{"id":7328,"date":"2026-03-24T08:41:38","date_gmt":"2026-03-24T08:41:38","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/regex-table\/"},"modified":"2026-03-24T08:41:38","modified_gmt":"2026-03-24T08:41:38","slug":"regex-table","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/regex-table\/","title":{"rendered":"Regex Table: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Tracking"},"content":{"rendered":"\n

A Regex Table<\/strong> is one of the most useful \u201cbehind-the-scenes\u201d assets in modern Conversion & Measurement<\/strong>. It\u2019s a structured set of regular-expression rules that standardizes messy marketing data\u2014campaign names, URLs, referrers, page paths, event labels, or product SKUs\u2014so your Tracking<\/strong> and reporting stay consistent as channels, creatives, and teams change.<\/p>\n\n\n\n

In real organizations, the same campaign can be labeled five different ways, landing pages can be duplicated across subdomains, and partners can send inconsistent UTMs. A well-governed Regex Table<\/strong> reduces that chaos by applying the same mapping logic everywhere, turning raw strings into stable categories you can trust for attribution, funnel analysis, and optimization. In other words: it\u2019s a small operational tool that has outsized impact on Conversion & Measurement<\/strong> accuracy and decision-making.<\/p>\n\n\n\n

What Is Regex Table?<\/h2>\n\n\n\n

A Regex Table<\/strong> is a table (often a spreadsheet, database table, or configuration file) where each row contains:<\/p>\n\n\n\n

    \n
  • A regular expression pattern to match (the \u201cwhen\u201d)<\/li>\n
  • An output value or transformation (the \u201cthen\u201d)<\/li>\n
  • Optional metadata like priority\/order, notes, owner, and last-updated date<\/li>\n<\/ul>\n\n\n\n

    Beginner-friendly definition: it\u2019s a rule list that uses pattern matching<\/strong> to classify or transform marketing and analytics data at scale.<\/p>\n\n\n\n

    The core concept is simple: instead of hard-coding dozens (or thousands) of one-off string checks, you maintain a centralized set of patterns that can match many variations. Business-wise, a Regex Table<\/strong> protects your reporting from inconsistent naming and makes Tracking<\/strong> outputs comparable across time and teams.<\/p>\n\n\n\n

    Within Conversion & Measurement<\/strong>, it typically sits between raw data collection and analysis\u2014helping normalize dimensions such as channel, campaign, landing page group, content type, geographic region inferred from URL structure, or internal site sections. It supports Tracking<\/strong> by making event and traffic data more interpretable, which is essential for attribution, experimentation, and performance reporting.<\/p>\n\n\n\n

    Why Regex Table Matters in Conversion & Measurement<\/h2>\n\n\n\n

    In Conversion & Measurement<\/strong>, the quality of your conclusions depends on the quality of your input data. A Regex Table<\/strong> matters because it:<\/p>\n\n\n\n

      \n
    • Improves attribution quality:<\/strong> If \u201cpaid social\u201d traffic is mislabeled as \u201creferral\u201d or \u201cother,\u201d your budget allocation will be wrong.<\/li>\n
    • Enables consistent reporting:<\/strong> Executives expect stable definitions of channels, campaigns, and product lines\u2014even when naming conventions drift.<\/li>\n
    • Reduces data cleanup cycles:<\/strong> Analysts spend less time patching dashboards and more time generating insights.<\/li>\n
    • Creates operational leverage:<\/strong> One update to the Regex Table<\/strong> can fix classification across multiple reports, pipelines, and dashboards.<\/li>\n
    • Supports competitive speed:<\/strong> Faster, cleaner Tracking<\/strong> feedback loops help you iterate campaigns and landing pages sooner.<\/li>\n<\/ul>\n\n\n\n

      Strategically, a Regex Table<\/strong> is a governance tool. It turns tribal knowledge (\u201cthis partner\u2019s UTMs are always weird\u201d) into a maintainable system that strengthens Conversion & Measurement<\/strong> across the organization.<\/p>\n\n\n\n

      How Regex Table Works<\/h2>\n\n\n\n

      A Regex Table<\/strong> is more practical than theoretical. Here\u2019s how it works in a typical workflow for Tracking<\/strong> and reporting:<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ trigger<\/strong>\n Raw text enters your measurement ecosystem: URLs, referrers, UTMs, event names, form IDs, page titles, or ecommerce categories. This arrives via analytics collection, server logs, ad platform exports, or CRM data.<\/p>\n<\/li>\n

      2. \n

        Analysis \/ processing<\/strong>\n A script, ETL job, tag management rule, or data-modeling layer reads the Regex Table<\/strong> from a central location. It evaluates each input value against the table\u2019s patterns.<\/p>\n<\/li>\n

      3. \n

        Execution \/ application<\/strong>\n When a pattern matches, the system assigns an output (for example, Channel = Paid Search<\/code>) or performs a transformation (for example, rewriting inconsistent campaign names into a standard format). In many setups, rule order matters: the first matching row \u201cwins.\u201d<\/p>\n<\/li>\n

      4. \n

        Output \/ outcome<\/strong>\n The normalized fields flow into dashboards and models: channel performance, conversion funnels, cohort reports, and attribution. The result is more trustworthy Conversion & Measurement<\/strong> and more interpretable Tracking<\/strong> data.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        The key idea: you are separating \u201cbusiness definitions\u201d (how you define channels, campaigns, or site sections) from \u201craw strings\u201d (how those values appear in the wild).<\/p>\n\n\n\n

        Key Components of Regex Table<\/h2>\n\n\n\n

        A reliable Regex Table<\/strong> is not just patterns in a sheet. The best implementations include:<\/p>\n\n\n\n

          \n
        • Pattern column (regex):<\/strong> Carefully written expressions that match known variants without catching unrelated strings.<\/li>\n
        • Output column(s):<\/strong> The classification or transformed value (channel, campaign group, content theme, product family, etc.).<\/li>\n
        • Priority \/ order:<\/strong> Clear precedence so broad patterns don\u2019t override specific ones.<\/li>\n
        • Match scope:<\/strong> Which field the rule applies to (landing page path vs referrer vs campaign name).<\/li>\n
        • Test cases:<\/strong> Example inputs and expected outputs for regression testing.<\/li>\n
        • Change log & ownership:<\/strong> Who can edit, approval workflow, and what changed.<\/li>\n
        • Deployment path:<\/strong> How updates move from draft to production Tracking<\/strong> and reporting.<\/li>\n<\/ul>\n\n\n\n

          In Conversion & Measurement<\/strong> teams, ownership is often shared: marketers define business categories, analysts validate data impact, and developers or data engineers operationalize the table in pipelines.<\/p>\n\n\n\n

          Types of Regex Table<\/h2>\n\n\n\n

          \u201cRegex Table\u201d isn\u2019t a single standardized product category, but there are useful distinctions in how teams apply the concept:<\/p>\n\n\n\n

          Classification vs transformation tables<\/h3>\n\n\n\n
            \n
          • Classification Regex Table:<\/strong> Maps inputs to categories (e.g., referrer \u2192 channel group).<\/li>\n
          • Transformation Regex Table:<\/strong> Cleans or rewrites values (e.g., normalize campaign naming, extract IDs via capture groups).<\/li>\n<\/ul>\n\n\n\n

            Allowlist vs blocklist approaches<\/h3>\n\n\n\n
              \n
            • Allowlist:<\/strong> Only classify when a pattern is confidently matched; everything else goes to \u201cUnclassified.\u201d<\/li>\n
            • Blocklist:<\/strong> Exclude known noise (internal tools, payment gateways, QA domains) to protect Tracking<\/strong> cleanliness.<\/li>\n<\/ul>\n\n\n\n

              Single-field vs multi-field rule tables<\/h3>\n\n\n\n
                \n
              • Single-field:<\/strong> Rules apply to one input (e.g., page path only).<\/li>\n
              • Multi-field:<\/strong> Rules evaluate combinations (e.g., source<\/code> + medium<\/code> + landing page) for more accurate Conversion & Measurement<\/strong> channel grouping.<\/li>\n<\/ul>\n\n\n\n

                Ordered rules vs scoring rules<\/h3>\n\n\n\n
                  \n
                • Ordered:<\/strong> First match wins (common and easy to implement).<\/li>\n
                • Scoring\/weighted:<\/strong> Multiple matches contribute signals (more complex; used when classifications overlap).<\/li>\n<\/ul>\n\n\n\n

                  Real-World Examples of Regex Table<\/h2>\n\n\n\n

                  1) Channel grouping for acquisition reporting<\/h3>\n\n\n\n

                  A company receives inconsistent UTMs from agencies and partners. A Regex Table<\/strong> evaluates source<\/code>, medium<\/code>, and referrer patterns to assign channels like Paid Search, Paid Social, Affiliates, Email, and Organic. This stabilizes Conversion & Measurement<\/strong> dashboards and prevents misattribution caused by inconsistent Tracking<\/strong> inputs.<\/p>\n\n\n\n

                  2) Landing page segmentation for CRO and SEO insights<\/h3>\n\n\n\n

                  A content-heavy site wants to measure conversions by content theme. A Regex Table<\/strong> matches URL paths (e.g., \/guides\/<\/code>, \/pricing\/<\/code>, \/compare\/<\/code>) and assigns a \u201cPage Group.\u201d That enables funnel analysis, assisted conversions, and cohort comparisons by site section\u2014improving Conversion & Measurement<\/strong> planning across acquisition and onsite optimization.<\/p>\n\n\n\n

                  3) Event naming normalization across multiple products<\/h3>\n\n\n\n

                  A SaaS company has several teams emitting events with inconsistent names (signup_submit<\/code>, sign_up_submit<\/code>, SignupSubmitted<\/code>). A Regex Table<\/strong> standardizes event names in the data pipeline so product analytics, marketing attribution, and lifecycle reporting align. This improves Tracking<\/strong> reliability and reduces arguments about what is \u201cthe real\u201d conversion event.<\/p>\n\n\n\n

                  Benefits of Using Regex Table<\/h2>\n\n\n\n

                  A well-maintained Regex Table<\/strong> delivers tangible operational and performance benefits:<\/p>\n\n\n\n

                    \n
                  • Higher data quality:<\/strong> Fewer \u201cunknown\u201d buckets and fewer mislabeled channels improve Conversion & Measurement<\/strong> confidence.<\/li>\n
                  • Faster reporting cycles:<\/strong> Less manual cleanup and fewer dashboard patches.<\/li>\n
                  • More scalable governance:<\/strong> New campaigns and partners can be onboarded by adding rules instead of rebuilding reports.<\/li>\n
                  • Better experimentation:<\/strong> Cleaner segmentation makes A\/B test results easier to interpret and less prone to tracking noise.<\/li>\n
                  • Improved customer understanding:<\/strong> More accurate journey analysis and cohort reporting from better Tracking<\/strong> normalization.<\/li>\n<\/ul>\n\n\n\n

                    Challenges of Regex Table<\/h2>\n\n\n\n

                    Despite its value, a Regex Table<\/strong> can create new risks if unmanaged:<\/p>\n\n\n\n

                      \n
                    • Overmatching and undermatching:<\/strong> A broad regex can unintentionally classify unrelated traffic; overly strict patterns miss valid variants.<\/li>\n
                    • Rule conflicts:<\/strong> Multiple patterns match the same input, causing inconsistent outputs if priority isn\u2019t explicit.<\/li>\n
                    • Hidden technical debt:<\/strong> Tables grow without pruning, becoming hard to understand and error-prone.<\/li>\n
                    • Governance bottlenecks:<\/strong> If only one person can safely edit the rules, updates lag behind new campaigns\u2014hurting Conversion & Measurement<\/strong> responsiveness.<\/li>\n
                    • Environment drift:<\/strong> Staging and production versions diverge, creating confusing Tracking<\/strong> discrepancies between \u201cwhat we tested\u201d and \u201cwhat we shipped.\u201d<\/li>\n<\/ul>\n\n\n\n

                      The solution is not avoiding the approach\u2014it\u2019s treating the Regex Table<\/strong> as a maintained system with tests, ownership, and release discipline.<\/p>\n\n\n\n

                      Best Practices for Regex Table<\/h2>\n\n\n\n

                      To make a Regex Table<\/strong> trustworthy and scalable in Conversion & Measurement<\/strong> and Tracking<\/strong>, apply these practices:<\/p>\n\n\n\n

                        \n
                      • Start with a taxonomy:<\/strong> Define channel groups, campaign tiers, page groups, and conversion events before writing patterns.<\/li>\n
                      • Prefer specificity with safe fallbacks:<\/strong> Use targeted rules first, then broader catch-alls, and always keep an \u201cUnclassified\/Other\u201d bucket for review.<\/li>\n
                      • Make precedence explicit:<\/strong> Add a priority column and document \u201cfirst match wins\u201d behavior (or whichever model you use).<\/li>\n
                      • Add test cases per rule:<\/strong> Store example inputs and expected outputs; run tests before deploying changes.<\/li>\n
                      • Version control and approvals:<\/strong> Treat updates like code\u2014peer review changes that impact executive reporting.<\/li>\n
                      • Monitor unmatched volume:<\/strong> Regularly review the top \u201cunclassified\u201d inputs to decide whether to add rules or fix upstream naming.<\/li>\n
                      • Retire old rules:<\/strong> Remove patterns tied to deprecated campaigns, partners, or site sections to reduce false positives.<\/li>\n
                      • Document intent:<\/strong> Notes like \u201cmatches legacy partner UTMs\u201d prevent future editors from breaking hard-won knowledge.<\/li>\n<\/ul>\n\n\n\n

                        Tools Used for Regex Table<\/h2>\n\n\n\n

                        A Regex Table<\/strong> is tool-agnostic; what matters is consistent application across your Tracking<\/strong> and analytics stack. Common tool groups include:<\/p>\n\n\n\n

                          \n
                        • Analytics tools:<\/strong> Use regex-based filters or classification logic to group sources, pages, or events for Conversion & Measurement<\/strong> reporting.<\/li>\n
                        • Tag management systems:<\/strong> Apply regex rules to trigger tags, rewrite parameters, or standardize event names at collection time (with caution to avoid breaking raw data).<\/li>\n
                        • Data warehouses and ETL\/ELT pipelines:<\/strong> Apply the Regex Table<\/strong> during data modeling so transformations are centralized, auditable, and reusable.<\/li>\n
                        • CRM and marketing automation:<\/strong> Normalize lead sources, campaign IDs, and form identifiers so pipeline reporting aligns with Tracking<\/strong>.<\/li>\n
                        • Reporting dashboards \/ BI tools:<\/strong> Implement regex mapping at the semantic layer for consistent definitions across stakeholders.<\/li>\n
                        • QA and monitoring workflows:<\/strong> Automated checks that validate new campaigns against naming conventions and detect classification drift.<\/li>\n<\/ul>\n\n\n\n

                          The most robust pattern is: keep raw data intact, apply the Regex Table<\/strong> in a controlled transformation layer, and expose both raw and normalized fields for transparency in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                          Metrics Related to Regex Table<\/h2>\n\n\n\n

                          Because a Regex Table<\/strong> improves data reliability, many \u201cmetrics\u201d are data-quality and operations indicators:<\/p>\n\n\n\n

                            \n
                          • Match rate:<\/strong> % of records classified by the table (higher is usually better, but not at the cost of incorrect matches).<\/li>\n
                          • Unclassified volume trend:<\/strong> Whether unknown sources\/pages\/events are growing\u2014often a signal of new campaigns or broken naming.<\/li>\n
                          • Misclassification rate (via audits):<\/strong> Sample-based QA comparing expected vs actual outputs.<\/li>\n
                          • Rule coverage by channel\/campaign:<\/strong> Are the biggest spend sources fully mapped?<\/li>\n
                          • Time-to-classify new inputs:<\/strong> How quickly new partners\/campaigns become reportable in Conversion & Measurement<\/strong>.<\/li>\n
                          • Downstream stability:<\/strong> Reduction in dashboard changes and fewer \u201cdefinition disputes\u201d in Tracking<\/strong> reviews.<\/li>\n
                          • Decision impact:<\/strong> Improved ROAS\/CPA decisions due to cleaner channel and campaign segmentation (measured indirectly through better allocation outcomes).<\/li>\n<\/ul>\n\n\n\n

                            Future Trends of Regex Table<\/h2>\n\n\n\n

                            The role of the Regex Table<\/strong> is evolving alongside privacy changes and automation in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                              \n
                            • AI-assisted rule suggestions:<\/strong> Systems can propose patterns by clustering new unclassified strings, speeding up maintenance while still requiring human review.<\/li>\n
                            • Greater emphasis on first-party data:<\/strong> As identifiers and third-party signals shrink, consistent internal naming and normalization become more important for Tracking<\/strong> continuity.<\/li>\n
                            • Semantic layers and metric stores:<\/strong> Organizations are moving logic upstream into governed definition layers, where a Regex Table<\/strong> becomes part of a broader \u201cdefinitions as code\u201d strategy.<\/li>\n
                            • Real-time monitoring:<\/strong> More teams will track classification drift and anomalies as part of measurement observability.<\/li>\n
                            • Personalization and experimentation:<\/strong> As segmentation becomes more granular, the need for clean, standardized dimensions will increase, making Regex Table<\/strong> governance a core competency within Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                              Regex won\u2019t disappear\u2014its advantage is compact, expressive pattern matching. The trend is better tooling and governance around how those patterns are created, tested, and deployed.<\/p>\n\n\n\n

                              Regex Table vs Related Terms<\/h2>\n\n\n\n

                              Regex Table vs Regular Expression (Regex)<\/h3>\n\n\n\n

                              A regular expression is the pattern language itself. A Regex Table<\/strong> is the operational container: a managed list of regex patterns plus outputs, priorities, and documentation used in Tracking<\/strong> and reporting.<\/p>\n\n\n\n

                              Regex Table vs Lookup Table<\/h3>\n\n\n\n

                              A lookup table usually maps exact keys to values (e.g., utm_source=google \u2192 Paid Search<\/code>). A Regex Table<\/strong> handles variability (e.g., google|gclid|adwords<\/code>), making it better for messy real-world marketing data in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                              Regex Table vs Channel Grouping Rules<\/h3>\n\n\n\n

                              Channel grouping rules are a specific application (classifying traffic into channels). A Regex Table<\/strong> is broader: it can power channel grouping, page grouping, event normalization, partner mapping, and data-cleaning processes across your Tracking<\/strong> stack.<\/p>\n\n\n\n

                              Who Should Learn Regex Table<\/h2>\n\n\n\n
                                \n
                              • Marketers:<\/strong> You\u2019ll understand why reports change, how channels are defined, and how to design UTMs that reduce downstream cleanup in Conversion & Measurement<\/strong>.<\/li>\n
                              • Analysts:<\/strong> You\u2019ll gain a scalable method for classification, QA, and repeatable reporting\u2014critical for trustworthy Tracking<\/strong> insights.<\/li>\n
                              • Agencies:<\/strong> You can deliver cleaner handoffs, consistent campaign naming, and defensible performance reporting across clients.<\/li>\n
                              • Business owners and founders:<\/strong> You\u2019ll be able to ask better questions about attribution, channel performance, and why dashboards don\u2019t match.<\/li>\n
                              • Developers and data engineers:<\/strong> You\u2019ll implement the Regex Table<\/strong> safely in pipelines and ensure transformations are testable, versioned, and observable.<\/li>\n<\/ul>\n\n\n\n

                                Summary of Regex Table<\/h2>\n\n\n\n

                                A Regex Table<\/strong> is a governed list of regex-based rules that classifies or transforms messy marketing and analytics strings into stable, reportable dimensions. It matters because it improves accuracy, speed, and trust in Conversion & Measurement<\/strong>, while strengthening the consistency of Tracking<\/strong> across channels, campaigns, and products. When built with clear priorities, testing, and ownership, it becomes a foundational piece of scalable measurement operations.<\/p>\n\n\n\n

                                Frequently Asked Questions (FAQ)<\/h2>\n\n\n\n

                                What is a Regex Table used for in marketing analytics?<\/h3>\n\n\n\n

                                A Regex Table<\/strong> is used to classify and normalize inputs like UTMs, referrers, URLs, and event names so reporting categories (channels, campaigns, page groups) stay consistent for Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                Does a Regex Table replace good naming conventions?<\/h3>\n\n\n\n

                                No. It complements them. Strong naming conventions reduce complexity; a Regex Table<\/strong> handles exceptions, legacy formats, and third-party inconsistencies that still show up in real Tracking<\/strong> data.<\/p>\n\n\n\n

                                Where should I apply Regex Table logic: collection time or reporting time?<\/h3>\n\n\n\n

                                Prefer applying it in a governed transformation layer (data modeling\/ETL) so raw data remains intact. Use collection-time changes only when necessary and carefully QA the impact on Tracking<\/strong>.<\/p>\n\n\n\n

                                How do I know if my Tracking needs a Regex Table?<\/h3>\n\n\n\n

                                If you frequently see \u201cOther\/Unassigned,\u201d inconsistent channel totals, duplicated campaign names, or weekly dashboard fixes, a Regex Table<\/strong> will likely improve Conversion & Measurement<\/strong> stability.<\/p>\n\n\n\n

                                How often should a Regex Table be updated?<\/h3>\n\n\n\n

                                Update it whenever new unclassified inputs become material (new partners, new campaign structures, new site sections). Many teams review monthly, with ad-hoc updates for major launches that affect Tracking<\/strong>.<\/p>\n\n\n\n

                                What\u2019s the biggest risk when maintaining a Regex Table?<\/h3>\n\n\n\n

                                Overly broad patterns that misclassify data. That can quietly distort Conversion & Measurement<\/strong> decisions. Mitigate with priorities, test cases, sampling audits, and version control.<\/p>\n\n\n\n

                                Can non-technical marketers maintain a Regex Table?<\/h3>\n\n\n\n

                                Yes, if the table is well-documented and includes examples and guardrails. Many organizations let marketers propose changes while analysts or engineers validate patterns to protect Tracking<\/strong> integrity.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                A **Regex Table** is one of the most useful \u201cbehind-the-scenes\u201d assets in modern **Conversion & Measurement**. It\u2019s a structured set of regular-expression rules that standardizes messy marketing data\u2014campaign names, URLs, referrers, page paths, event labels, or product SKUs\u2014so your **Tracking** and reporting stay consistent as channels, creatives, and teams change.<\/p>\n","protected":false},"author":10235,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1890],"tags":[],"class_list":["post-7328","post","type-post","status-publish","format-standard","hentry","category-tracking"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7328","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/users\/10235"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/comments?post=7328"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7328\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7328"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7328"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7328"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}