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CIKM 2021: The 30th ACM International Conference
on Information and Knowledge Management

1 - 5 November 2021, Online
(Queensland, Australia)

http://www.cikm2021.org
https://easychair.org/conferences/?conf=cikm2021
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The Conference on Information and Knowledge Management (CIKM) provides a unique venue for industry and academia to present and discuss state-of-the-art research on artificial intelligence, search and discovery, data mining and database systems, all at a single conference. CIKM is uniquely situated to highlight technologies and insights that materialize the big data and artificial intelligence vision of the future. CIKM 2021 will take place online in a lively and interactive manner.

CIKM recognises the importance of publicly available scientific resources, such as datasets, evaluation benchmarks, tools or libraries as a necessary and important prerequisite for fostering research in core CIKM areas.

The Resources Track seeks submissions from both academia and industry that describe resources available to the community. Resources include, but are not restricted to, information retrieval test collections, labelled datasets for machine learning, and software tools and services. Furthermore, we also seek papers describing original research on building datasets and resources for broad use as well as the lessons learnt in doing this.

An ideal resource track paper will fit into one or more of the following categories:
* Describing a new and innovative dataset or protocol
* To support research on novel application domains;
* To support novel evaluation tasks;
* Created using novel methods and/or algorithms;
* Labeled using novel and well-described annotation and/or crowdsourcing approaches;
* Reusable research prototypes and services;
* Open software frameworks, tools and libraries which support tasks and evaluation in data science, data engineering or information & knowledge management.

Historically, CIKM has published research in all of these areas. The goal of the Resource Track is to highlight artifacts of CIKM research and the process of making those artifacts available to the community. It is expected that at the time of submission the described resource will be available under reasonably liberal terms and sufficiently well-documented such that reviewers may consult that documentation as they conduct their reviews. (This of course means that reviews will be single-blind for this track.)
AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of your conference. The official publication date affects the deadline for any patent filings related to published work. (For those rare conferences whose proceedings are published in the ACM Digital Library after the conference is over, the official publication date remains the first day of the conference.)

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Key Dates
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Paper Submission Deadline:

June 16, 2021 (anywhere in the world)

Acceptance Notification:

August 9, 2021 (anywhere in the world)

Final Version Submission:

Aug 23, 2021 (anywhere in the world)

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Topics of Interest
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We encourage submissions of high-quality research papers on all topics in the general areas of artificial intelligence, data science, databases, information retrieval, and knowledge management. Topics of interest include, but are not limited to, the following areas:

* Data and information acquisition and preprocessing (e.g., data crawling, IoT data, data quality, data privacy, mitigating biases, data wrangling)

* Integration and aggregation (e.g., semantic processing, data provenance, data linkage, data fusion, knowledge graphs, data warehousing, privacy and security, modeling, information
credibility)

* Efficient data processing (e.g., serverless, data-intensive computing, database systems, indexing and compression, architectures, distributed data systems, dataspaces, customized hardware)

* Special data processing (e.g., multilingual text, sequential, stream, spatio-temporal, (knowledge) graph, multimedia, scientific, and social media data)

* Analytics and machine learning (e.g., OLAP, data mining, machine learning and AI, scalable analysis algorithms, algorithmic biases, event detection and tracking, understanding, interpretability)

* Neural Information and knowledge processing (e.g., graph neural networks, domain adaptation, transfer learning, network architectures, neural ranking, neural recommendation, and neural prediction)

* Information access and retrieval (e.g., ad hoc and web search, facets and entities, question answering and dialogue systems, retrieval models, query processing, personalization, recommender and filtering systems)

* Users and interfaces for information and data systems (e.g., user behavior analysis, user interface design, perception of biases, personalization, interactive information retrieval, interactive analysis, spoken interfaces)

* Evaluation, performance studies, and benchmarks (e.g., online and offline evaluation, best practices)

* Crowdsourcing (e.g. task assignment, worker reliability, optimization, trustworthiness, transparency, best practices)

* Understanding multi-modal content (e.g., natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge graphs, and knowledge representations)

* Data presentation (e.g., visualization, summarization, readability, VR, speech input/output)

* Applications (e.g., urban systems, biomedical and health informatics, legal informatics, crisis informatics, computational social science, data-enabled discovery, social media)

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Paper Submissions
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Resource track papers should not exceed 9 pages plus unlimited references, using the ACM sigconf template, see https://www.acm.org/publications/proceedings-template. Papers should include a description of the resource, an illustration of the use case(s) for the resource, explain its utility, reusability and novelty, and focus on anticipating and answering questions that CIKM reviewers and readers are likely to ask, as laid out in the review criteria below. It is expected that papers describing datasets clearly explain how the data was collected, all stages of processing from the source to the resulting dataset, and assumptions and risks presented by the collection and/or labeling strategy.
The resource paper review process is single-blind, which means that reviewers are aware of the names and affiliations of paper authors. Therefore, unlike other paper tracks in CIKM 2021, there is no need to hide author information in the submission.

Papers should be submitted through the CIKM online submission system, using the “Resource Track”.

Accepted resources track papers will be published within the official CIKM2021 proceedings as part of the ACM digital library.

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Ethics
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Resources are expected to be available as described, where “available” means that most researchers in our community could obtain and make use of the resource without strongly limiting the research they can perform with it. Datasets are expected to be collected in accordance with institutional review board standards and ACM standards of ethics. Reviewers are instructed to not use their reviews as an advocacy platform for these issues, but to do what they can to help authors bring their resources to fruition.

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Review Criteria
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Novelty
* What is new about this resource?
* Does the resource represent an incremental advance or something more dramatic?

Availability
* Is the resource available to the reviewer at the time of review?
* Are there discrepancies between what is described and what is available?
* Are the licensing/terms of use sufficiently open as to allow most academic and industry researchers access to the resource?
* If the resource is data collected from people, do appropriate human subjects control board procedures appear to have been followed?

Utility
* Is the resource well documented? What level of expertise do you expect is required to make use of the resource?
* Are there tutorials or examples? Do they resemble actual uses or are they toy examples?
* If the resource is data, are appropriate tools provided for loading that data?
* If the resource is data, are the provenance (source, preprocessing, cleaning, aggregation) stages clearly documented?

Predicted Impact
* What CIKM research activity is enabled by the availability of this resource?
* Does the resource advance a well-established research area or a brand new one?
* Do you expect that this resource will be useful for a long time, or will it need to be curated or updated? If the latter, is that planned?
* How large is the (anticipated) research user community? Will that grow or shrink in the next few years?

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Dual Submission Policy
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It is not allowed to submit papers that are identical (or substantially similar) to versions that have been previously published, or accepted for publication, or that have been submitted in parallel to other conferences. Such submissions violate our dual submission policy. There are several exceptions to this rule:

* Submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published.

* Submission is permitted for papers that have previously been made available as a technical report (or similar, e.g., in arXiv). In this case, the authors should not cite the report, so as to preserve anonymity.

It is also NOT permitted to double submit the content to both resource track and other track(s) of CIKM 2021 (e.g. a resource paper for building Dataset A and a full paper containing the construction process of Dataset A in the experiment section).

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ACM Policy Against Discrimination
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All authors and participants must adhere to the ACM discrimination policy.
For full details, please visit this site:

https://www.acm.org/special-interest-groups/volunteer-resources/officers-manual/policy-against-discrimination-and-harassment

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PC Chair Contact Information
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For more information, contact the appropriate PC chairs:
Resource Paper Track Email: cikm2021-resource@easychair.org

Nicola Ferro, University of Padua, Italy
Zhenhui Jessie Li, Pennsylvania State University, USA