28 Mar
2022
28 Mar
'22
4:29 p.m.
**
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*Postdoc position at MISTEA, INRAE Montpellier, France – Semantic Web,
Data linking*
*Areas:Semantic Web, Linked Data, Data linking, Representation learning *
*Qualifications: PhD in Informatics, AI. Background in knowledge
engineering. *
*Context:ANR DACE-DL
<https://anr.fr/Projet-ANR-21-CE23-0019>(DAta-CEntric AI-driven Data
Linking)*
*Contact & Collaboration:*
*Danai Symeonidou, danai.symeonidou(a)inrae.fr
<mailto:danai.symeonidou@inrae.fr>*
*Clement Jonquet, clement.jonquet(a)inrae.fr
<mailto:clement.jonquet@inrae.fr>*
*Dates:Position available for 2 years. Beginning date is flexible. *
*Location:INRAE, Centre Occitanie-Montpellier, MISTEA
<https://www6.montpellier.inrae.fr/mistea/>research unit*
*Salary:Between 2200€ and 2700€ gross monthly depending on
qualifications and situation. *
*Institut: INRAE is the French research organization in agriculture,
food and environmental sciences; it is a pioneer in France in terms of
data sharing and Open Science commitment. The MathNum research
department gathers around 200 scientists in mathematics and digital
technologies in 13 research units in France. MISTEA is a joint research
unit of INRAE and Montpellier Institut Agro engineering school with
activities in the development of mathematical, statistical and
informatics methods dedicated to analysis and decision support for
agronomy and environment. The team is also recognized for its expertise
in knowledge engineering and ontology-based scientific data management
and information systems.*
*Project context: Data linking is the scientific challenge of
automatically establishing typed links between the entities of two or
more structured datasets. A variety of complex data linking systems
exists, evaluated on public benchmarks [1,2,3]. While they have allowed
for the generation of vast amounts of linked data in the context of
various dedicated projects, data generic systems often have limited
applicability in many real-world scenarios, where data are highly
heterogeneous and domain-specific. The ANR project DACE-DL (2022-2024)
targets a paradigm shift in the data linking field with a data-centric
bottom-up methodology relying on machine learning and representation
learning models [4]. We hypothesize there exists a finite number of
identifiable and generalisable linking problem types (LPTs), that we
need to categorize and analyze to provide better linking results. *
*Topic:The postdoc will work to identify and provide a
categorisation/taxonomy of the different linking problem types based on
an in-depth analysis of the linked datasets provided by the project and
beyond. The first objective is to provide an in-depth analysis of the
linked data available along with an exhaustive study of the
state-of-the-art in the field of data linking. A finite number of
generalisable linking problem types will be classified including the
relations and inherent structure of the LPTs made explicit to both human
and machine. The goal is to answer questions such as: are certain LPTs
or groups of LPTs (e.g. siblings at a given level of the taxonomy)
specific to a domain, language or a community? Are certain LPTs inherent
to specific types of data? Once a formal taxonomy of LPTs is produced,
various datasets will be manually annotated. These annotations on
existing pairs of datasets will be used to learn, using machine learning
strategies, features for the automatic categorization of other datasets.
The postdoc will co-supervise a PhD student working on the machine
learning methods.*
*Application: Send application to the contact emails including:*
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*a short description of introducing yourself *
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*your adequacy to the position *
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*a CV and *
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*one major publication*
*References*
*[1] M. Nentwig, (...) E. Rahm. A survey of current link discovery
frameworks, Semantic Web, 2017.*
*[2] Euzenat, J., (...), Trojahn, C. Ontology matching benchmarks:
generation, stability, and discriminability. Web Semantics, 2013.*
*[3] Zhou, L, (...), Trojahn, C., Zamazal, O: Towards evaluating complex
ontology alignments. Knowl. Eng. Rev., 2020.*
*[4] Todorov, K. Datasets First! A Bottom-up Data Linking Paradigm. ISWC
2019 Satellite Tracks, Auckland, New Zealand, October 26-30, 2019.*
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