Knowledge-Based Systems: Scopus Indexing, Scope, Metrics & Submission Guide

Knowledge-Based Systems (KBS) is an Elsevier journal focused on artificial intelligence, knowledge-based and data-driven intelligent systems, and methods that support prediction, decision-making, reasoning, optimization, and practical AI applications. Elsevier describes it as an international and interdisciplinary journal that publishes original research spanning both theory and practical study.

The journal is indexed in Scopus and Science Citation Index Expanded (SCIE) according to Elsevier’s journal insights. The supplied Scopus source record identifies Source ID 24772. As of a 2026 Elsevier journal page, KBS displays a CiteScore of 13.7 and an Impact Factor of 7.6. Because metric pages can update at different times, authors should reconfirm the latest values before using them for university, promotion, funding, or PhD requirements.

For researchers considering KBS, the central question is not only whether a paper uses AI, but whether it makes a clear contribution to knowledge-based or AI-technique-based systems and demonstrates technical novelty, rigorous evaluation, and relevance to the journal’s current research community.

Knowledge-Based Systems Journal Information at a Glance

Metric note: Elsevier pages cached at different times can display different CiteScore values. A 2026 KBS page displays 13.7, while some older cached pages display 15.0. For formal reporting, always use the latest value shown in the database required by your institution.

What This Page Covers

• Knowledge-Based Systems aims, scope, subject coverage, and likely manuscript fit.
• Scopus and other major indexing information that could be verified.
• Current publisher-displayed journal metrics and how to interpret changing values cautiously.
• Article types visible in recent journal issues and special-issue material.
• Open-access options, APC information, and subscription publishing.
• Peer review, editorial process, and publisher-reported timelines.
• Practical manuscript-preparation and submission-readiness checks.
• Ethical ways Contentxprtz can support language, formatting, references, and publication readiness without promising acceptance.

Methodology and Sources Used for This Journal Profile

This guide was prepared by cross-checking the supplied Scopus source record with authoritative Elsevier and ScienceDirect pages for Knowledge-Based Systems, including the journal overview, journal insights, editorial board, current 2026 issue information, and Elsevier publishing-ethics policies. Where an exact journal-specific value or rule could not be confirmed from an authoritative live source, it is marked for verification rather than estimated.

Journal metrics, publication charges, editorial teams, indexing coverage, and author instructions can change. Authors should reconfirm time-sensitive details on the official journal site and the database relevant to their institution immediately before submission.

What Is Knowledge-Based Systems?

Knowledge-Based Systems is an international AI journal designed around systems that use knowledge-based and other artificial-intelligence techniques. The publisher’s scope explicitly connects these systems to human prediction and decision-making through data science and computational methods, while emphasizing both theoretical development and practical study.

The journal is therefore broader than classic rule-based expert systems. Its current scope reaches machine learning, data science, recommender systems, intelligent decision support, computational intelligence, data-driven optimization, cognitive interaction, brain–computer interfaces, and knowledge-based computer vision. Recent 2026 articles also show active work in knowledge-aware recommendation, large-language-model knowledge retrieval, knowledge-graph completion, image fusion, and reinforcement-learning applications.

For journal selection, this matters: a manuscript does not need to be about a traditional knowledge base, but it should make a credible contribution to an intelligent system, model, method, or computational framework aligned with the journal’s AI and knowledge-oriented objectives.

Knowledge-Based Systems Aims and Scope

Elsevier states that KBS publishes original, innovative, and creative research in knowledge-based and AI-technique-based systems. Its objectives include supporting prediction and decision-making, balancing theoretical and practical work, and encouraging new intelligent models, methods, systems, and software tools.

Core subject areas highlighted by the publisher

• Machine-learning theory, methodology, and algorithms.
• Data-science theory, methodologies, and techniques.
• Knowledge representation/presentation and knowledge engineering.
• Recommender systems and e-service personalization.
• Intelligent decision-support, prediction, and warning systems.
• Computational-intelligence systems.
• Data-driven optimization.
• Cognitive interaction and brain–computer interfaces.
• Knowledge-based computer-vision techniques.

The scope also allows applications in areas such as business, government, education, engineering, and healthcare. That multidisciplinary application reach is useful, but the application domain alone is not enough. A strong KBS manuscript should explain why its intelligent-system contribution matters beyond the immediate dataset or case study.

Research that may be less aligned

As an editorial interpretation rather than an official rejection rule, papers may be less competitive if they offer only routine implementation, use a standard model with minimal methodological contribution, provide weak comparisons, omit important baselines or ablations, or focus on an application without demonstrating a meaningful knowledge-based or AI-systems advance. Authors should compare their manuscript with recent KBS papers to judge the expected novelty and evaluation depth.

Who Should Consider Submitting to KBS?

KBS may be relevant to researchers in artificial intelligence, machine learning, knowledge engineering, intelligent decision systems, recommender systems, optimization, computational intelligence, knowledge graphs, computer vision, and interdisciplinary AI applications. It can also be relevant to PhD scholars and professional research teams when their work has a clear scholarly contribution and sufficiently rigorous experimental or theoretical support.

A practical fit test is to ask three questions. First, is the manuscript’s main contribution an AI/knowledge-based systems contribution rather than only a domain application? Second, can the authors identify several recent KBS papers that address a similar methodological or problem family? Third, does the manuscript provide enough evidence—comparative experiments, robustness checks, theoretical justification, ablation, statistical analysis, or real-world validation—to support its claims?

Scopus Indexing, SCIE Visibility, and Journal Metrics

Elsevier’s journal insights list Scopus and Science Citation Index Expanded among the journal’s abstracting and indexing services. The source record supplied for this task identifies Scopus Source ID 24772, which is the most useful identifier when journals have similar titles.

A 2026 Elsevier KBS page displays a 13.7 CiteScore and an Impact Factor of 7.6. Older cached publisher pages still show a 15.0 CiteScore, illustrating why metrics should be attached to a reporting year and checked immediately before they are used in a formal claim. The current SJR, SNIP, and quartile were not independently verified from an authoritative live database record during preparation of this page, so this guide does not invent them.

For PhD or institutional publication requirements, do not rely on a screenshot or third-party ranking site alone. Confirm the journal title, ISSN, Scopus Source ID, active coverage status, and the exact metric or quartile year required by your university.

Publication Frequency and Current Activity

Elsevier’s shop page lists 24 annual issues, while recent ScienceDirect records show a high publication volume and continuing 2026 issues. Volume 341, dated 23 May 2026, contains research articles and a special issue on causal inference for learning and applications. This confirms active publication in 2026.

Because KBS publishes frequently, authors have a substantial body of recent material to use for fit analysis. Before submitting, review papers from the last 12–24 months rather than relying only on older landmark articles; this gives a better view of present methodological expectations, terminology, datasets, baselines, and emerging themes.

What Types of Articles Does Knowledge-Based Systems Publish?

Recent ScienceDirect issues prominently classify papers as Research articles. The journal also publishes editorial material, including special-issue editorials, and its special-issue guidance states that guest editors may prepare an editorial or a field survey after a special issue is completed.

The exact list of submission article types can change in the online submission system. Authors should therefore select their intended article type only after checking the current KBS Guide for Authors and submission portal. This guide does not infer a complete article-type menu from examples in published issues.

Peer Review and Editorial Process

Elsevier describes Knowledge-Based Systems as a peer-reviewed academic journal. Its special-issue instructions state that papers submitted to special issues undergo peer review and that the Editor-in-Chief makes final acceptance decisions after editorial recommendations. Articles in press on ScienceDirect are described as accepted, peer-reviewed articles that are awaiting assignment to a volume or issue.

The journal’s publisher-reported insight figures currently show 3 days from submission to first decision, 52 days from submission to a decision after review, 155 days from submission to acceptance, and 5 days from acceptance to online publication. These are journal-level statistics, not promises for an individual manuscript. Review time can vary substantially by topic, reviewer availability, revisions, and editorial assessment.

Professor Jie Lu of the University of Technology Sydney is listed as Editor-in-Chief. The board also includes senior and associate editors across multiple countries, consistent with the journal’s international positioning.

Open Access, Subscription Publishing, and APC

KBS supports both open-access and subscription publishing. Elsevier currently lists an open-access article publishing charge of USD 3,350, excluding taxes. The amount may be reduced where an institutional agreement, funding arrangement, or other applicable discount exists.

For the subscription option, Elsevier states that no publication fee is charged to authors and the article is immediately available to subscribers. Authors should distinguish this from optional open-access charges and should verify the current price at the point of submission or acceptance, because APCs and agreements can change.

Never treat payment of an APC as increasing the probability of acceptance. Editorial decisions should be independent of commercial considerations, and Elsevier’s editorial-independence policy states that editorial decision-making is kept separate from commercial interests.

Manuscript and Submission Readiness

The current KBS Guide for Authors should be treated as the final authority for formatting and submission requirements. Because the complete live journal-specific guide was not reliably retrievable for every detailed field during preparation of this profile, the points below are framed as publication-readiness checks, not as invented KBS-specific rules.

Before submission, verify the journal-specific requirements

• Accepted article type and any length or structure rules.
• Required title-page information and corresponding-author details.
• Abstract format, word limit, and keyword requirements.
• Whether highlights or a graphical abstract are required or encouraged.
• Figure resolution, file formats, table formatting, equations, and supplementary files.
• Reference and citation style, DOI formatting, and data/software citation expectations.
• Declaration of competing interests, funding disclosures, data availability, ethics statements, and author-contribution declarations.
• Any anonymization requirements associated with the journal’s current peer-review model.

Strengthen the manuscript for KBS specifically

As a practical publication-readiness step, state the knowledge-based or AI-systems contribution in the title, abstract, introduction, and conclusion without exaggeration. The introduction should establish a precise gap and distinguish the proposed method from the closest recent work. The experimental section should explain datasets, preprocessing, baselines, hyperparameters, evaluation metrics, and statistical or robustness checks sufficiently for expert assessment.

For model-based work, ablation studies, sensitivity analysis, computational-cost discussion, and failure-case analysis often help show that claimed improvements are attributable to the proposed contribution rather than an incidental experimental choice. If explainability, fairness, privacy, safety, or human decision support is central to the application, address those issues directly rather than treating them as peripheral.

Journal-Fit Checklist for Knowledge-Based Systems

Common Reasons a Manuscript May Be Unsuitable

KBS does not publish a simple public checklist of rejection reasons on the pages used for this profile. However, researchers can reduce avoidable mismatch by checking for common selection problems: weak connection to the journal’s AI/knowledge-systems scope, incremental novelty, insufficient experimental comparison, unsupported performance claims, poor reproducibility, unclear writing, weak discussion of limitations, or failure to engage with recent literature.

A technically correct paper can still be a poor journal fit. If the manuscript’s main contribution belongs more naturally to a domain-specialist venue—such as a clinical, manufacturing, finance, education, or networking journal—authors should compare the benefits of a specialist audience with KBS’s AI-systems audience before deciding.

Publication Ethics and Responsible AI Use

Knowledge-Based Systems is published under Elsevier’s broader publishing policies. Elsevier’s ethics guidance covers duties of authors, reviewers, editors, and the publisher, including originality, appropriate authorship, disclosure of competing interests, accurate reporting, proper citation, and protection of the scholarly record. Elsevier also makes Crossref Similarity Check available across its editorial systems.

Where research involves human participants, personal data, biological materials, or other regulated research contexts, authors should follow applicable laws and institutional approvals and include the statements required by the publisher and journal. Permissions are also required for copyrighted third-party content where applicable.

Elsevier maintains specific generative-AI policies for journal publishing. Authors using AI-assisted tools should check the current policy and journal Guide for Authors before submission, particularly around disclosure, authorship responsibility, confidentiality, and the distinction between language assistance and substantive scientific responsibility.

How to Prepare a Stronger KBS-Aligned Manuscript

1. Map the paper to the scope. Write a one-sentence explanation of how the manuscript advances a knowledge-based or AI-technique-based system.
2. Audit recent KBS literature. Review several 2025–2026 papers in the closest methodological area and compare contribution depth, baselines, datasets, and presentation.
3. Clarify novelty. Separate what is new in the architecture/method from what is new only in the application setting.
4. Strengthen evidence. Add relevant baselines, ablation, robustness, statistical analysis, computational considerations, and limitations where appropriate.
5. Improve reproducibility. Define datasets, splits, preprocessing, training conditions, parameter choices, software versions, and evaluation procedures.
6. Check claims and language. Make sure each major claim is supported by a result, citation, proof, or appropriately qualified interpretation.
7. Reformat only after fit is established. Avoid spending time on detailed journal formatting until scope and contribution fit have been assessed.
8. Run a final policy check. Reconfirm current author guidelines, fees, declarations, ethics, AI-use disclosure, and submission files on the official site.

Official Sources and Final Verification

• Knowledge-Based Systems — official ScienceDirect journal page
• Knowledge-Based Systems — journal insights
• Knowledge-Based Systems — editorial board
• Knowledge-Based Systems — policies and guidelines
• Scopus Source ID 24772
• Elsevier publishing ethics

Authors should treat the official journal website and current Scopus/Clarivate records as the final source of truth for time-sensitive details. If an institutional rule requires a particular quartile, indexing year, Impact Factor year, or Scopus coverage period, verify that exact field rather than relying on a general journal profile.

Conclusion: Should You Submit to Knowledge-Based Systems?

Knowledge-Based Systems is a well-established Elsevier AI journal with verified Scopus and SCIE visibility, active 2026 publication, and a broad but clearly AI-centered scope. It is most suitable when the manuscript’s main contribution advances a knowledge-based, machine-learning, data-science, optimization, computational-intelligence, or intelligent-decision system and supports that contribution with rigorous evidence.

Before submission, verify the current Guide for Authors, article type, metric year, quartile if required, fee route, ethics declarations, and submission files. Then compare the manuscript with recent KBS papers to judge whether its novelty and evaluation depth are competitive for the journal’s present research community.

At Contentxprtz, we don’t just edit; we help ideas reach their fullest potential—through ethical, researcher-led support that improves clarity and publication readiness without replacing editorial judgment or promising acceptance.