Scientific advancement is often reliant on good quality resources that provide the necessary scaffolding to support the scientific publications. Theoretical, analytical, and empirical aspects of the Semantic Web are considered to produce innovative and high quality resources.
The ESWC 2022 Resources Track aims to promote on the one had innovative or novel and on the other hand sharable and reusable resources that contributed to the generation of scientific work including, but not limited to:
Datasets, knowledge graphs and annotated corpora,
Papers that describe the production or consumption (e.g., methodology, algorithm) of datasets or knowledge graphs to support specific tasks.
Ontologies, vocabularies and ontology design patterns
Papers that describe the modeling process underlying the creation of ontologies or vocabularies of particular interest to the community.
Workflows, services and APIs
Papers that describe research prototypes or services supporting a given research hypothesis.
Evaluation benchmarks or methods and replication studies
Papers that describe benchmarks focusing on datasets and algorithms for comprehensible and systematic evaluation of existing and future systems, and evaluation methodologies and their demonstration in experimental studies.
Papers that describe machine learning models that would impact the knowledge engineering community, e.g., comprehensive word embeddings trained on large corpora, or embeddings of commonly known knowledge graphs, e.g., DBpedia or Wikidata, and software frameworks that can be extended or adapted to support scientific study and experimentation.
The program committee will consider both the resource and the paper and they will review both the research and the technical aspects. Resources will be evaluated along the following generic review criteria which should be carefully considered both by authors and reviewers.
- Is the resource new or original?
- Is the resource novel or innovative?
- Is the resource well presented?
- Is the paper clear and of high quality?
- Is the resource positioned with respect to the state of the art?
- Is the resource compared to the state of the art? (if meaningful)
- Scientific quality:
- Is the methodology, modeling choices and functionality sound and well motivated?
- Are the domain, modeling problems and requirements clearly described and significantly cover the domain?
- Is the resource’s design and coverage clear, reasonable and logically correct?
- Are the advantages, complexities and limitations well described?
- Is the resource validated in real use cases?
- If the resource is a benchmark, do its metrics measure something significant, relevant and important?
- Is the resource of interest to the Semantic Web community?
- Is the resource of interest to society in general?
- Will the resource have an impact, especially in supporting the adoption of Semantic Web technologies?
- Is the resource relevant and sufficiently general, does it measure some significant aspect or plug an important gap?
- Does the resource break new ground?
- How does the resource advance the state of the art? Has the resource been compared to other existing resources (if any) of similar scope?
- Is the resource easy to (re)use? For example, does it have good quality documentation? Are there tutorials available?
- Is there potential for extensibility to meet future requirements (e.g., upper level ontologies, plugins in protege)?
- Does the resource clearly explain how the data and software can be used? Does the resource provide detailed documentation?
- Does the resource description clearly state what the resource can and cannot do, and the rationale for the exclusion of some functionality?
Design & Technical quality:
- Does the design of the resource follow resource specific best practices?
- Did the authors perform an appropriate re-use or extension of suitable high-quality resources? For example, in the case of ontologies, authors might extend upper ontologies and/or reuse ontology design patterns.
- Does the resource provide an appropriate description (both human and machine readable), thus encouraging the adoption of FAIR principles? Is there a schema diagram? For datasets, is the description available in terms of VoID/DCAT/DublinCore?
- Does it use open standards, when applicable, or have good reason not to?
- If the resource proposes performance metrics, are such metrics sufficiently broad and relevant?
- If the resource is a comparative analysis or replication study, was the coverage of systems reasonable, or were any obvious choices missing?
- Is the resource (and related results) published at a persistent URI
(PURL, DOI, w3id)?
- Does the resource provide a (preferably open) license specification?
(See creativecommons.org, opensource.org for more information).
- Is the resource publicly available?
For example as API, Linked Open Data, Download, Open Code Repository.
- Is the resource publicly findable? Is it registered in (community) registries (e.g. Linked Open Vocabularies, BioPortal, DataHub, or DBpedia Databus)? Is it registered in generic repositories such as FigShare, Zenodo or GitHub?
- Is there a reasonable sustainability and maintenance plan specified for the resource?
- Is the resource (and related results) published at a persistent URI
We welcome papers on well established as well as emerging publicly available resources.
- ESWC will not accept work that is under review or has already been published in or accepted for publication in a journal, another conference, or another ESWC track.
- The proceedings of this conference will be published in Springer’s Lecture Notes in Computer Science series. The preprints of the accepted papers will be available openly.
- Abstracts need to be pre-submitted followed by a timely submission of the full work.
- Papers must not exceed 15 pages (with unlimited references) and in English.
- Submissions must be either in PDF or in HTML, formatted in the style of the Springer Publications format for Lecture Notes in Computer Science (LNCS). For details on the LNCS style, see Springer’s Author Instructions. For HTML submission guidance, see the HTML submission guide.
- The Resource Track follows a single blind reviewing process.
- Authors will have the opportunity to submit a rebuttal to the reviews to clarify questions posed by program committee members.
- At least one author per contribution must register for the conference for presentation.
- Change of authors after acceptance is not allowed.
- Submission will be done through Easychair. When logging in select the appropriate track.
Authors must ensure easy access to the resource during the review process, ideally by the resource being cited at a permanent location specific for the resource. For example,
links to datasets and other materials used for evaluation and software source code.
The links need to be provided after the abstract and they should include:
Resource type: … → The type of the resource presented in the paper
License: … → The license of the resource presented in the paper
DOI: … → The DOI of the resource presented in the paper
URL: … → The URL of the resource presented in the paper
An example of such a list can be seen in this paper:
- data available in a repository such as FigShare, Zenodo, or a domain specific repository.
|December 8, 2022
|December 18, 2022 (extended)
|Opening of rebuttal period
|January 30, 2023
|Closing of rebuttal period
|February 6, 2023
|Notification to authors
|February 23, 2023
|Camera-ready papers due
|March 23, 2023
All deadlines are 23:59 anywhere on earth (UTC-12).
Delineation from the other Tracks
We strongly recommend that prospective authors carefully check the calls of the other main tracks of the conference in order to identify the optimal track for their submission.
Papers that propose new algorithms and architectures should continue to be submitted to the regular research track.
Papers that reuse and apply state-of-the art semantic technology or resources in practical settings should be submitted to the in-use track (i.e., the novelty falls into the in-use application of the semantic technology or resource).
Authors who want to present an interesting industry application but who do not want to submit a full paper should submit to the industry track.
Papers describing concrete resources (datasets, ontologies, vocabularies, annotated corpora, workflows, knowledge graphs, evaluation benchmarks, etc.) should be submitted to the resources track.
Note that research, in-use and resource papers are published within the same proceedings by Springer’s Lecture Notes in Computer Science series.
The text from this CfP is partially based on the call for Resource Papers for ESWC 2022 by Catia Pesquita and Hala Skaf-Molli, which relied on previous calls, including the call for Resource Papers for ESWC 2021 by Maria Maleshkova and Pierre-Antoine Champin, the call for Resource Papers for ESWC2019 by Amrapali Zaveri and Alasdair J. G. Gray, and the call for Resource Papers for ESWC2020 by Heiko Paulheim and Anisa Rula.