=Paper=
{{Paper
|id=Vol-3707/D2R224_paper_3
|storemode=property
|title=Anticipate Risk with the Value and Trade Flows Knowledge Graph
|pdfUrl=https://ceur-ws.org/Vol-3707/D2R224_paper_3.pdf
|volume=Vol-3707
|authors=Felix Engel,Mark Vanin,Nenad Krdzavac
|dblpUrl=https://dblp.org/rec/conf/d2r2/0002VK24
}}
==Anticipate Risk with the Value and Trade Flows Knowledge Graph==
https://ceur-ws.org/Vol-3707/D2R224_paper_3.pdf
Anticipate risk with the value and trade flows
knowledge graph
Felix Engel1 , Mark Vanin1 and Nenad Krdzavac1
1
TIB – Leibniz Information Centre for Science and Technology, Welfengarten 1B, 30167 Hanover, Germany
Abstract
A key to resilient supply chains is the prediction of risk. There are extensive and openly licensed data
sources that can be used to predict risk. We are analyzing some of these data sources as part of the
“Cognitive Economy Intelligence Platform for the Resilience of Economic Ecosystems” project. In this
article, we present a solution in the context of the challenges we faced. Thereby, we put a focus on
data integration and enrichment to support risk management tools for risk anticipation. To address
these challenges, this paper introduces work on an ontology and a corresponding knowledge graph. The
ontology contains, among other things, mappings between commonly used classification schemes for
industry codes. This is one of the key pieces of information in the resulting knowledge graph. For the
implementation and analysis, the knowledge graph combines information from the Organization for
Economic Cooperation and Development Trade in Value Added and the International Trade at Product
Level databases. These databases have been prepared in the form of knowledge graphs by various project
partners from their respective sources.
Keywords
ontology, trade in value added indicators, international trade at product level, knowledge graph, resilient
1. Introduction
Global production processes are highly dependent on the resilience of global supply chains
[1]. In order to measure the resilience of international trade flows various indicators from
available information sources must be brought together as comprehensively as possible. A
formal semantic description of international trade flows, taking into account existing standards,
is suitable for such an integration of different data sources.
There are many global databases that provide insight into global supply chains [2]. For
example, the World Input-Output Database (WIOD) [3], the Eora Global Supply Chain Database
[4], the Global Trade Analysis Project (GTAP) [5] which use analytical models to study global
supply chains, the database International Trade at Product Level (BACI), and Trade in Value
Added (TiVA) databases [6].
To the best of our knowledge, these databases lack advanced analytical capabilities that use
knowledge graph for semantic data integration and sharing across the various computational
tools employed in resilience analytics.
Third International Workshop on Linked Data-driven Resilience Research (D2R2’24) co-located with ESWC 2024, May
27th, 2024, Hersonissos, Greece
Envelope-Open Felix.Engel@tib.eu (F. Engel); Mark.Vanin@tib.eu (M. Vanin); Nenad.Krdzavac@tib.eu (N. Krdzavac)
Orcid 0000−0002−3060−7052 (F. Engel); 0000-0003-4647-7886 (M. Vanin); 0000−0002−7881−3285 (N. Krdzavac)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
From the above list of available data sources, we have decided to use in the TiVA and the BACI
in this research. This is because they have a large overlap in trade flows. The BACI publishes
data on bilateral trade flows at the product level [7]. The BACI database contains information
on product names and the corresponding Harmonized System (HS) nomenclature for trade,
export and import country codes, trade volume, trade value, unit of measure, value and annual
data. With their intersection and divergent information, the TiVA and BACI databases, when
integrated, complement each other to form an information-rich basis for analyzing the resilience
of many different supply chains.
However, both the TiVA and BACI databases have non-binary relationships between entities.
For example, the domestic value added content of gross export (code name exgr_dva) indicator
has trade, which consists of trade amount, trade value and product name. This means that the
exgr_dva has values for different aspects of the existing trade relation. Its trade and amount
values are decimal numbers, and product names are strings. Such non-binary relations are
problematic when developing models that integrate different data sources using only binary
relations. We propose the use of the Web Ontology Language (OWL) [8] to model the four and
three dimensional indicators, and thus provide a framework for semantic data representation
and querying. In this work, we address the following research questions (RQ):
RQ1: Can we apply n-ary relations [9] to overcome the challenge of developing a model that
integrates existing data sources related to supply chains?
RQ2: How can federated querying be leveraged to efficiently retrieve information from the
integrated ontology model concerning global supply chains?
RQ3: How do we ensure interoperability between different industry classification standards
used in these data sources?
To address the outlined research question, the contributions of this paper are:
• To document the value and trade flows (VTF) ontology (RQ1), the implementation of
mappings between the TiVA and the International Standard Industrial Classification of
all Economic Activities (ISIC) Rev. 4 industry code classification schemes (RQ3), and the
VTF Knowledge Graph (VTF KG) (RQ1).
• To outline the implementation details of REST API for querying the TiVA Knowledge
Graph (TiVA KG) (RQ2).
• To describe the implementation of federated query against the TiVA KG and BACI Knowl-
edge Graph (BACI KG) to enrich value and trade flows data with data about products
(RQ2).
The paper is structured as follows. An overview of the TiVA indicators with four and three
dimensions is provided in section 2. Related work on monitoring supply chain resilience is
discussed in Section 3 following by Section 4, which describes the VTF KG. Within this section,
the sub section 4.1 describes the VTF ontology. Section 5 describes the implementation of the
REST API for querying the TiVA KG, and the federated query against the TiVA KG and the
BACI KG to enrich value and trade flows results with information about products. The Section
6 presents conclusion and future work. The Appendix section lists all the concept and role
inclusion axioms of the VTF knowledge base (KB) and federated SPARQL query.
2. Trade in value-added origin indicators in a nutshell
This section provides a brief description of four and three dimensional TiVA indicators [10].
The Economic Cooperation and Development (OECD) published a guide to TiVA [10, 11] that
outlines how to measure trends in global value chains. Table 1 provides a summary of selected
indicators with four and three dimensions used in this paper. The indicator code name is given
at the top of each column. Each TIVA dimension consists of a country code (C) or an industry
code (I). The tuples (C, I), (I), and (C) denote the country code or the industry code or both
that belong to one of the TIVA perspectives. For each indicator, the number of country and
industry codes should be equal to the number of dimensions. For example, the four dimensional
indicator fdva_bsci (see Table 1) has two country code values and two industry code values. In
this paper, all indicators have their value expressed in the USD currency and the year is set to
2018.
Table 1
Selected TiVA indicators with four and three dimensions (C=country code, I=industry code).
indicator code name fdva_bsci exgr_bsci imgr_bsci fd_exgr_va exgr_dva
number of dimensions 4 4 4 4 3
value added origin (C,I) (C,I) (C) (C)
exports (C,I) (C,I) (C,I) (C,I)
imports (C) (I)
final demand (C,I) (C)
value USD USD USD USD USD
year 2018 2018 2018 2018 2018
The code name fdva_bsci refers to the origin of value added in final demand, which is a
four-dimensional indicator. It shows how the value of final demand and services consumed
within a country is derived from the accumulation of values produced by several industries in
different countries [10]. The value added origin and the final demand are determined by the
country and industry codes.
The origin of value added in the gross exports dataset, identified by the code name exgr_bsci,
provides estimates of the total gross exports grouped by each exporting industry in a country.
The estimates are broken down by the value added generated by the originating industry and
country [10]. Both value added origin and gross exports are determined by the respective
country and industry codes.
The origin of the value added in gross imports with the code name imgr_bsci links the country’s
imports with the country of origin of the exports of goods and services of the exporting country
[10]. In this indicator, the value added origin and imports are determined by country code, but
exports are determined by the country and industry codes.
The gross exports by origin of the value added and final destination, denoted by the code name
fd_exgr_va, shows the value added from the source country that is embodied in the exports of
an exporting country that ends up in the final destination country [10]. In this indicator, the
value added origin and final demand are described by the source country code, and exports are
identified by the country and industry codes.
The domestic value added content of gross exports is a three dimensional indicator with the
code exgr_dva (see page 19 in [10]). This indicator is described by country and industry codes in
the export dimension and the country code in the import dimension. It means that the industry
of an exporting country to the partner country in the import dimension represents the exported
value added generated in the economy of the exporting country. This indicator excludes intra
regional trade and intra regional value added flows [10].
All indicators of global flows of goods and services can be linked in more than two hundred
billion combinations as described on page 15 of the TiVA guide [10]. For example, the origin of
value added is the Chilean copper industry. German exports of auto parts embodied Chilean
copper. The Chinese automotive industry imports German auto parts. Finally, the European
Union has a final demand for cars assembled in China.
3. Related work
Several studies have used knowledge graphs and visualization approaches to monitor supply
chain resilience. A recently published market convergence prediction framework [12] uses
the chain knowledge graph to improve supply chain management through network resilience
experiments. The knowledge graph facilitates cross-domain information connectivity for better
decision making. The framework visualizes the interconnections and collaborative relationships
between companies in each industry [12].
A knowledge graph-based risk management framework (SCRM) [13] for supply chain resilience
is developed. The framework includes a knowledge graph for monitoring risks and long-term
disruptions. The constructed knowledge graph contains 2.5 million entities. The framework
applies knowledge retrieval, data visualization analysis, risk monitoring, and early warning to
supply chain risk management.
Many knowledge graphs suffer from incompleteness, which affects link prediction. To predict
missing information and identify critical entities in the supply network, the knowledge graph
completion methods are applied to link prediction[14].
4. Value and trade flows knowledge graph
The main purpose of this section is to describe the VTF KG and to address research questions
RQ1 and RQ3. The VTF KG is federation of TiVA KG and the BACI KG. The VTF KG comprises
of:
• The VTF ontology that is available at https://schema.coypu.org/vtf/1.4.
• The TiVA and the ISIC Rev. 4 industry code thesauruses, and the mappings between them.
• Individual assertions of VTF ontology derived from TiVA CSV files. The TiVA KG SPARQL
endpoint is available at https://tiva.coypu.org/tiva.
• The BACI KG created by CoyPu partners.
4.1. The VTF ontology
This section describes how to encode the TiVA [10] indicators shown in Table 1 into a VTF KB
and to address research question RQ1. All four dimensional indicators presented in Table 1 have
a tree structure, which graphical representation is available in the CoyPu GitLab repository 1 .
To elegantly express the TiVA indicators with four and three dimensions in the VTF KB, we use
a Description Logic (DL) syntax [15]. The Appendix section specifies the Concept Inclusion (CI)
and Role Inclusion (RI) axioms, including domain and range restrictions on role names in the
VTF KB.
The tеrm trade in value added refers to a set of indicators used to understand global production
networks and supply chains [10]. These indicators are divided into several groups according
to the number of dimensions. The VTF KB implements GrossExports, OriginOfValueAdded-
FinalDemand, OriginOfValueAddedGrossExports, OriginOfValueAddedGrossImports, Domestic-
ValueAddedContentOfGrossExports as concept names. These concept names are subsumed by
the TradeInValueAdded concept name, as expressed in the CI from 8 to 13 in the Appendix
section. These concept names are expressed as the range side of the corresponding role names,
as expressed in the CI 23 through 25, and 31 in the Appendix section.
The term industry sector classification scheme refers to a systematic approach to assigning
classifiers to organizations based on their industry sector codes. To express this term in the VTF
KB, we use the concept name IndustrySectorClassificationScheme from the Financial Industry
Business Ontology (FIBO) [16]. The term ISIC Rev.4 is expressed in the VTF KB as the ISIC4
concept name and is subsumed by the IndustrySectorClassificationSchema concept name (see CI
21 and 22 in the Appendix section).
Figure 1: N-ary relation ontology design pattern to model domestic value added content of gross
exports (exgr_dva code name).
The VTF ontology is derived from the VTF KB and it is implemented using the OWL2 language
[17] to support integration and semantic querying of different data sources. Concept names from
the VTF KB are implemented as classes in the VTF ontology and role names are implemented
as object properties. The VTF ontology currently contains 16 classes, 15 object properties, and
23 logical axioms. However, datatype properties have not yet been implemented. The ontology
is being reused to implement the domestic value added content of gross exports indicator (see
exgr_dva code in Table 1), within the COY ontology [18]. There is a n-ary relation between
trade and domestic value added content of gross exports terms that is problematic to represent in
ontology using binary relations because there are quantitative values describing this relation that
are type of decimal number or string [9]. To overcome this challenge, the n-ary relation ontology
1
https://gitlab.com/coypu-project/coy-ontology/-/tree/main/ontology/indicators
design pattern is used [9, 19] by creating the concept name Trade and the role name hasTrade
(see CI 3, 19, 20 and 28, RI 36 and 37 in the Appendix section). The concept names TradeAmount
(CI 7 in the Appendix section), Product (CI 2 in the Appendix section), TradeValue (CI 5 in
the Appendix section) and the role names hasTradeAmount, hasTradeProduct, hasTradeValue,
including range restrictions for these role names are created to express quantitative values of
Trade (see CI 15, 16, 17, 18, 30, 33, 34 in Appendix section). The Trade concept name is specified
using reflexivity restriction on isTrade role name (see CIs 19 and 20 in the Appendix section). To
link DomesticValueAddedContentOfGrossExport, TradeAmount, Product and TradeValue concept
names, the RI 36, 37, 38, 39, and 40 in the Appendix section are created by using isTrade ,
hasTradeAmount, hasTradeProduct, hasTradeValue role names.
The evaluation of the VTF ontology includes tests for accuracy, completeness, computational
efficiency, consistency, and coherence [20]. The accuracy test has been passed, and there are
no illegal re-declarations of entities within the VTF ontology. However, the completeness test
has not been passed, because the full list of value added origin indicators [10] has not been
implemented. The VTF ontology implements four indicators with four dimensions among more
than 40 of the indicators listed in Table 3.1 in [10]. The computational efficiency test shows that
the DL expressivity of the VTF ontology is equivalent to 𝐴𝐿𝑅𝐼 DLs, which is between DL-Lite
[21] and 𝑆𝑅𝑂𝐼 𝑄 DLs [22]. This means that the HermiT [8] reasoner is able to classify the VTF
ontology. The reasoner detects that the ontology is consistent and coherent.
4.2. TiVA and ISIC Rev. 4 industry code thesauruses
This section describes the implementation of thesauruses for the TiVA and the ISIC Rev. 4
industry sector codes using an ontology-based approach, including the implementation of map-
pings between them. This subsection addresses research question RQ3. The Simple Knowledge
Organization System (SKOS) [23] is used to serialize mapping between the thesauruses. The
result of this implementation is:
• The TiVA industry sector codes thesaurus.
• The ISIC Rev. 4 industry sector codes thesaurus.
• Automatically produced mapping between these two thesauruses.
The CoyPu GitLab repository 2 provides the complete implementation and an explanation of
how to reproduce the listed thesauruses and mappings. We start by using Table A.3, which was
published in [10], and the ISIC Rev. 4 document, which is available in [24]. Table A.3 provides a
comprehensive list of all TiVA industry sector codes and their correspondence to ISIC Rev. 4
industry sector codes. The rationale for the approach used in this section is twofold. It can be
used to produce and validate mappings between any other industry codes that are not explicitly
given, as shown in Table A.3. The other reason is that the ISIC Rev. 4 industry sector codes
may change over time and this generic solution can be used to produce and validate mappings
between the TiVA industry sector codes and the ISIC Rev. 4 industry sector code based on these
changes.
2
https://gitlab.com/coypu-project/coy-ontology/-/tree/main/ontology/mapping
Figure 2: The ontology development workflow for the ISIC Rev. 4 and the TiVA industry sector codes.
Figure 2 shows the implementation workflow. The first task is to automatically generate
SKOS-based thesaurus from ISIC Rev. 4 industry sector codes 2 . This is achieved by mapping
from the ISTC Rev. 4 CSV file to the corresponding TTL file using RDFizer [25]. The resulting
SKOS-based thesaurus is validated against SKOS shapes by using the TopBraid SHACL [26]
engine. In the next step, marked with number 2 in Figure 2, the TiVA SKOS-based thesaurus is
automatically created and validated against the SKOS shapes. Table A.3 published in [10] shows
the correspondence between the TiVA industry sector codes and the ISIC Rev. 4 industry sector
codes, but does not show the semantic relations between these two sets of industry codes. In
this work, these semantic relations are automatically generated by the LogMap [27] matching
tool. The tool accepts the ISIC Rev. 4 and TiVA as source and target thesauruses respectively.
As a result, the tool generates 38 mappings between the source and target thesauruses. The
LogMap tool did not produce conflictive mappings between these two thesauruses. Each
mapping contains a SKOS concept from the ISIC Rev. 4 thesaurus, a SKOS concept from the
TiVA thesaurus, the type of mapping between these two SKOS concepts, the mapping direction,
and the mapping confidence. This information is stored in a CSV file available in the CoyPu
Gitlab repository 2 . In the 4th and 5th steps shown in Figure 2, the RDFizer tool converts the
CSV file containing information about mappings into a TTL file. The resulting TTL file is
validated against the SKOS shapes using the TopBraid SHACL engine.
4.3. Implementation TiVA KG
The TiVA KG is implemented by processing the raw data, which are available as CSV files [6]
and representing four dimensional indicators listed in Table 1. The raw data consists of industry
and country code names, which are string types, and value and year, which are numbers. The
Python code transforms raw data into TTL files consisting of individual assertions, which are
instances of the VTF ontology schema.
Table 2
Size of ontologies generated for each indicator shown in Table 1
Indicator code name ontology size
fdva_bsci 60GB
exgr_bsci 54GB
imgr_bsci 67GB
fd_exgr_va 76GB
Table 2 summarizes the size of the ontology files generated for each TiVA indicator raw
file. The final step is to load generated TTL files, VTF ontology schema, TiVA and ISIC Rev. 4
industry sector code thesauruses, and the produced and validated mappings between these two
thesauruses [28] into a remote triple store. The size of the VTF KG is 257GB and it contains
1128749054 triples.
4.4. Implementation of the BACI KG
Data on the domestic value added content of gross exports (exgr_dva code name) three dimen-
sional indicator are stored in the BACI KG3 . The BACI KG is populated from the international
trade at product level database [7]. It contains information on exporting and importing country
codes, product names, product codes, trade amount value, trade amount, year of trade. The
BACI KG contains 185251116 triples and was created by CoyPu project partners.
5. Implementation of the REST API to query VTF KG
In this section, we discuss the design and implementation of the REST API to query VTF KG. It
addresses research question RQ2. The pipeline consists of two main building blocks, namely
Backend, Data Storage , which are shown in Figure 3.
5.1. Backend
The output of the Backend module is a JSON file as a result of querying VTF KG by executing
parameterized SPARQL queries against a remote TiVA or BACI SPARQL endpoints [28]. The
Swagger interface4 allows users to interact with the API implemented in the Backend module by
passing parameters for each operation. Users can also implement their own client-side solutions
using this RESTful API.
Figure 4 shows an example of a parameterized SPARQL query. The parameter in this query
is a trade location, highlighted in red, of the value added origin in the origin of value added
in gross imports indicator. The result of this query is a JSON file consisting of exports trade
location, exports industry code, imports trade location, value and year for origin of value added in
gross imports.
3
https://skynet.coypu.org/#/dataset/coypu-internal/query
4
https://service.tib.eu/sandbox/tiva/swagger-ui/index.html
Figure 3: Querying VTF KG via REST API.
Figure 4: A SPARQL query to fetch exports trade location, exports industry code, imports trade location,
value and year for origin of value added in gross imports indicator.
5.2. Federated query against TiVA KG and BACI KG
The aim of the previous sections was to present our work with regard to the integration of data
sources. This section addresses research question RQ2, essentially how we can utilise integrated
data sources. As a proof of example, we have created a federated SPARQL query, available
in Appendix section, to get information about product trade information, about location of
exporting and importing country, product code and name and value (in the USD currency).
The implementation of the federated query involves a SPARQL query executed program-
matically against the two SPARQL endpoints of TIVA KG and BACI KG (see Data Storage
component shown in Figure 3). A JSON object as a result of the execution of this federated
query contains the value, year, exporting and importing trade location of the imgr_bsci indicator
available in TiVA KG (see 27 and 28 rows in the federated SPARQL query). These exporting
and importing trade locations must match the importing and exporting trade locations of the
exgr_dva indicator available in BACI KG (see rows 41 and 42 in the federated SPARQL query).
Based on this match, the resulting JSON object also contains the product name, product code,
quantity value and year of trade available in BACI KG.
6. Conclusions and future work
In this work all research questions are addressed. This paper shows valuable results that are
the basis for deeper analysis of international trade flows via building more federated SPARQL
queries and implementation of a dashboard that should dynamically generate charts using
implemented REST API. The OECD forum 5 recently discussed four key issues 6 for resilient
supply chains. To address one of these issues, this paper presents VTF KG and a RESTful API.
The OECD discusses the need to implement policies that strengthen the resilience of supply
chains. One of the key policy actions is to determine government role, which includes the
international exchange of information 7 . This paper addresses this issue by enabling services
and tools to share information about trade flows using a RESTful API and to perform federated
queries against TiVA and BACI knowledge graphs. We observe gaps in this work that should be
addressed in further development such as to infer missing information in the VTF KG and to
solve the incompleteness of the VTF ontology.
Appendix
Value and trade flows (VTF) knowledge base (KB) expressed in Description Logic (DL) syntax:
Concept inclusion (CI) axioms
1. Exports ⊑ ⊤
2. Product ⊑ ⊤
3. Trade ⊑ ⊤
4. Imports ⊑ ⊤
5. TradeValue ⊑ ⊤
6. FinalDemand ⊑ ⊤
7. TradeAmount ⊑ ⊤
8. TradeInValueAdded ⊑ ⊤
9. GrossExports ⊑ TradeInValueAdded
5
https://www.oecd.org/trade/resilient-supply-chains/
6
https://www.sustainablesupplychains.org/blog/four-keys-to-resilient-supply-chains/
7
https://www.oecd.org/trade/resilient-supply-chains/determine-government-role/
10. DomesticValueAddedContentOfGrossExports ⊑ TradeInValueAdded
11. OriginOfValueAddedFinalDemand ⊑ TradeInValueAdded
12. OriginOfValueAddedGrossExports ⊑ TradeInValueAdded
13. OriginOfValueAddedGrossImports ⊑ TradeInValueAdded
14. ValueAddedOrigin ⊑ ⊤
15. DomesticValueAddedContentOfGrossExports ⊑ ∃hasTrade.Trade
16. ∃hasTrade.Trade ⊑ DomesticValueAddedContentOfGrossExports
17. Trade ⊑ ∃hasTradeAmount.TradeAmount ⊓ ∃hasTradeProduct.Product ⊓
∃hasTradeValue.TradeValue ⊓ ∃hasTradeValueAdded.TradeInValueAdded
18. ∃hasTradeAmount.TradeAmount ⊓ ∃hasTradeProduct.Product ⊓
∃hasTradeValue.TradeValue ⊓ ∃hasTradeValueAdded.TradeInValueAdded ⊑ Trade
19. Trade ⊑ ∃isTrade.Trade
20. ∃isTrade.Trade ⊑ Trade
21. IndustrySectorClassificationScheme ⊑ ⊤
22. ISIC4 ⊑ IndustrySectorClassificationScheme
Domain and range restrictions on role names
23. ⊤ ⊑ ∀hasExport.Exports
24. ⊤ ⊑ ∀hasFinalDemand.FinalDemand
25. ⊤ ⊑ ∀hasTradeValueAdded.TradeInValueAdded
26. ⊤ ⊑ ∀hasImport.Imports
27. ⊤ ⊑ ∀hasIndustryCode.IndustrySectorClassificationScheme
28. ⊤ ⊑ ∀hasTrade.Trade
29. ∃hasTrade.⊤ ⊑ TradeInValueAdded
30. ⊤ ⊑ ∀hasTradeAmount.TradeAmount
31. ∃hasTradeLocation.⊤ ⊑ TradeInValueAdded
32. ⊤ ⊑ ∀hasTradeLocation.⊤
33. ⊤ ⊑ ∀hasTradeProduct.Trade
34. ⊤ ⊑ ∀hasTradeValue.TradeValue
35. ⊤ ⊑ ∀hasValueAddedOrigin.ValueAddedOrigin
Role inclusion axioms
36. hasTrade ⊑ hasTradeValueAdded−1
37. hasTradeValueAdded ⊑ hasTrade−1
38. hasTradeValueAdded−1 ∘ isTrade ∘ hasTradeProduct ⊑ hasTradeInValueAddedProduct
39. hasTradeValueAdded−1 ∘isTrade∘hasTradeAmount ⊑ hasTradeInValueAddedTradeAmount
40. hasTradeValueAdded−1 ∘ isTrade ∘ hasTradeValue ⊑ hasTradeInValueAddedTradeValue
Federated SPARQL query
PREFIX r d f : < h t t p : / / www. w3 . o r g / 1 9 9 9 / 0 2 / 2 2 − r d f − s y n t a x − ns #> 1
PREFIX v t f : < h t t p s : / / schema . coypu . o r g / v t f #> 2
PREFIX r d f s : < h t t p : / / www. w3 . o r g / 2 0 0 0 / 0 1 / r d f − schema #> 3
PREFIX s k o s : < h t t p : / / www. w3 . o r g / 2 0 0 4 / 0 2 / s k o s / c o r e #> 4
PREFIX coy : < h t t p s : / / schema . coypu . o r g / g l o b a l #> 5
SELECT DISTINCT ? v a o I m p o r t V a l u e ? v a o I m p o r t Y e a r ? e x T r a d e L o c a t i o n 6
? importLocation ? import ? export ? productMatch ? productLabel 7
? amountYear ? amountValue ? v a l u e 8
WHERE { 9
GRAPH < h t t p s : / / d a t a . coypu . o r g / t r a d e / b a c i / > { 10
? e x g r d v a r d f : type v t f : ExgrDva . 11
? exgrdva v t f : hasImport ? import . 12
? exgrdva v t f : hasExport ? export . 13
? exgrdva v t f : hasTrade ? t r a d e . 14
? trade v t f : hasTradeProduct ? product . 15
? product rdfs : l a b e l ? productLabel . 16
? product skos : exactMatch ? productMatch . 17
? t r a d e v t f : hasTradeAmount ? tradeAmount . 18
? tradeAmount coy : h a s Y e a r ? amountYear . 19
? tradeAmount coy : h a s V a l u e ? amountValue . 20
? t r a d e coy : h a s T r a d e V a l u e ? t r a d e V a l u e . 21
? t r a d e v a l u e coy : h a s V a l u e ? v a l u e . 22
FILTER ( s t r ( ? amountYear ) = ' 2 0 1 8 ' ) . 23
} 24
SERVICE < h t t p s : / / t i v a . coypu . o r g / t i v a > { 25
{ 26
SELECT ? v a o I m p o r t V a l u e ? v a o I m p o r t Y e a r ? e x T r a d e L o c a t i o n 27
? importLocation 28
WHERE { 29
? imgrbsci a v t f : ImgrBsci . 30
? i m g r b s c i coy : h a s V a l u e ? v a o I m p o r t V a l u e . 31
? i m g r b s c i coy : h a s Y e a r ? v a o I m p o r t Y e a r . 32
? i m g r b s c i v t f : h a s E x p o r t ? ex . 33
? imgrbsci v t f : hasImport ? imgrImport . 34
? imgrImport v t f : hasTradeLocation ? importLocation . 35
? ex r d f : type v t f : E x p o r t . 36
? ex v t f : h a s T r a d e L o c a t i o n > ? e x T r a d e L o c a t i o n . 37
} 38
} 39
} 40
FILTER ( s t r ( ? i m p o r t ) = s t r ( ? i m p o r t L o c a t i o n ) && 41
s t r ( ? export )= s t r ( ? exTradeLocation ) ) . 42
} LIMIT 1 0 ; 43
Acknowledgments
The research has received funding from the Federal Ministry for Economic Affairs and En-
ergy of Germany in the project Cognitive Economy Intelligence Plattform für die Resilienz
wirtschaftlicher Ökosysteme - CoyPu (project number 01MK21007[A-L]).
References
[1] R. C. Johnson, G. Noguera, A portrait of trade in value-added over four decades, The
Review of Economics and Statistics 99 (2017) 896–911. doi:10.1162/REST_a_00665 .
[2] X. Gong, E. Keeble, D. Boko, A. Deveza, Building afciot and tiva indicators in africa
in support of afcfta (2023). URL: https://iioa.org/conferences/29th/papers/files/4853_
PaperonAfCIOT_2023-05-15.pdf.
[3] World input-output database project, 2024. URL: www.wiod.org, accessed February 28th,
2024.
[4] The eora global supply chain database, 2024. URL: https://worldmrio.com/, accessed Febru-
ary 28th, 2024.
[5] Global trade analysis project (gtap), 2024. URL: https://www.gtap.agecon.purdue.edu/,
accessed February 28th, 2024.
[6] Trade in value added origin principal indicators raw data, 2024. URL: https://stats.oecd.org/
DownloadFiles.aspx?HideTopMenu=yes&DatasetCode=TIVA_2021_C1, accessed Febru-
ary 21st, 2024.
[7] G. Gaulier, S. Zignago, BACI: International Trade Database at the Product-Level. The
1994-2007 Version, Working Papers 2010-23, CEPII, 2010. URL: http://www.cepii.fr/CEPII/
en/publications/wp/abstract.asp?NoDoc=2726.
[8] B. Motik, P. F. Patel-Schneider, B. Parsia, C. Bock, A. Fokoue, P. Haase, R. Hoekstra,
I. Horrocks, A. Ruttenberg, U. Sattler, et al., Owl 2 web ontology language: Structural
specification and functional-style syntax, W3C recommendation 27 (2009) 159.
[9] P. Hayes, C. Welty, Defining n-ary relations on the semantic web. w3c working group
note, 12 april 2006, World Wide Web Consortium. http://www. w3. org/TR/swbp-n-
aryRelations/(last visited December 27, 2012) (2006).
[10] OECD, Guide to oecd’s trade in value added (tiva) indicators, 2018 edition, 2019. URL:
https://stats.oecd.org/fileview2.aspx?IDFile=88076c6d-d86a-4f18-8cd9-923c26fb4c98.
[11] J. M. Guilhoto, C. Webb, N. Yamano, Guide to oecd tiva indicators, 2021
edition (2022). URL: https://www.oecd-ilibrary.org/science-and-technology/
guide-to-oecd-tiva-indicators-2021-edition_58aa22b1-en.
[12] S. Zhou, Y. Liu, Y. Liu, A market convergence prediction framework based on a supply
chain knowledge graph, Sustainability 16 (2024). URL: https://www.mdpi.com/2071-1050/
16/4/1696.
[13] Y. Yang, C. Peng, E.-Z. Cao, W. Zou, Building resilience in supply chains: A knowledge
graph-based risk management framework, IEEE Transactions on Computational Social
Systems (2023) 1–9. doi:10.1109/TCSS.2023.3334768 .
[14] Y. Liu, B. He, M. Hildebrandt, M. Buchner, D. Inzko, R. Wernert, E. Weigel, D. Beyer,
M. Berbalk, V. Tresp, A knowledge graph perspective on supply chain resilience, arXiv
preprint arXiv:2305.08506 (2023).
[15] F. Baader, I. Horrocks, C. Lutz, U. Sattler, An Introduction to Description Logic, Cambridge
University Press, 2017. doi:10.1017/9781139025355 .
[16] M. Bennett, The financial industry business ontology: Best practice for big data, Journal
of Banking Regulation 14 (2013) 255–268.
[17] M. Smith, I. Horrocks, M. Krotzsch, B. Glimm (eds.), OWL 2 Web Ontology Language con-
formance (second edition), 2012. URL: https://www.w3.org/TR/owl2-overview/, accessed
February 21st, 2024.
[18] A. Schaefer, E. Hoerster, F. Engel, J. Reineke, J. Rathschlag, L. Michaelis, L. Bühmann,
M. Weber, N. Weissmann, N. Arndt, N. Krdzavac, P. Ulrich, R. Usbeck, S. Gründer-Fahrer,
S. Bin, T. Grabo, Coypu ontology, 2023. URL: https://schema.coypu.org/global/2.3, accessed
February 26st, 2024.
[19] M. Giunti, G. Sergioli, G. Vivanet, S. Pinna, Representing n-ary relations in the semantic
web, Logic Journal of the IGPL 29 (2021) 697–717.
[20] H. Hlomani, D. Stacey, Approaches, methods, metrics, measures, and subjectivity in
ontology evaluation: A survey, Semantic Web Journal 1 (2014) 1–11.
[21] D. Calvanese, G. De Giacomo, D. Lembo, M. Lenzerini, R. Rosati, Tractable reasoning and
efficient query answering in description logics: The dl-lite family, Journal of Automated
reasoning 39 (2007) 385–429.
[22] I. Horrocks, O. Kutz, U. Sattler, The even more irresistible sroiq., Kr 6 (2006) 57–67.
[23] A. Miles, J. R. Pérez-Agüera, Skos: Simple knowledge organisation for the web, Cataloging
& Classification Quarterly 43 (2007) 69–83. doi:10.1300/J104v43n03\_04 .
[24] U. N. D. of Economic, S. A. S. Division, International standard industrial classification
of all economic activities (isic) rev.4, 2008. URL: https://unstats.un.org/unsd/publication/
seriesm/seriesm_4rev4e.pdf, accessed February 21st, 2024.
[25] E. Iglesias, S. Jozashoori, D. Chaves-Fraga, D. Collarana, M.-E. Vidal, Sdm-rdfizer: An
rml interpreter for the efficient creation of rdf knowledge graphs, in: Proceedings of the
29th ACM international conference on Information & Knowledge Management, 2020, pp.
3039–3046.
[26] H. Knublauch, An open source implementation of the w3c shapes constraint language
(shacl) based on apache jena., 2023. URL: https://github.com/TopQuadrant/shacl, accessed
February 27th, 2024.
[27] E. Jiménez-Ruiz, B. Cuenca Grau, Logmap: Logic-based and scalable ontology matching,
in: The Semantic Web–ISWC 2011: 10th International Semantic Web Conference, Bonn,
Germany, October 23-27, 2011, Proceedings, Part I 10, Springer, 2011, pp. 273–288.
[28] L. Buehmann, Oecd tiva knowledge graph sparql endpoint, 2024. URL: https://tiva.coypu.
org/tiva, accessed February 26st, 2024.