Skill UI

This guide helps researchers determine if an interesting phenomenon or trend constitutes a genuine theoretical puzzle suitable for the Academy of Management Review (AMR). It guides users to distinguish between a simple topic, an empirical test, and a deep theoretical contradiction that requires building new knowledge-based claims.

A comprehensive guide for critically appraising primary academic studies (e.g., DID, IV, RDD) when writing literature reviews. It teaches how to assess the methodological credibility, robustness, and potential biases of existing evidence, ensuring the synthesis accurately reflects the field's best knowledge rather than merely counting conflicting results.

A guide for creating high-impact academic exhibits for literature reviews (e.g., Annual Reviews of Economics). It teaches how to synthesize findings into structured formats, including 'Who-found-what' summary tables, conceptual framework diagrams, and meta-evidence plots. The focus is on synthesizing existing knowledge across studies, not presenting original estimates. Essential for communicating complex, cross-field research findings to non-specialists.

This guide helps researchers determine if a psychology topic is academically mature, important, and ready for a comprehensive review article suitable for an Annual Review publication. It provides a structured framework to scope the topic, ensuring it is bounded, current, and proposes a new organizing idea rather than merely summarizing existing knowledge.

This guide provides rigorous standards for crafting academic literature reviews, particularly for specialized journals in economics. It instructs authors to move beyond simple citation listing and instead structure the review around identified research gaps. The required structure emphasizes a clear progression from 'Current Status' to 'Deficiencies' and finally to 'Novel Positioning,' ensuring the paper's marginal contribution is clearly derived from a knowledge gap.

A comprehensive guide detailing the methodology for packaging, documenting, and staging research artifacts (including code, datasets, knowledge graphs, and demo systems) for CIKM conference submission. It covers best practices for licensing, provenance tracking, versioning, and transitioning artifacts from anonymous review status to citable public releases, ensuring reproducibility and adoptability.

A comprehensive guide for designing rigorous and robust empirical programs in Information Retrieval, Data Mining, and Knowledge Management. It outlines how to meet the stringent evidence requirements for top-tier conferences, ensuring claims are substantiated across multiple evaluation dimensions (retrieval, mechanism, scalability) and incorporating best practices for reproducibility and handling modern challenges like LLM contamination.

This guide details the complex requirements for writing the 'Related Work' section for CIKM submissions. It emphasizes the need for 'three-front coverage' (Retrieval, Mining, and Knowledge Management/Databases) and provides specific instructions on writing the 'boundary-work paragraph.' It also covers critical academic hygiene, such as avoiding venue misattribution, maintaining recency baselines, and accurately stating novel contributions.

This comprehensive guide outlines best practices for achieving rigorous reproducibility in complex AI and knowledge graph research. It details how to pin every component—from tokenizers and KGs to LLM prompts and environment settings—ensuring that results are fully documented and verifiable by peers, even when proprietary data is involved.

This guide provides an in-depth analysis of the CIKM peer review process, which operates under double-blind rules and integrates perspectives from three distinct fields: Information Retrieval, Data Mining, and Knowledge Management. It details how authors can optimize their submissions to satisfy diverse expert standards, how to interpret complex, multi-faceted review reports, and the critical dynamics that determine a paper's acceptance status.

This guide helps researchers assess if their work correctly spans the tri-community area of Information Retrieval, Data Mining, and Knowledge Management. It provides a detailed framework for comparing CIKM against major academic venues like SIGIR, KDD, WSDM, and SIGMOD/VLDB, ensuring the paper's scope is optimally matched to the target conference.

This comprehensive guide provides expert advice on structuring and polishing research manuscripts for multi-disciplinary academic venues like CIKM. It details how to satisfy reviewers from Information Retrieval, Data Mining, and Knowledge Management simultaneously, covering the optimal structure for the opening page, vocabulary discipline, budget compression, and effective title optimization.