where the subject of
the property is converted to a topic and the
object of the property
to the topic occurrence. More specifically,
the object of the seeAlso property
is converted to an external occurrence of
type Information
Resource. The remaining
RDF properties (describing user defined
structures) are subject to generic translation.
They are converted
into a Topic Maps binary association with
two role types: subject and
object,
mirroring the subject and the object of the
corresponding RDF property.
The type of the association corresponds to
the predicate of the property.
Going in the reverse direction is
similar. All TM Class-Instance associations
are converted to an RDF type property, where the
topic instances are mapped to the
subjects and the typing topics of the
associations are mapped to the objects of the
type
property. Superclass-Subclass
associations are converted to a subClassOf property, where
the players of
type superclass are mapped to
the objects and the players of type
subclass
are mapped to the subjects of the
subClassOf property.
The remaining binary associations are also
subject to focused translation. The association
assoc(player1:Role1,
player2:Role2) is translated to RDF(S)
by creating a property
assoc with
domain Role1
(Role2) and range
Role2
(Role1). Note that the choice of
which association role types to
map to the domain and which one to the range
of the property is arbitrary. The lack of
predetermined mapping
reflects the fact that, the TM role types
play a neutral role in the associations and any
decision of how to
map them to the domain and range will have
equal effect on the translation. For example, one
possible
translation of the association employment(Christo:employee,
WSSU:employer)
into an RDF property may result in an RDF
property employment with domain
employee
and range employer.
This translation generates the property
employment that
applies to instances of
the class employee and the values of
the employment
property are instances of the class employer.
The other alternative
will generate an RDF property employment with domain
employer
and range employee, which
means that the property employment now applies to
instances of the
class employment and the values
of the property are instances of the class
employee.
Since the Topic Maps associations are
directionally neutral,
there is no danger of reversing the
directionality of the origin. Instead, we are
fixing it. As Topic Maps
do not assume any directionality, the names
of the associations are typically articulated by
a direction-neutral
terms (e.g. employment). Thus, the
selection of the directionality does not
affect the readability of the translation result.
Topic Maps external occurrences are
converted to a seeAlso property
with an object of the occurrence URI.
Internal occurrences are also converted to a
seeAlso
property with an object of the internal resource
(string). Finally topic
names are
converted to an RDFS label property.
Translating between RDF and Topic Maps metadata formats
As stated earlier, the goal of this
work is to foster the interchange of resources
between RDF and Topic Maps.
However, resource semantics is a broad term,
which includes ontologies, metadata and
instance-data. From the
perspective of a librarian, cataloger or
publisher, the Semantic Web is a metadata
initiative. Therefore,
successful interoperation requires an
efficient strategy of how the standard categories
of metadata relate to
each other across RDF and Topic Maps
formats. The current web includes ontologies and
metadata developed by
different communities, such as Dublin Core
metadata, FOAF etc. FOAF data currently makes the
bulk of the
Semantic Web information. Another trend is
the rise of data that commits to simple
ontologies like SKOS.
The proposed approach combining translation
of overlapping concepts with generic translation
also incorporates
methods specializing in specific metadata
formats and dialect vocabularies. The inclusion
of metadata into
“overlapping concepts” is a reflection
of the existence of both RDF and Topic Maps
standards for encoding the
included metadata. The standardization is an
important component that integrates them in both
languages and
makes them overlapping concepts. For example
DCMI currently has a Recommendation for
expressing Dublin Core
in RDF/XML [Beck02],
[Nils08] and a new work item was
recently approved
by ISO for a Type 3 Technical Report on Expressing
Dublin Core Metadata Using Topic Maps [Pepp07a], [Pepp07b].
Assuming a two-format representation, the translation turns
into a simple mapping between two standard
encodings of the same metadata concepts.
Table 2 shows the proposed translation
between RDF and Topic Maps representation of
Dublin Core Metadata.
Based on the recommended encoding of the
Simple Dublin Core Metadata Element Set [DCMI06]
and following Pepper [Pepp07a], elements contributor, coverage,
creator, type, format, language,
publisher, relation, source and
subject are
translated into an
association of the corresponding type (see
Table 2). The resource to which metadata is being
assigned
is represented as an association role of
type resource,
while
the other role of the association is of
type value. The
elements
date, description,
identifier and rights
are represented as occurrences, while the
element title is
represented
as a topic name.
The reverse direction is obvious. Topic
Map associations of type contributor, coverage,
creator format, language, publisher,
relation, source, subject, and
type
are translated to dc:contributor, dc:coverage,
dc:creator, dc:format, dc:language,
dc:publisher, dc:relation, dc:source,
dc:subject, and dc:type, where the
players of type resource are translated to
the subject and the player of type
value to
the object of the corresponding property. The
occurrences of type
date, description,
identifier and rights are
translated to properties dc:date, dc:description,
dc:identifier and
dc:rights.
Finally, the topic name
is translated to dc:title.
Table II
Translation between Dublin Core
Metadata Element Set and Topic Maps
DC Term Name
TM Representation
contributor
association
contributor(object: resource, subject:
value)
coverage
association
coverage(object: resource, subject:
value)
creator
association
creator(object: resource, subject:
value)
date
occurrence of type date
description
occurrence of type description
format
association
format(object: resource, subject: value)
identifier
occurrence of type identifier
language
association
language(object: resource, subject:
value)
publisher
association
publisher(object: resource, subject:
value)
relation
association
relation(object: resource, subject:
value)
rights
occurrence of type rights
source
association
source(object: resource, subject: value)
subject
association
subject(object: resource, subject:
value)
title
name of type title
As for RDF dialects, such as FOAF and
SKOS, there are no corresponding recommendations
from the
Topic Maps ISO side. As an illustration of a
translation between SKOS and Topic Maps, we
present
sketchy translations between associations
from the TM4L Dialect of Topic Maps and SKOS
properties.
The association Related(player1:Relation,
player2:Relation) is translated
to RDFS by creating a symmetric property
rdfs:related by defining
a symmetric property rdfs:related
which entails the inverse relation of subject and object.
Generic Translation
Semantics preserving translation is
just one of the criteria guiding our translation
strategy.
As we mentioned earlier, the translation is
intended to facilitate interoperability. The
interoperability
is measured by the ability of the two models
to share common concepts. Concepts that have no
match in the
other model are among the ones that make the
translation task difficult. For such concepts it
is possible
to provide translation generating
semantically equivalent results such that even a
user familiar with RDF
and Topic Maps will hardly recognize and
understand. Therefore, for non overlapping concepts
we combine
our criteria with pragmatic
interoperability. Pragmatic
interoperability
is used to measure the utilization of the
translation result for achieving the agent’s
goals. It is in
line with the intentional consistency as a
measure of the occurrences when the intended
meaning of the
translation is in agreement with its actual
interpretation from agents operating on the
source. The proposed
generic translation is intended to generate
syntactically correct encoding in terms of the
target model
with minimal intentional inconsistency with
respect to the original. All Topic Maps and RDF
concepts that
do not have corresponding counterparts in
the other model (i.e. with no matching target
concepts) are
subject to generic translation. Strictly
speaking, the term “generic translation”
refers to a combination
of translations and transformations. The
semantics of particular source concept may change
its meaning
when translated into the target model in
order to make use of existing concepts with
similar meaning
instead of introducing new ones.
Syntactically correct RDF (Topic Maps) output
generated by the proposed
approach does not always imply a compete
translation. It is possible that part of the
input changes both
the representation and semantics during the translation.
The Topic Maps N-ary associations are
dealt with generic translation. The concept of an
n-ary
relationship can be represented in multiple
ways in RDF. We propose a translation where an
association
n-assoc(player1:Role1,
player2:Role2, .., playern:Rolen) is
translated
to RDF by creating blank nodes as shown in Figure 1.
N-ary-relation
Figure 1. N-ary Relationship
The other Topic Map concept that is a
subject to a generic translation is scope. We
translate scope by
defining an RDF class Scope. The intention is
for all scopes to be defined as
a subclass of the class Scope. Thus each scoped
item (e.g. name or occurrence)
becomes a member of a particular “scope”
subclass in addition to its preexisting class
memberships. Such
scope-based classifications allow additional
grouping of the corresponding items based on
their scopes.
The variant names are translated into a
new RDFS property altLabel
defined as a sub-property of the property
rdfs:label. The
use of the term
altLabel
was motivated by its inclusion in the SKOS
vocabulary.
The non-overlapping concepts from the
RDF side include containers and
collections.
The three types of containers:
bag,
sequence and alternative are intended
for a different type of resource grouping. We
transform RDF containers into compositions of the
predefined
Topic Maps InstanceOf and Whole-Part binary
associations. For each container, a topic of
type Bag, Seq or
Alt is
generated. This topic container is also a role
payer of type
whole. The
contained items, called members, are translated
as role players
of type part
and linked by the Whole-Part
association to the container topic.
For example, the statement “Group1
consists of the students Elva, James and Paul”
with the following
RDF representation.
The remaining type of containers
rdf:Seq or
rdf:Alt are
translated in a similar fashion. The difference
is that the
container topic is now of type Seq or Alt
instead of Bag. Thus each container
is composed from multiple
Whole-Part
associations applied to the container topic of
type
Bag/Seq/Alt.
The translation of the RDF built-in
structure List
is similar with some
abuse of the Whole-Part association. To
avoid defining new non-standard
associations we (inaccurately) treat list
members as part of a particular container of type
List. Thus
RDF list members are represented as parts of a
topic
of type List .
Implementation and Examples
The tool providing the translation
between RDF and Topic Maps has been designed as a
plug-in to the Topic Maps
editing environment, TM4L
(http://compsci.wssu.edu/iis/nsdl/
download.html). TM4L [Dich06]
development was driven
by two competing considerations: to provide
a general purpose Topic Map editor and to create
an e-learning
environment providing authoring and browsing
support for creating ontology-based learning
content and for
structuring digital repositories. To be
consistent with the intended support and
functionality, TM4L has
extended the basic Topic Map vocabulary with
predefined associations that are of specific
importance to
organizing digital collections. The
vocabulary supported by TM4L can be viewed as a
light Topic Map dialect.
The translation tool being a plug-in to TM4L
interprets the TM4L (extended) vocabulary as a
basic Topic Maps
vocabulary. That explains why the
associations of type Whole-Part and
Related are
dealt with focused translation. The first version
of the plug-in was
released for public use and testing in
October 2007. The next version is planed for
August 2008.
The following are examples intended to
demonstrate the work of the translation plug-in.
The examples
are presented based on RDF/XML format (for
RDF) and XTM (for Topic Maps). The reason to use
the verbose
XTM format is because (i) the Topic Map
input/output from TM4L is in XTM format; (ii)
there is no official
standard for a compact Topic Maps syntax.
The first one demonstrates translations
of RDF to Topic Maps. The RDF statement asserting
that the
resource with rdf:label property value
Spinach
Lasagna is an instance of the resource
with
rdf:label
property value Lasagna is
translated into an equivalent Topic Maps set
of assertions: namely, the topic with basename
Spinach Lasagna is an instance
of the topic with basename Lasagna.
In the following examples we use
obvious short expressions, e.g. “resource
Lasagna”
instead of “resource with rdf:label
property value Lasagna” and “topic
Lasagna”
instead of “topic with base name Lasagna” in order to
simplify the readability
of the description.
The next example illustrates
translation of the Topic Maps Superclass-Subclass
association into rdf:subClassOf property.
The Topic Maps expressions asserting that
the topic Lasagna plays a role of
subclass and
Pasta
Dishes plays a role of superclass in the
association of type Superclass-Subclass is
translated into a set of
RDF statements asserting that the resources
Lasagna and
Pasta
Dishes are instances of rdfs:Class and
Lasagna is
subClassOf Pasta
Dishes.
The next example illustrates a generic
translation using the user-defined binary
association
Recommended. The Topic
Maps statements asserting that
Turkey Dish
playing a role of Dish and
Thanksgiving playing a
role of Event in
the Recommended association
are translated into a set of RDF statements
asserting that Recommended is a property
with
rdfs:domain
Dish and rdfs:range Event. Turkey
Dish is an instance of Dish and is Recommended
for Thanksgiving, which is an
instance of Event.
To simplify the illustration, the topic
definition Dish, Turkey
Dish, Event
and Thanksgiving have been
omitted from the xtm verbose encoding in the
following example.
The final example illustrates
translation of Dublin Core meta-data represented
in RDF/XML format
into Topic Maps. The RDF statements
asserting that Dave
Beckett is a
creator of
Dublin Core Metadata
Initiative - Home Page
with creation
date of 2001-01-16
is translated into Topic Maps statements asserting that the topic
Dublin Core Metadata
Initiative - Home Page topic with
occurrence
2001-01-16
of type Date
plays a role of
Resource,
while the topic Dave
Beckett
plays a role of Value in the Creator
association.
The Web and Semantic Web visions are to
share and reuse as much information as possible.
A significant amount of work is being done
to match different vocabularies. In this area
there have
been considerable studies of approaches for
ontology mapping where the concept of sharing and
reusability
has been interlinked to merging, alignment,
articulation and fusion of ontologies [Kalf03],
[Klein01, [Shvai05].
In parallel, a number of different
languages for ontology modeling have been
proposed [Cran01],
[Hunt03], [UML], [XTM],
[RDF],
[OWL].
The problem of interoperation of
heterogeneous data, based on different data
models and modeling languages
like RDF, Topic Maps, UML, etc. implies
model-based methods aiming at model-level
interoperability.
As a result, a number of informal
model-to-model mappings have been defined,
including several proposals
for mapping between Topic Maps and RDF
[Bowe02], [Cran01], [Creg05],
[DCMI06], [Dich07], [Dich06].
Despite the emerging
interest in the problem
and the availability of some mapping tools,
the reports on integration and reuse of data
across the two
models are scarce.
Moore [Moore01] was
perhaps the first to propose a general strategy
towards
integration between Topic Maps
and RDF. Lacher and Decker [Lach01] presented a model for mapping
between the two
standards by exploiting the
“Topicmaps.net’s Processing Model for
XTM 1.0” [Newc01].
Technically, the conversion
from Topic Maps to RDF is
achieved by mapping the Topic Map graph
model to an RDF graph. Another work in the area
of TM2RDF mapping
has been presented by Ogievetski. His
starting point is also [Newc01]
but he uses
XSLT-based technology to
translate any topic map document expressed
in XTM into RDF abbreviated syntax. The relation
between Topic Maps
and RDF are discussed in detail by Garshol
[Gars03], [Gars05]. The
proposal in [Cran01] is the most mature work in the field,
which describes how to convert information
back and forth between the two technologies, how
to convert schema
information, and how to do queries across
both information representations. Some
strategies and algorithms
to achieve these goals are also presented
along with a discussion of the problems of
conversion between
Topic Maps and RDF. A practical result of
this work is the extension of the Ontopia’s
Topic Map browser
Omnigator (http://www.ontopia.net) with
functionality allowing export/import between
Topic Maps and RDF data.
An interesting work on integration
between the Topic Maps and RDF technologies has
been done as part
of the META project, resulting in a set of
tools for converting, editing and navigating
metadata expressed
in either language [Cian03].
In this work the authors present their approach
to the
bidirectional conversion of RDF
and Topic Maps and show how the use of
schemas and standard predicates in RDF can lead
to a flexible integration
of the two languages. This integration is in
the heart of the effort aimed at incorporating
separate modules into
a single editing and navigation tool that
can be used for metadata collections expressed in
both languages.
An analysis of the relationship between
Topic Maps and RDF and a complete review and
comparison of the
RDF/Topic Maps interoperability proposals
can be found in [Pepp06a].
Despite the efforts
and the proposed Guidelines
for RDF/Topic Maps Interoperability [Pepp06b], however, there is no fully
worked out and
widely agreed upon solution
to achieving data interoperability between RDF and Topic Maps yet.
Among the relevant works there are also
the efforts for translating between Topic Maps
and ontology
models such as OWL and UML [Colo06], [Creg05].
While our work might seem close to the
previous proposals on the surface, there are
principal differences
stemming from the different perception of
the commonalities in both models. For example,
the key correspondences
between RDF and Topic Maps according to
Garshol and Pepper [Gars03],
[Pepp06b]
are “subject (TM) — resource (RDF)” and
“topic (TM) — node (RDF)”, which is
reflected in their mapping strategy. In contrast,
our approach builds on
ontological correspondence. Commonalities
are better explicated in correspondences between
how the things are
related than in correspondences between the
things alone. Thus, the “overlapping
approach” reflects our
understanding of the role of a shared
ontological ground for meaning preserving
translation. This sets the
starting point in our translation strategy
on overlapping concepts with reliance on existing
and established
vocabularies (e.g. RDF/RDFS, Dublin Core,
and SKOS). The key factors of this strategy
include minimizing the
need of (i) a centralized agreement on new
vocabulary terms emerging from the translation
and (ii) an agreement
of how to guide the translation process.
In contrast to previous work,
considering the completeness of translation as a
key requirement, our
starting point here was the question: Which
Topic Maps constructs can be represented
naturally in RDF (i.e.
as RDF graphs) without RDF vocabulary
extension? We also asked the reverse question:
Which are the RDF
constructs naturally expressible in Topic
Maps? Since, generally, full automatic
translation between RDF and
Topic Maps is not possible, one possibility
is to focus on semantic-preserving translation of
the
ontologies/schemas with common conceptual
space. Translation satisfying such requirements
can be achieved by
identifying the overlapping conceptual
domains of both models. Another difference is
that our approach does
not assume any annotation for guiding the
translation. As a result, pre-translating human
involvement is
eliminated.
Among the most challenging tasks in the
area of Topic Maps/RDF interoperability is the
problem of
translating the Topic Maps concept of scope.
Except for Garshol [Gars03] no
other
works handle scope in satisfactory
manner. The list of challenging problems
also includes mapping variant names and RDF types
such as containers
and collections. None of the existing tools
which provide translation between RDF and Topic
Maps are able to
handle containers and collections. The only
approach for dealing with variant names is that
of Ciancarini et
al [Cian03], [Pepp06c]. They suggest translating
variant names
as compound concepts, which is quite unintuitive. Our
strategy with non-overlapping concepts is
similar to the way terms are traditionally
translated from one
language to another with a more limited
vocabulary, namely, with a maximal reuse of the
source vocabulary that
is able to convey the intended meaning of the original.
Conclusion
Taking the information from one
representation scheme (such as Topic Maps) and
extracting some
or all of it for use in another scheme (such
as RDF) is a difficult task [Bowe02].
In fact, few working tools
exist to perform such transformations. One
reason such conversions are difficult is that
representation
schemes differ in the basic structural
constructs, granularity and schema constraints
they provide for
organizing information. As a consequence, straightforward, one-to-one
mappings between schemes rarely exist.
In this context, the presented work is
a contribution to the efforts aimed at improving
the
interoperability between Topic Maps and RDF.
It has been carried on as a part of the TM4L
project. The driving
insight was to allow TM4L users to use RDF
data and also enable RDF applications to use
Topic Maps data produced
with TM4L. During the implementation stage,
the initial objective was broadened to create a
general tool for
RDF - Topic Maps translation. In the
proposed approach, the balance between
translation and transformation is
in agreement with the target model. The type
of translation applied to a particular construct
depends on the
level of overlapping between the
corresponding concepts in the two models. The
idea is to provide a translation,
from which the reader in the target is able
to grasp the intended meaning of the original
author. This implies
a translation with acceptable loss of
information when complete translation is
impractical.
The proposed Topic Maps/RDF translation
approach is implemented as a plug-in for TM4L.
Although the
illustrative examples mostly cover Topic
Maps to RDF mapping, the actual implementation
enables round tripping
translation for a significant class of
RDF/Topic Maps concepts. The idea is to enable
Topic Maps users not
only to merge existing Topic Maps data with
RDF data but also to exploit some inference
capability and then
translate the results back into Topic Maps.
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