=Paper=
{{Paper
|id=Vol-3293/paper53
|storemode=property
|title=A Reconciliation Framework for the Integration of Stocks and Fisheries Information
|pdfUrl=https://ceur-ws.org/Vol-3293/paper53.pdf
|volume=Vol-3293
|authors=Yannis Marketakis,Yannis Tzitzikas,Aureliano Gentile,Bracken van Niekerk,Marc Taconet
|dblpUrl=https://dblp.org/rec/conf/haicta/MarketakisTGNT22
}}
==A Reconciliation Framework for the Integration of Stocks and Fisheries Information==
https://ceur-ws.org/Vol-3293/paper53.pdf
A Reconciliation Framework for the Integration of Stocks and
Fisheries Information
Yannis Marketakis 1, Yannis Tzitzikas 1,2, Aureliano Gentile 3, Bracken van Niekerk 3 and
Marc Taconet 3
1
Institute of Computer Science, FORTH-ICS, Heraklion, Greece
2
Computer Science Department, University of Crete, Heraklion, Greece
3
Food and Agriculture Organization of the United Nations, Rome, Italy
Abstract
Fisheries management relies on analyzing data using complex models and software and
includes the usually manual process of identifying and combining different parts of information
about stocks and fisheries, which is a time-consuming and error-prone process. Firstly because
there is no single source of information but rather they are many, and secondly because there
are alternative ways of modeling and referring to the same piece of information. Approaches
like the Global Record of Stocks and Fisheries (GRSF), which are the result of the semantic
data integration of the corresponding information from different data sources, aim to overcome
such problems, by providing a unified view of the stocks and fisheries information in a
homogeneous manner. In this paper, we propose a reconciliation framework ensuring that
similar pieces of information from heterogeneous sources are properly connected during the
construction of the semantic warehouse of GRSF.
Keywords 1
Reconciliation, Entity Matching, Stock, Fishery, Species, Water Area, Fishing Gear
1. Introduction
The main goal of fisheries management is to monitor, specify and propose regulations and rules for
protecting the fishery resources, so that their sustainable exploitation is possible. Although there is no
clear and generally accepted definition of fisheries management, according to [1] the main task of
fisheries management is the integrated process of information gathering, analysis, planning,
consultation, decision-making, allocation of resources and formulation and implementation, with
enforcement as necessary, of regulations or rules which govern fisheries activities in order to ensure the
continued productivity of the resources and the accomplishment of other fisheries objectives. The key
indicators for efficient fisheries management are stocks and fisheries. Stocks refer to groups or
individuals of a species occupying a well-defined spatial range (e.g. swordfish in the Mediterranean
Sea), while fisheries describe the activities leading to the harvesting of the fish within a particular area,
using a particular method or equipment and purpose of activity (e.g. the Atlantic cod fishery in the area
of East and South Greenland).
Nowadays there are several stakeholders, reporting information about stocks and fisheries at
regional, national and local levels. These stakeholders maintain their knowledge and publicize their
contents independently from each other. However, cross-references between the knowledge bases from
different stakeholders is not a common issue. In addition, the use of common vocabularies or standards
Proceedings of HAICTA 2022, September 22–25, 2022, Athens, Greece
EMAIL: marketak@ics.forth.gr (A. 1); tzitzik@ics.forth.gr (A. 2); aureliano.gentile@fao.org (A. 3); bracken.vannieker@fao.org (A. 4);
marc.tacone@fao.org (A. 5)
ORCID: 0000-0002-0417-2526 (A. 1); 0000-0001-8847-2130 (A. 2); 0000-0002-6542-132x (A. 3); 0000-0001-8537-3305 (A. 4); 0000-
0002-3103-6204 (A. 5)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
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for describing particular aspects has not been globally agreed. This leads to the creation of several data
silos, each one with its own rules and terminology.
The Global Record of Stocks and Fisheries (GRSF) [5], aims to overcome such problems by
introducing a workflow that collects and semantically integrates stocks and fisheries information from
different databases, and present them in a unified manner. More specifically, it is the result of the
integration of (a) FIRMS [2], (b) RAM [3] and (c) FishSource [4]. During the construction and
refreshment [6] of GRSF, a reconciliation workflow is applied, ensuring that if there are similar
resources expressed in a different way they will be properly linked. In the following of the paper we
describe such cases. More specifically, in Section 2, we further discuss about the problem, in Section 3
we elaborate with the reconciliation framework and its applicability, and Section 4 concludes our work.
2. Motivation
Since the sources contributing stocks and fisheries information to GRSF contain complementary
information, GRSF provides a merged view of those records. Merging is applied by comparing some
key elements of the records. For the case of stock records, the elements that are compared are: species
and assessment area, while for the case of fisheries they are: species, fishing area, management
authority, fishing gear, flag state. It becomes evident that the accuracy of the information of these
elements, specify the successful merging or not.
Of course, there is not a single way of describing those elements. For example, marine species can
be referred to using their scientific name (e.g. Thunnus albacares), their common name in any language
(e.g. Yellowfin tuna in English), their 3-Alpha code (YFT), their APHIA ID (e.g. 127027)), etc. In a
similar manner, an area can be referred to using a common name, a FAO major area code (e.g., 37.3.1),
a GFCM code, an LME code, an ISO3 code of the exclusive economic zone, etc. The same applies for
all the aforementioned elements.
Moreover, it is quite common that different sources use different terminologies for describing their
contents. For example, in FIRMS species are usually referred to using their 3-Alpha codes, RAM reports
their scientific name and taxonomy, and FishSource uses various combinations. It is therefore evident
that before applying any merging activity in GRSF, it is required to build a knowledge base, with the
different identifiers of each resource, so that they can be efficiently compared.
3. Reconciliation Framework
The reconciliation framework is responsible for constructing a knowledge base consisting of the
different identifiers of the entities. The knowledge base can be afterwards exploited during the
construction of the GRSF, so that (a) the merging of records from different sources can be executed
properly and (b) all the records in GRSF use the preferred terminologies with respect to GRSF
guidelines. Below we introduce the conceptual backbone of the constructed knowledge base, we
describe the reconciliation framework workflow and we provide more information about the
applicability of the framework for the purposes of GRSF.
3.1. Model
The configuration of the conceptual model for the reconciliation framework of GRSF is shown in
the upper part of Figure 1. The core element is Term, and is associated with the elements Identifier and
Information Object. More specifically, a Term can have a preferred identifier, one or more alternative
identifiers, and many additional information objects. In order to compare two different terms, the
identifier elements are used. In particular, if two terms have at least one of their identifiers the same
then they are merged into one. For this reason, we are comparing all the available identifiers and a
match is confirmed if all the values of an identifier match (i.e. identifier, type, index). For terms not
having any identifier, we are comparing their additional information objects. The main difference is
that when two terms have the same additional information object (same value and type), and at least
one of them does not have any identifier, a suggestion of merging the terms is made and is it up to the
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user to approve it. The lower part of Figure 1 shows an indicative example for the term about the species
with scientific name “Thunnus albacares”.
Figure 1: The Reconciliation framework data model
Identifier elements are enough for comparing and merging two terms. Practically, this means that as
soon as two terms have at least one of their identifiers the same then they are matched, no matter if the
identifier is the preferred or an alternative one. Furthermore, each identifier is associated with an index,
representing the order of preference. Upon the construction of all the terms, they can be used for
selecting the most preferred identifier of a term, starting from the preferred identifier (that has always
index =1), and if that does not exist, the alternative identifier with the lowest index is used. In the
example of Figure 1 the preferred identifier is YFT (3-Alpha code), the second most preferred is 127027
(APHIA ID), and so on. The types and indexes of the identifiers are specified during the configuration
of the GRSF construction/refresh workflow. An indicative configuration of the types and indexes of the
identifiers for GRSF is given in Section 3.3.
3.2. Workflow
Figure 2 shows the overall workflow that is used for constructing GRSF and the placement of the
reconciliation workflow. The detailed description of the construction workflow and the refreshment of
GRSF can be found in [5] and [6]. More specifically, the reconciliation KB is constructed using as input
the transformed data as well as data from external sources. For each concrete resource, a new entry is
added in the knowledge base with the available information. Of course it is expected that some
information might be missing during the creation of a new terminology resource. The reconciliation
framework will take care of complementing the missing information as soon as they exist somewhere
else (e.g. in another record, a transformed source, or an external source). Particularly, if a term with a
particular identifier already exist, then its information are complemented with the new ones, otherwise
a new term is created.
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Figure 2: The GRSF construction workflow
Figure 3 illustrates an example depicting three stock records, occupying the same species, and each
source documents that species differently. S1 will create a new term of entity type Species, with
preferred identifier “YFT”, and additional identifier “Thunnus albacares” with type scientific name. S2
instead of creating a new term, it will enhance the already created term shown before, because they
have the same additional identifier of type scientific name. Finally, S3 will further enhance the term
with an additional identifier.
Figure 3: Creating and enhancing a term
After the construction of the reconciliation KB, it is exploited during the construction of the GRSF
records. This is mandatory, so that records are fully compliant with the GRSF guidelines, that specify
how the different information of GRSF records should be presented. This is achieved through the lookup
methods, that allow spotting terms from the KB. As soon as a term is located, its preferred identifier is
used for the construction of the corresponding GRSF record. If it does not exist, the alternative identifier
with the lowest index will be used.
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3.3. Application
Table 1 shows the list of term types that are reconciled using the reconciliation framework for the
purposes of GRSF. For each term type, we provide the preferred identifier, and the alternative ones, as
they have been agreed in GRSF guidelines.
Table 1 The term types and their identifiers
Term Type Preferred ID / Index Alternative IDs / Index
Species 3-Alpha[7] code / 1 APHIA[8] ID / 2
Scientific Name / 3
Areas (assessment, fishing) FAO[9] code / 1 GFCM[12] / 2
LME[10] / 3
MRGID[11] / 4
ISO-3[13] (EEZ) / 5
Fishing Gear ISSCFG[14] code / 1 ISSCFG abbrev. / 2
ISSCFG category / 3
Flag State ISO-3 / 1 Legal name / 2
Management Authority Acronym / 1 Full name / 2
4. Conclusion
This paper demonstrates the reconciliation framework that facilitates the semantic data integration
of data sources with stocks and fisheries and is compliant with the GRSF guidelines and best practices.
Although we focus on the particular use case, the framework is generic enough and can be used across
different domains as well. In fact, in this paper we have described its configuration on top of the GRSF
construction workflow, with the objective of efficiently comparing and locating similar entities that
have been described using different identifiers.
5. Acknowledgements
This work has received funding from the European Union’s Horizon 2020 innovation action
BlueCloud (Grant agreement No 862409)
6. References
[1] K. L. Cochrane and S.M. Garcia, A Fishery Manager's Guidebook, John Wiley & Sons, 2009.
[2] Fisheries and Resources Monitoring System, 2022. URL: http://firms.fao.org/firms/en
[3] The RAM Legacy Stock Assessment Database, 2022. URL: https://www.ramlegacy.org/
[4] Sustainable Fisheries Partnership, FishSource 2022. URL: https://www.fishsource.org/
[5] Y. Tzitzikas, Y. Marketakis, N. Minadakis, M. Mountantonakis, L. Candela, F. Mangiacrappa, P.
Pagano, C. Perciante, D. Castelli, M. Taconet, A. Gentille, G. Gorelli, Towards a Global Record
of Stocks and Fisheries, 8th International Conference on Information and Communication
Technologies in Agriculture, Food & Environment, September 21-24, 2017, Chania, Crete,
Greece.
[6] Y. Marketakis, Y. Tzitzikas, A. Gentille, Bracken Van Niekerk, and Marc Taconet, On the
Evolution of Semantic Warehouses: The Case of Global Record of Stocks and Fisheries, 14th
International Conference on Metadata and Semantics Research, Special Track on Metadata &
Semantics for Agriculture, Food & Environment (MTSR'20) Madrid, 2020.
[7] ASFIS List of Species for Fishery Statistics Purposes. URL:
https://www.fao.org/fishery/en/collection/asfis/en
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[8] W. Appeltans, M.J. Costello, B. Vanhoorne, W. Decock, L. Vandepitte, F. Hernández, J. Mees, E.
Vanden Berghe, E., 2008. Aphia for a World Register of Marine Species (WoRMS). VLIZ Special
Publication.
[9] FAO Major Fishing Areas. URL: https://www.fao.org/fishery/en/area/search
[10] Large Marine Ecosystems (LME). URL: https://lmehub.net/
[11] Marine Regions. URL: https://www.marineregions.org/
[12] General Fisheries Commision for the Mediterranean (GFCM). URL:
https://www.fao.org/gfcm/about/en/
[13] ISO 3166. URL: https://www.iso.org/iso-3166-country-codes.html
[14] The International Standard Statistical Classification of Fishing Gear (ISSCFG). URL:
https://data.apps.fao.org/catalog/dataset/the-international-standard-statistical-classification-of-
fishing-gear-isscfg
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