The document discusses the integration of semantic web technologies and blockchain in the agrifood supply chain to enhance data transparency and trust among numerous stakeholders. It introduces 'linked pedigrees,' which utilize semantic principles to create audit trails for tracing products, highlighting the importance of data integration in preventing food crises. Additionally, the potential of blockchain technologies is examined as a means to address trust issues in centralized data storage and improve supply chain visibility.

1. Semantic blockchains
in the supply chain
Christopher Brewster
Aston University
C.A.Brewster@aston.ac.uk

2. Outline
• Information integration in the agrifood supply chain
• Semantic web technologies
• Semantic Web technologies in the Food System: Linked
Pedigrees
• Some limitations
• Blockchain technologies
• Integrating Semantics and the Blockchain - some initial
thoughts

3. The Problem

4. The Food Supply Chain
• From Farm to Fork
• The agri-food system
includes much more
• More and more parts of
this supply chain and
agri-food system are
leaving digital trace … in
James Scott’s terms
becoming more “legible”.

5. Data - Information -
Knowledge
• The food supply chain involves hundred of
actors, thousands of processes, millions of
products and (potentially) billions of data points!
• Children believe milk comes from supermarkets!
• Too much or too little data?
• Why do we need it?

6. Characteristics of Supply Chain
• Large numbers of participants
• Heterogeneity of participants
• Huge variety in ICT uptake
• Poor information flow (need to know attitude)
• Only one up, one down data flow
• Solved by regulation and certification

7. Food supply chain is …
• A highly heterogenous loosely coupled large-scale
network of entities with variable but largely minimal
degrees of communication and trust between the actors

8. Drivers for Data Integration
• Need for transparency - tracking and tracing
• Desire for food awareness - on the part of
consumers, but not only
• Regulatory pressure - e.g.EU Regulation
1169/2011
• New business opportunities ….

9. Food Crises and Scandals
• Major driver for greater data integration (whether
open or closed).
• E. Coli in Germany in 2011 - spanish growers
lost over €200M
• Horsemeat scandal across Europe in 2013 -
impact very great on some supermarkets

10. Lack of Data Integration
• Both scandals suffered from lack of data and data
integration
• E. Coli - who affected? what purchased? where?
when? and who participated in the supply chain
• Horsemeat - six months for Irish FSA to map the
supply chain network
• Need for greater supply chain transparency =
need for data integration

11. Tracking, tracing and
Visibility
• Core demand is to make tracking and tracing
easy, AKA supply chain “visibility”
• “Visibility is the ability to know exactly where
things are at any point in time, or where they
have been, and why” — GS1
• Major challenge
http://www.gs1.org/docs/GS1_SupplyChainVisibility_WhitePaper.pdf

12. Trust - 1

13. Trust - 2
•More information, more data = more trust

14. What role semantic
technologies?

15. Key Features of Semantic
Technologies
• Unique identifiers (URIs) — enables consistency and data
accretion
• Common vocabularies/ontologies/data schemata — creates
a tendency towards standardisation WITHOUT losing
flexibility
• Linking and mappings - create a natural space for new
knowledge and data integration
• Logical rigour — rules for validation and quality control can
be written

16. AKTive Food (2005)
• A bit of history
• Paper on “Semantic Web based knowledge
conduits for the Organic Food Industry”
• Centred on decision support for a restaurant
based on data crawled from semantic web
marked up websites of food producers
• Nice vision …. but no implementation

17. Linked Pedigrees
• Based on “pedigree” concept common in
pharmaceutical industry - an audit trail which record
path of ownership
• Based on GS1 standards (pedigree standard +
EPCIS)
• “Linked pedigrees” use semantic web/linked data
principles
• Involves formalisation of EPCIS standard in two
ontologies

18. Linked Pedigrees
• Datasets described and accessed using linked data principles.
• Encapsulate the knowledge required to trace and track products in
supply chains on a Web scale.
• Facilitate the interlinking of a variety of related and relevant data,
i.e., GS1 product master data with event data PLUS other data
outside the GS1 system.
• Based on a domain independent data model for the sharing of
knowledge among Semantic Web/Linked data aware systems
deployed for the tracking, tracing and data capture.
• Product knowledge shared among partners as products physically
flow downstream or upstream in the supply chain.

19. Linked Pedigree Architecture

20. Linked Pedigrees and EPCIS
• Formalisations of:
• EEM - The EPCIS Event Model
• CBV - Core Business Vocabulary
• This allows the representation of
EPCIS events on the Web of Data
• This enables sharing and
traceability of information
• Tracking of inconsistencies

21. Socio-technical Limitations
• Heterogeneity of the food system - so many
different actors, in size and shape
• Continuous changes - actors entering and
leaving the market
• Lack of trust - actors (farmers, food producers)
do not trust overarching systems
• Cost - margins in the food system are very tight

22. Key Problems
• Any form of centralised
data runs into data control
issues. Who know what?
(cf. Uber as an example)
• If each actor must keep
their triple store up and
running - data access
issues (important in food
crises)

23. What role blockchain
technologies?

24. What is the blockchain?
• A file called The Blockchain is spread over
millions of machines
• Which use proof of work and byzantine
consensus
• To provide a set of chained hashes and digital
signatures
• To create an unforgeable record of …. (e.g.) who
owns how much bitcoin

25. Blockchain - another
definition
• “A blockchain is a magic computer that anyone
can upload programs to and leave the programs
to self-execute, where the current and all
previous states of every program are always
publicly visible, and which carries a very strong
cryptoeconomically secured guarantee that
programs running on the chain will continue to
execute in exactly the way that the blockchain
protocol specifies.” — Vitalik Buterin (founder of
Ethereum)

26. Blockchain
• Developed originally as part of Bitcoin
• Provides underlying distributed ledger for Bitcoin
• HOWEVER, quite separate from Bitcoin and has
potentially many other uses
• Lots of eager uptake with many startups being
founded around this technology (Ethereum,
Bitshares, Helloblock, Ripple Labs etc.)

27. Blockchain in the supply
chain
• Not my idea! Other people have thought of this!
• Startup provenance.org wants to use the blockchain to
“tell a story” about a product from producer to end
consumer. Currently focussing on certification data!
• Still working on on what data to represent ….

28. Semantic Blockchains
• Concept: Construct an architecture where some or all of
the data involved in Linked Pedigree is held on the block
chain
• Result:
• Distributed database would resolve some trust issues
• Guarantee of continuous uptime (so if an actor
disappears, their data is still accessible)
• Rules can be written as to who has access to data
using specific governance algorithms

29. Step 1: Basic Usage
Eliminate the
problems of data
centralisation

30. Step 2: More advanced Usage
Guarantee
accessibility of data
now and in future

31. Other potential
Consequences
• Disintermediation of GS1 for product data
• Product data, tracking and tracing and supply
chain visibility at very low cost. This could be
very important for small scale producers/
developing country producers
• Standardising supply chain data schemata/
ontologies by the back door

32. Conclusions
• We have argued for the importance of data
integration in the agrifood supply chain
• We showed the applicability of semantic
technologies in the supply chain and introduced
the concept of “linked pedigrees”.
• We then suggest that blockchain technologies
could further improve this technology stack and
solve some problems e.g.lack of trust in
centralised data control

33. Thanks
• Monika Solanki for all the technical work on
Linked Pedigrees, EEM, CBV and much else
• Vinay Gupta for conversations leading to the
Semantic Blockchain ideas
• Jessie Baker for explaining provenance.org

34. References
• Christopher Brewster, Hugh Glaser, and Barny Haughton. “AKTive Food: Semantic Web based knowledge conduits for the
Organic Food Industry.” In Proceedings of the ISWC Worskshop Semantic Web Case Studies and Best Practice for
eBusiness (SWCASE 05) , 4th International Semantic Web Conference, 7 November (Galway, Ireland, 2005). URL http://
www.cbrewster.com/papers/Brewster_SWCASE.pdf
• Monika Solanki and Christopher Brewster. “OntoPedigree: A content ontology design pattern for traceability knowledge
representation in supply chains.” Semantic Web – Interoperability, Usability, Applicability (2015). URL http://goo.gl/OdUPg0
• Monika Solanki and Christopher Brewster. “Enhancing visibility in EPCIS governing Agri-food Supply Chains via Linked
Pedigrees.” International Journal on Se- mantic Web and Information Systems 10 (2014). (Impact Factor 0.393), URL http://
www.ijswis.org/?q=node/52
• Monika Solanki and Christopher Brewster. “Consuming Linked data in Supply Chains: Enabling data visibility via Linked
Pedigrees.” In Proceedings of the Fourth Inter- national Workshop on Consuming Linked Data (COLD2013), held at the
Interna- tional Semantic Web Conference (ISWC 2013), 21-25 October 2013 (2013). URL http://www.cbrewster.com/papers/
Solanki_COLD13.pdf
• Monika Solanki and Christopher Brewster. “Representing Supply Chain Events on the Web of Data.” In Proceedings of the 3rd
International Workshop on Detection, Represen- tation, and Exploitation of Events in the Semantic Web (DeRiVE 2013), , held
at the International Semantic Web Conference (ISWC 2013), 21-25 October 2013 (2013). URL http://www.cbrewster.com/
papers/Solanki_DeRiVE13.pdf
• Monika Solanki and Christopher Brewster. “EPCIS event based traceability in pharmaceu- tical supply chains via automated
generation of linked pedigrees.” In International Semantic Web Conference 2014 (ISWC 2014) (Rivo di Garda, 2014). (ISWC
accep- tance rate: 21.1% 180 full submissions, 29 accepted, 9 conditionally accepted), URL http://www.cbrewster.com/
papers/Solanki_ISWC14.pdf