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A Logical Model for Taxonomic Concepts for
Expanding Knowledge using Linked Open Data
Rathachai Chawuthai1, Hideaki Takeda2, Vilas Wuwongse3, and Utsugi Jinbo4
1
Asian Institute of Technology, Prathumtani, Thailand
National Institute of Informatics, Tokyo, Japan
3
Thammasat University, Prathumtani, Thailand
4
National Museum of Nature and Science, Tokyo, Japan
2
*These authors contributed equally to this work
§
Corresponding author
Email addresses:
RC: rathachai.c@gmail.com
HT: takeda@nii.ac.jp
VW: wvilas@engr.tu.ac.th
UJ: ujinbo@kahaku.go.jp
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Abstract
Background
The variety of taxonomic classification systems and the new discovery of taxonomists
lead to the diversity of biological information, especially taxon concepts. The
association among taxon concepts across research institutes is very difficult to
establish, because there is no single interpretation of the name of a taxon concept.
Owing to this difficulty, further integration of more biological knowledge is very
complicated when they deal with many sources of data or depending on different
taxon concepts.
Results
1This research describes the change of taxon concept as event of change in Resource
Description Framework (RDF). Our logical model, Linked Taxonomic Knowledge
(LTK), provides ontology and semantic rules to execute the change to establish the
evolutionary relationship between taxon concepts, and temporal information about a
taxon concept. Herewith, we implement a prototype to demonstrate the feasibility and
suitability of our approach. Moreover, our model can publish some taxonomic
information as linked data and, hence, with additional benefits of Linked Open Data
(LOD) cloud. Our findings enabled the way to link knowledge about taxon concepts
which are recorded in different name by different time and different community.
Conclusions
We have developed a framework and an application for establishing evolutionary
relationships among taxon concepts across research repositories, and preserving
background knowledge of their changes. It has been found that the precise
interpretation and the link of taxon concepts gave positive support to biologists to
understand species correctly.
Keywords
Biological data, Biodiversity informatics, Logical model, Linked data, Ontology,
Semantic web, Taxon concept
Background
More than 1.4 million species throughout the world have been truly described and
classified with appropriate naming depended upon their characteristics; such as,
morphological characters, living behaviors, DNA sequences, etc. [Darwin &
Peckham, 1959] [Winston, 1999]. Many taxonomists have dedicated themselves to
study living organisms, research, and publish their knowledge for over hundred years.
However, their researchers have not been completely accepted across all researchers
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around the world. In addition, there is no consensus on classification systems among
all taxonomists. In other words, taxonomists might have different perspectives to
classify and name living organisms. As a consequence, a same species often be
classified and named differently [Winston, 1999]. For example, Papilio xuthus
Linnaeus, 1767, Chinese Yellow Swallowtail Butterfly, has also been given several
names by several taxonomists, such as xuthulus Bremer, 1861, chinensis Neuburger,
1900, koxinga Fruhstorfer, 1908, and neoxuthus Fruhstorfer, 1908.
The progress of taxonomic studies frequently causes redefinition of taxon concept, a
circumscription of the taxon [Winston, 1999]. For instance, two genera of owls,
Nyctea and Bubo, were merged into the latter genus Bubo. Following the change of
genera, the scientific name of a snowy owl Nyctea scandiaca has been subsequently
changed to Bubo scandiacus in order to satisfy the convention of the scientific name
[Wink & Hedrich, 1999]. Thus, a scientific name and a taxonomic concept become
lacking of a single interpretation in biological [Mallet, 2007][Ytol & Morse, 2001].
Due to such change of taxon names, one sometimes misses information of this species
under the name of the old scientific name when he or she searches information by the
new scientific name.
Moreover, some details make researchers be confused when some taxon concepts
were reclassified many times. For example, there is a case of a Baltimore oriole
(Icerus galbula Linnaeus 1758) and a Bullock’s oriole (I. bullockii Swainson, 1827).
In 1964, Sibley and Short argued that these two species should be merged into a
single species [Sibley, 1964]. The former name, I. gulbula, was an accepted name;
whereas the I. bullockii was a jounior synonym. By contrast, in 1995, the DNA
sequence result led to the splitting of I. galbula to be I. bulbula and I. bullockii again
[Freeman, 1995]. Although these two species are currently disjointed, a part of
information of I. galbula, especially recorded between 1964 and 1995, might include
details of I. bullockii. One could obtain imprecise information when he or she simply
searches information by the name I. galbula. Therefore, a mechanism that enables to
link among taxon concepts in the precise context is necessary.
Recently, there was a research about managing the change in scientific conception.
The work applied semantic web to develop a meta-ontology of a biological name
(TaxMeOn). It provides metadata for representing and managing the temporal change
of scientific names of a unit of taxon concept to another unit, and emphasized how the
biological names publish [Tuominen & Laurenne, 2011]. However, the management
of name change is not enough for researchers. The correct interpretation with
temporal context of concepts and reasons of their changes becomes necessity as well.
The purpose of our research is to formulate a logical model for preserving background
knowledge of the change of taxon concepts, and link some related concepts together.
We introduced ontology for collecting the change of taxon concepts, cause and effect
of the change; and linked data resulting from the change of concepts. We considered
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to enhance Conceptual Knowledge for Archives (CKA) [Chawuthai, 2012] approach
to capture the changes of taxon concepts, and their context. We also reused taxonomic
terms from Linked Open Data for ACademia (LODAC) [lod.ac], employed Simple
Knowledge Organization System (SKOS) [skos] vocabularies to manage the
relationship between concepts, and publicized data to Linked Open Data (LOD)
Cloud [lod]. We also performed an implementation to prove the feasibility of our
proposed model. At last, we evaluated this work by having discussions with
researchers who study about the species.
Methods
In this section we describe the tools and methods used to develop a logical model, and
the prototype.
Linked Taxonomic Knowledge (TLK) Ontology
The logical model, developed in this work, introduced the functional requirements of
data model to query, access, link, manage, and execute the change of taxon concepts.
We therefore considered Semantic Web to formulate a model, because this technology
results in the semantic integration of data among dataset in the world [Health, 2011].
The temporal RDF approach is also utilized to develop the representation of the
change of taxonomic concepts within timeframe [Gutierrez, 2005]. As a result, the
model was presented in an RDF language called Linked Taxonomic Knowledge
(LTK) Ontology together with rule-based reasoning. The semantic web rules were
used to execute the temporal RDF for producing the evolutionary relationship of
taxon concepts, and then the data is published in global data space.
Web Application
The prototype is a web-based system that comprises three service layers: web
interface, web services, and RDF data store. Firstly, the web interface allows a user to
create the knowledge of taxon concepts in RDF. It also demonstrates the temporal
context and link of taxon concepts. Further, it presents the reasons and details about
changes of them. Secondly, an LTK-Service, Java servlet service, is made for
managing and computing RDF data by using the performance of Jena [jena] reasoning
engine. Other clients can access data via this layer. Lastly, we used OpenRDF
[openrdf], an RDF store, to record data. Users can create data which come from some
publications or books, and then the data is published to the LOD cloud by providing
SPARQL endpoint.
LTK-Service and SPARQL endpoint
Some example dataset was loaded into OpenRDF [openrdf] storage via LTK-Service
for querying. SPARQL endpoint for querying the evolutionary relationships of a
taxon concept is accessible at http://[hostname]/openrdf-sesame/repositories/taxon.
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This endpoint also offers the querying of temporal data of a taxon concept. However,
the LTK-Service enables a service to present N-Triples RDF of a taxon concept at a
specific time by the following request http: //[hostname] /ltk-service/context/?
concept= [taxonconcept] &time= [timepoint]; where [taxonconcept] is a URI of a
taxon concept, and [timepoint] is a specific time-point in XSD: dateTime. Moreover,
the background knowledge of the change is available at http://[hostname]/ltkservice/reason/?concept1=[taxonconcept1]&concept2=[taxonconcept2],
where
[concept1] and [concept2] are URIs of two taxon concepts which are focusing on.
Results
We present here a logical model for taxonomic concepts for expanding knowledge
using LOD. Here, our model is expressed in ontology named Linked Taxonomic
Knowledge (LTK), and semantic web rules. Moreover, the last part was to implement
a prototype. In order to prove the feasibility and suitability of our model, we
implemented a web-based application as a prototype.
A Logical Model
In the beginning, we studied how to classify the change of taxon concept; we found
that they are two major categories: the change of name, and the change of
classification [Wingston, 1999][Tuominen & Laurenne, 2011] [Franz & Peet, 2009].
A taxon name is sometimes changed for several reasons. For example, Hoare (2008)
established the genus Kendrickia (ostracods). Then Kempf (2010) found that this
genus was a primary junior homonym of Kendrickia Solem, 1985 (gastropods), and
proposed Dickhoarea as the replacement name for Kendrickia Hoare, 2008. It results
to the subsequent change of species names; for instance Kendrickia asketos had been
changed into Dickhoarea asketos since Kampf (2010) has been published [Wingston,
1999]. Apart from such name change, classifications also may be changed according
to the progress of taxonomic researches. For example, the genus Columba (pigeons)
has been split into five genera: Patagioenas, Chloroenas, Lepidoenas, Oenoenas, and
Columba in the new narrow concept, and then some species of genus Columba have
been assigned to one of these newly separated genera. For instance, Columba speciosa
changed to Patagioenas speciosa [Banks & Cicero, 2003]. The analysis of the
changes of taxon concept is described by Fig. 1.
After that, we reviewed ideas in TaxMeOn, to describe concepts in the taxonomic
field linked to identifiers [Tuominen & Laurenne, 2011]. In general, when a concept’s
scope is changed, the changed concept needs to be recognized as new identifier. For
instance, the genus Bubo before merging with Nyctea must not be the same identifier
as Bubo after merging [Wingston, 1999][Wink&Heidrich,1999]. Thus, an identifier
similar to those in TaxMeOn is required for our model. On the other hand, most
attributes of the old Bubo can be copied to the new Bubo definitely, because, the old
Bubo and the new Bubo may share many attributes.
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Next, to publish data, we reviewed some standards that can be reused for our model.
To model the entities of taxon concepts, we considered reusing some vocabularies
from Linked Open Data for ACademia (LODAC), a project to publish a wide range of
academic data including species information [lodac]. For example, a relationship
between a species and a genus can be described as RDF using LODAC terms
(species and genus are namespaces for species and genus in LODAC,
respectively):
species:Nyctea_scandiaca
species:hasSuperTaxon
genus:Nyctea .
Another issue is to describe changes of concept and associated information on the
change. There is an approach named Contextual Knowledge for Archives (CKA)
Ontology. It offers a logical model developed using Flouris’s theory for presenting the
changes of conceptions, such as, merge, replace, and split. It also presents reasons
behind the changes, changes of relationships such as the reclassification of a concept,
and links between some relevant concepts. The CKA illustrates the change of
concepts as dynamic RDF that contains fact and temporal aspect
[Chawuthai,2012][Flouris,2007]. For instance, the following RDF expresses the
splitting of a genus Columba.
ex:change2003
cka:interval
cka:assure
[tl:beginAtDateTime “2003”] ;
ex:split1 .
ex:split1
rdf:type
ltk:TaxonSplitter ;
cka:conceptBefore genus:Columba ;
cka:conceptAfter genus:Patagioenas,
genus:Chloroenas,
genus:Lepidoenas,
genus:Oenoenas,
genus:Columba_2003.
Further, the framework provides a technique to transform this dynamic RDF to static
RDF with a given specific time point. For example, after year 2003, relationships
among genus:Columba and its allies can be found as follows:
genus:Columba ltk:splitInto
genus:Patagioenas,
genus:Chloroenas,
genus:Lepidoenas,
genus:Oenoenas,
genus:Columba_2003.
Technically, the CKA framework allows other ontology create own operations of
changes by extending a class cka:ConceptEvolution for the change of scope of some
concepts, or a class cka:RelationEvolution for the change of binary relationship
between two concepts. For example, the operations of the change of taxon concepts,
such as the ltk:TaxonMerger and ltk:TaxonSplitter, has extended the
cka:ConceptEvolution. Thus, in case of having new properties which are not a part of
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either CKA and LTK, such as, color, size, organ, behavior, etc.; it needs to define
some new operations of change, extend one of the mentioned classes from CKA, and
then bind the new operations with some related properties. In addition, this model
states one change as one unit. It offers association among related units of some
changes by having some properties: cka:caused, and cka:effect to express reason and
outcome of a change respectively. For example, Fig. 2 demonstrates the new name
Patagioenas species and its background. Consequently, we can find out the history of
the name “Patagioenas speciosa”. Then, we can use its background concept, such as
the old name “Columba speciosa” to explore more information in the public LOD.
To link data with LOD Cloud, our research proposed some useful operations that
specify the change of taxon concepts, the changes of details of a taxon concept, the
changes of relationships between taxon concepts, and the background of the change.
All operations are defined by extending some vocabularies from the well-known
ontology: Simple Knowledge Organization System (SKOS), and some properties
from LODAC and CKA. The namespaces used by this model are described in Table
1. As a result, the data from our framework can definitely be exchanged among other
repositories. Example of some properties is shown in Table 2.
For example, the genus:Nyctea and genus:Bubo in old concepts have been merged
into a new concept with the name Bubo. As stated previously, the genus Bubo in the
new concept should be given a new identifier. In practice, we ended the year when it
has been changed, so the new identifier of genus:Bubo may be genus:Bubo_1999. The
property named ltk:mergedInto is defined to express a merge of two taxon concepts.
The relationship between genus:Nyctea and genus:Bubo_1999 remains to be specified
by the property ltk:mergedInto. On the other hand, another special property name
ltk:majorMergedInto is introduced to demonstrate the very close relationship of two
concepts, such as genus:Bubo and genus:Bubo_1999. As Nyctea was merged to Bubo,
Nyctea scandiaca, the only member species of Nyctea, is transferred to Bubo and
change the name to Bubo scandiacus [Wingston, 1999][Wink&Heidrich,1999]. In
summary, these facts will be presented in RDF that satisfies the logical model of the
CKA approach as follows:
ex:change1999
bibo:performer
bibo:issuer
dcterms:source
cka:interval
cka:assure
pp:Wing, pp:Heidrich ;
pp:Richard ;
pub:5224773;
[tl:beginAtDateTime “1999”];
ex:mg1, ex:rp1, ex:ac1 .
ex:mg1
rdf:type
cka:conceptBefore
cka:conceptAfter
ltk:TaxonMerger ;
genus:Bubo, genus:Nyctea ;
genus:Bubo_1999 .
ex:rp1
rdf:type
cka:conceptBefore
ltk:TaxonReplacement ;
species:Nyctea_scandiaca ;
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cka:conceptAfter
species:Bubo_scandiacus .
ex:ac1
rdf:type
cka:child
cka:parent
ltk:HigherTaxonAddition ;
species:Bubo_scandiacus ;
species:Bubo_1999 .
ex:mg1
cka:cause
ex:rp1 .
ex:rp1
cka:detail
ex:ac1 .
After that, we apply some rules to transform dynamic RDF data to static form. For
example, a rule that infers the merging operation of taxon concepts is expressed along
these lines:
?change
rdf:type
ltk:TaxonMerger .
?change
cka:conceptBefore
?before .
?change
cka:conceptAfter
?after .
―----------------------------------------------------------?before
ltk:mergedInto
?after .
This rule and some other rules that infer each operation of change can convert the
temporal RDF to be the following result.
genus:Nyctea
ltk:mergedInto
genus:Bubo_1999 .
genus:Bubo
ltk:majorMergedInto
genus:Bubo_1999 .
species:Bubo_scandiacus
ltk:higherTaxon
genus:Bubo_1999 .
species:Bubo_scandiacus
ltk:synonym
species:Nyctea_scandiaca .
genus:Nyctea
genus:Bubo
genus:Bubo_1999
species:Nyctea_scandiaca
species:Bubo_scandiacus
cka:expired
cka:expired
cka:entered
cka:expired
cka:entered
“1999”
“1999”
“1999”
“1999”
“1999”
.
.
.
.
.
Therefore, clients can query these facts conveniently. For instance, if the users query
some genera, which closely match (skos:closeMatch) genus:Nyctea; they will get
genus:Bubo_1999. They sometimes query the data with species:hasSuperTaxon and
get the result as same as ltk:higherTaxon. They can also find the present-day taxon
concepts by inquiring some concepts which do not have a property named
cka:expired. Moreover; the client can query more detail about a fact that includes the
time when it changed, people who involved, reference documents, and triple data. For
example, the replacement of species:Nyctea_scandiaca was caused by the merging
between genus:Nyctea and genus:Bubo. In addition, the relationships of concepts can
be presented by RDF statements, because the operation ltk:HigherTaxonAddition can
establish the associations between concepts by producing some triples with having a
property named ltk:higherTaxon. Our work offers some operations binding with
properties; such as, dwc:scientificName [dwc], foaf:depiction [foaf],
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species:hasCommonName [lodac], etc. Thus, the consumers can query temporal
information of taxon concepts along with specific time point.
Prototype
After developing the LTK ontology, we verified the possibility and feasibility of it by
implementing a prototype. The main purposes of the implementation are to define and
execute the change of taxon concepts, and to present temporal information about
them.
The prototype provides a web page that allows users to input the change of taxon
concepts. At first, users have to choose an operation of change such as merge, replace,
split, and etc; and identify URI(s) of concept before and URI(s) of concept after. After
that, the user may define the relationship between changes. At last, they have to
prepare some metadata of these changes, such as, begin time, end time, some
performers, some reporters, and some references. After the data is submitted, the
evolutionary relationships of taxon concepts are produced by rule-based reasoning.
The prototype also presents temporal data of a taxon concept along with specific time;
so users have to specify a URI of a taxon concept and a time-point in XSD:DateTime,
e.g.
http://[host]/ltk/concept.php?concept=http://lod.ac/species/Nyctea_scandiaca&date=2
012-01-01T00:00z . It results in the taxon concept’s RDF data which is correct at that
time. An example of the result is demonstrated in Fig. 3. On the figure, the left-side
screen presents the context of the species:Nyctea_scandiaca (the figure displays as
spc:Nyctea_scandiaca) and its linked taxon concepts, and the right-side screen shows
information about background knowledge of the change of the selected species.
Discussion
In this research, we discussed the outcome of our model by demonstrating the
prototype to people who work closely with taxonomic data, such as a biologist.
The outcome of using Linked Taxonomic Knowledge
[TBD]
To demonstrate the usability of our research, we describe the main idea of this
research and presented the prototype to some researchers who involved with
taxonomic data. Because our contribution is to develop a logical model for the change
of taxon concepts and linked data, most of data from prototype displayed in RDF that
is difficult to understand by end users. Therefore, it has described the outcome of our
research in case some applications integrate their data together with data from our
prototype.
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Limitations
According to example RDF data, it demonstrates that one change consists of many
triples. When all changes are recorded, the triple store will manage over billion
triples. Thus, it will consume a lot of resources when the service transforms the
dynamic data to flat data for every request. However, most of all requests always ask
for the present data. The prototype has to prepare current static data every time when
each dynamic data is recorded. Then, the service can provide fast responses to the
present information.
Conclusions
Our paper presents a logical model and ontology for linking taxon concepts which
comprises a series of changes, the diversity of taxonomic classifications, and the
variety of naming. For the purpose of linking data, we have developed our model by
employing ontology of contextual knowledge evolution together with some widely
accepted ontology such as LODAC and SKOS. Therefore, our model can deal with
both dynamic and static information represented in RDF and hence can trace the
history of the taxon concept. In addition, we have implemented a prototype which
utilizes the proposed model in order to publish the taxonomic information to LOD
cloud. As a consequence, other applications that need linked taxon concepts can
readily connect to these data. Moreover, we have implemented a knowledge base
using Jena’s inference engine and OpenRDF’s storage for computing data, and we
have provided a web application to record and present the information. The result of
our prototype demonstrates that our approach is feasible and suitable for the need of
linked taxon concepts across different repositories and relationship backgrounds in
order to discover broader knowledge of biology.
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However, our approach gives priority to ontology rather than software application;
hence the system requires much human effort to import a great number of data. For
example, when a genus is split, some species under the genus have to move to new
genera. In this case, taxonomists have to analyze and enter data by themselves. Thus,
it should have some algorithms to improve the reclassification of some taxonomic
ranks by their attributes. Moreover, in the future, when the number of data is over a
billion, requesting historical data would be a great challenge because it requires the
inference engine to process complex activities that consume very high computing
capability. Future research might be focusing on how to improve the computing
resources or methodologies for caching time-series of taxonomic data.
Abbreviations
LTK: Linked Taxonomic Knowledge; RDF: Resource Description Framework; LOD:
Linked Open Data; LODAC: Linked Open Data for ACademia; SKOS: Simple
Knowledge Organization System; FOAF: Friend of a Friend; SPARQL: SPARQL
Protocol and RDF Query Language; URI: Uniform Resource Identifier; XSD: XML
(Extensible Markup Language) Schema Definition
Competing interests
The authors have no conflicts of interest to declare.
Authors' contributions
RC participated in the analysis and design of the logical model, implementing a
prototype, and drafted and revised the manuscript. HT initiated this study, provided
case studies, gave advice, approved research plan and associated budget, and revised
the final version of the manuscript. VW offered technical sessions, gave research
methodology instruction, and revised the final version of the manuscript. UJ helped
analyze case studies, collected example data, and revised for the final version of the
manuscript. All authors read and approved the final manuscript.
Acknowledgements
This study was fully supported by National Institute of Informatics (NII).
Prior presentations
The methodology previously presented in the
Joint International Semantic
Technology Conference (JIST) during 2-4 December 2012 in Nara, Japan ; and the
first international workshop on Semantics for Biodiversity (S4BioDiv) during 26–27
May 2013 in Montpellier, France.
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17. Simple Knowledge Organization System: http://www.w3.org/TR/skos-primer/
18. Linked Open Data: http://linkeddata.org/
19. Darwin Core Terms: http://rs.tdwg.org/dwc/terms/
20. Friend of a Friend: http://xmlns.com/foaf/0.1/
21. The Timeline Ontology: http://motools.sourceforge.net/timeline/timeline.html
22. Semantically-Interlinked Online Communities Core Specification:
http://www.w3.org/Submission/sioc-spec/
23. Dublin Core Metadata Initiative Terms: http://dublincore.org/documents/dcmiterms/
24. Bibliographic Ontology: http://bibliontology.com
25. Apache Jena - reasoners and rule engines: http://jena.apache.org/
26. OpenRDF– a framework for processing RDF data: http://www.openrdf.org/
Figures
Figure 1 - Classification of the Changes of Taxon Concept
The analysis of the changes of taxon concept
Figure 2 - Relationship between some changes
Example relationship between splitting genus change and species name change.
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Figure 3 - Prototype
Example screen of information about the concept species:Nyctea_scandiaca.
Tables
Table 1 - Prefixes and their matching namespaces used in this paper
All prefixes that the logical model refers to.
Properties
rdfs:subPropertyOf
ltk:
Linked Taxonomic Knowledge Ontology
http://lod.ac/ontology/ltk-onto#
cka:
Contextual Knowledge for Archives Ontology [cka]
http://www.cka.org/2012/01/cka-onto#
tl:
Timeline Ontology
http://purl.org/NET/c4dm/timeline.owl#
soic:
Semantically-Interlinked Online Communities Core
Ontology
http://rdfs.org/sioc/ns#
dcterm:
Dublin Core Terms Namespace
http://purl.org/dc/terms/
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bibo:
Bibliographic Ontology
http://purl.org/ontology/bibo/
foaf:
Friend of a Friend Ontology [foaf]
http://xmlns.com/foaf/0.1/
species:
LODAC Species [lodac]
http://lod.ac/species/
skos:
Simple Knowledge Organization System Namespace
[skos]
http://www.w3.org/2004/02/skos/core#
Table 2 - Some properties from LTK Ontology
Example properties from LTK which are derived from CKA, LODAC, and SKOS
Properties
rdfs:subPropertyOf
ltk:higherTaxon
cka:higherClass, skos:broaderTransitive,
and species:hasSuperTaxon
ltk:replacedTo
cka:serialLinkTo, ltk:synonym, species:hasSynonym,
and skos:exactMatch
ltk:mergedInto
cka:serialLinkTo, and skos:relatedMatch
ltk:majorMergedInto
cka:serialLinkTo, and skos:closeMatch
ltk:splitInto
cka:serialLinkTo, and skos:relatedMatch
ltk:majorSplitInto
cka:serialLinkTo, and skos:closeMatch
ltk:synonym
skos:exactMatch
Additional files
Additional file 1 – Sample additional file title
Additional file descriptions text (including details of how to view the file, if it is in a
non-standard format).
Additional file 2 – Another sample additional file title
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Additional file descriptions text (including details of how to view the file, if it is in a
non-standard format).
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