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REVIEW 4 major objections 6 minor 57 references

An Agentic Hybrid Top-Down and Bottom-Up Approach to Knowledge Graph Generation

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read An agentic hybrid pipeline grounds an LLM in Wikidata and uses a reflection loop to convert noisy, multilingual skill declarations into a structured, auditable knowledge graph and taxonomy, reporting 77% global coverage and 100%…

desk verdict A sensible industrial pipeline with an honest pre-consolidation evaluation, but the headline self-healing claim rests on an untested and probably fragile deterministic-label assumption. read the letter →

arxiv 2608.07023 v1 pith:PX5Y3QG3 submitted 2026-08-07 cs.CL cs.AI

Emma Jouffroy , Warren Jouanneau , Marc Palyart This is my paper · ORCID
classification cs.CLcs.AI
keywords knowledgegraphgenerationskillstaxonomyagenticreflexionWikidatagroundingentityreconciliationmultilingualcanonicalizationlong-tailskillextractionHRAI
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a five-stage, agentic pipeline can turn thousands of messy, multilingual free-text skill declarations into a structured, auditable knowledge graph and taxonomy. Rather than choosing between rigid top-down ontologies and chaotic bottom-up clustering, the system anchors recognized skills to stable Wikidata entities and lets an LLM reflectively synthesize new nodes for unrecognized long-tail skills. This would let an HR platform keep its skill taxonomy current without manual curation while preserving cross-lingual consistency. The evaluation on 36,037 real user strings reports 77% of inputs mapped to graph nodes, 79.7% alignment and 84.9% found coverage against a hand-annotated gold standard, and 100% of canonical nodes labeled in all five target languages.

What carries the argument

The load-bearing component is the agentic reflexion and orphan-recovery loop. When the curation agent rejects a raw skill as a specialization rather than an equivalent, it synthesizes a suggested preferred label; that label is hashed to a deterministic ORPHAN_ID, which becomes the anchor for a new sub-graph node in the next epoch. Because identical concepts are assumed to receive identical labels, the hash automatically clusters duplicate orphans. Wikidata supplies the multilingual anchor entities for the top-down side, and provenance metadata tracks whether each label came from platform usage, Wikidata, or generation.

What would settle it

Take the same raw skill string (for example 'gestion de projet web') through Stages 3 and 5 one hundred times and count the distinct ORPHAN_IDs produced; if a single concept yields more than one ID, the automatic grouping of duplicate orphans fails.

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Extended reading notes

Core claim

The central claim is that a hybrid architecture with strict external grounding plus an agentic reflexion loop achieves both the precision of a curated ontology and the adaptability of generative extraction. Recognized concepts are pinned to stable Wikidata QIDs with provenance-tracked labels; rejected specializations are not discarded but routed through a recursive orphan-recovery loop that assigns each one a suggested label, hashes that label into a stable ORPHAN_ID, and re-injects it in the next epoch so nested sub-specializations accumulate into coherent sub-graphs. The authors report that this yields 13,298 canonical skill nodes from 27,743 mapped variations, compressing the input vocabulary by 52.1%, while retaining the semantic long-tail and keeping every node expressed in all five supported languages.

Load-bearing premise

The load-bearing premise is that the LLM produces exactly the same suggested label for the same concept every time, so identical orphans hash to the same identifier; if that determinism fails, deduplication and convergence break.

Editorial extensions

If this is right

  • A skills taxonomy can be kept in sync with a live marketplace: new specializations are folded into the graph in later epochs instead of being dropped or over-generalized.
  • Cross-lingual matching improves because exact translations of a skill converge on the same Wikidata QID and receive a preferred label in every target language.
  • The provenance tags on labels make the resulting graph auditable: an operator can see which labels rest on platform usage, which on Wikidata, and which were generated.
  • The consolidation phase's merge decisions can be cached, so repeated runs avoid re-evaluating the same pairs, keeping incremental updates cheap.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The deterministic-hash trick is what makes the self-healing claim load-bearing; if the LLM's label output is not actually stable (for instance across model versions or batch contexts), the ORPHAN_ID groups will silently fragment, and the published metrics do not yet measure that stability.
  • Because 100% cross-lingual coverage is measured only on the 13,298 canonical nodes that survived curation, it may not reflect how the pipeline handles low-resource or highly inflected variants that fail to map to any QID; a per-language recall breakdown would be needed to confirm symmetry.
  • The same hash-based grouping idea could generalize beyond skills: any setting where an LLM synthesizes canonical labels for deduplicated entities could use deterministic identifiers to merge across runs, provided label determinism is verified.
  • The consolidation phase is the part that actually proves 'self-healing' across epochs; the paper reports it is not yet benchmarked, so the convergence claim rests on the orphan loop's internal logic rather than on end-to-end measurements.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a five-stage, agentic pipeline for generating a skills knowledge graph from noisy, multilingual freelancer expertise declarations. Stage 1 reconciles raw strings to Wikidata QIDs; Stage 2 canonicalizes clusters into multilingual preferred labels; Stage 3 curates clusters and rejects non-equivalent skills into an orphan queue; Stage 4 deduplicates across batches; and Stage 5 iteratively recovers orphans by synthesizing new sub-graph nodes with stable synthetic identifiers. The authors claim the result is a scalable, explicable, and self-healing framework, and they report coverage and precision numbers on a proprietary Malt dataset, including 77% global coverage, 79.7% alignment coverage, and 84.9% found coverage against a hand-annotated gold standard. The supplementary material provides prompt templates for all stages.

Significance. If the pipeline worked as claimed, it would be a practically valuable contribution to automated taxonomy and knowledge-graph construction for HR applications, with useful design choices: grounding in Wikidata, strict output schemas, provenance tracking for labels, and an explicit long-tail recovery mechanism. The release of full prompt templates is a reproducibility strength, and evaluation on real multilingual platform data is more persuasive than synthetic benchmarks. However, the manuscript's distinctive claims—especially the 'self-healing' convergence of the orphan-recovery loop and the multilingual symmetry result—are not actually supported by the reported experiments. The evaluation is explicitly limited to pre-consolidation stages, and the convergence argument rests on unverified determinism of LLM label generation. The contribution is therefore currently conditional on substantial additional evidence.

major comments (4)
  1. [Section 3.5 and Supplementary Figure 7] The self-healing convergence claim is load-bearing and unsupported. Section 3.5 states that each orphan is assigned an ORPHAN_ID derived from the LLM-generated suggested_pref_label and that 'because identical concepts receive the exact same suggested label from the model', duplicate orphans are automatically grouped until the queue empties. No evidence is provided that Gemini 1.5 Flash with temperature 0 produces identical labels across calls, batches, or epochs; semantically identical concepts can easily be labeled differently (e.g., 'Web Project Management' vs. 'Web PM'). Moreover, the supplementary orphan-canonicalization prompt (Figure 7) instructs the model to 'Create new synthetic identifiers using the format: SYNTH_SKILL_XXX (where XXX is a unique number)', which sounds like a sequential counter rather than a deterministic hash of the label, while the consolidation prompt (Figure 10) refers to IDs such as ORPHAN_ITER1_... . These identifier schemes are not reconciled, so the grouping mechanism and the claimed convergence are not established by the manuscript.
  2. [Section 4, first paragraph] The evaluation is explicitly restricted to 'the initial retrieval and pre-consolidation phases', and the paper states that the final post-consolidation phase 'has yet to be formally benchmarked'. The reported 77% global coverage, 79.7% alignment coverage, and 84.9% found coverage therefore do not evaluate the deduplication or self-healing contributions, which are central to the abstract's claims. In addition, no confidence intervals or inter-annotator agreement are reported for the hand-annotated gold standard, so the domain-level differences in Table 1 (e.g., 91.8% vs. 81.0% found coverage) may not be statistically meaningful.
  3. [Section 4.1] The '100% cross-lingual symmetry' result is an artifact of the generation procedure rather than an empirical finding. Because the pipeline synthesizes exactly five labels (fr, en, de, nl, es) for each of the 13,298 canonical nodes, it produces 66,490 labels by construction; this does not demonstrate that the labels are semantically aligned or that all languages are equally well supported. The claim should be reframed as a design property of the pipeline, not as a validated multilingual-quality result.
  4. [Section 4, first paragraph and Section 3.1] There is a partial circularity between the evaluation and the pipeline's grounding. The gold standard is described as 'curated Wikidata gold standard', while Stage 1 of the pipeline anchors on Wikidata entities. If the gold standard is constructed from the same Wikidata entities used as anchors, the reported coverage and precision may be inflated by design. The authors should clarify how the gold standard was built, report what fraction of its labels are non-Wikidata and human-verified, and ideally evaluate on a held-out set of labels that are not present in Wikidata.
minor comments (6)
  1. [Throughout (Abstract, Sections 1 and 3.2)] The term 'explicable' is used repeatedly; the standard term in this literature is 'explainable'.
  2. [Section 4.2] The gold-standard description should state the number of items, the number of annotators per item, and the agreement measure; currently only 'five domain experts' are mentioned.
  3. [Figures 2 and 3] The iterative loop from Stage 5 back to Stage 3/4 is not visually explicit; adding labeled arrows for 'Epoch N+1' would help readers follow the self-healing claim.
  4. [Section 5] The bias discussion is framed as a goal ('Our goal is to mitigate...') rather than an evaluation; a preliminary analysis of gender-marked terms or locale-specific labels would strengthen the paper.
  5. [Section 2 and Section 5] TnT-LLM [22] and CLIMB [13] are discussed but no quantitative comparison is provided; a small comparison table or explicit baseline numbers would help contextualize the reported coverage figures.
  6. [Section 2] There is a typographical spacing issue in 'T oestablish' at the start of the second paragraph; this should be corrected.

Circularity Check

2 steps flagged · score 4.0 of 10

Self-healing convergence is asserted via unverified label determinism, and the gold-standard evaluation reuses the same Wikidata ontology that anchors the pipeline.

  1. self definitional [Section 4 (Evaluation) and Section 3.1 (Reconciliation)]
    "Because HR matching engines require strict reliability, model performance is evaluated against a curated Wikidata gold standard. ... the Reconciliation phase maps noisy, unstandardized, and multilingual skill mentions to stable, unambiguous Wikidata entity identifiers (QIDs), which serve as the foundational anchor nodes for our knowledge graph."

    The pipeline is anchored to Wikidata, and the evaluation criterion is defined in the same ontology: the gold standard is described as 'curated Wikidata', while the reconciliation task is to output Wikidata QIDs. The reported Alignment Coverage (79.7%) and Found Coverage (84.9%) therefore measure the LLM's ability to re-select the Wikidata entity used to build the gold standard, not the independent correctness of the generated knowledge graph. Human curation adds genuine signal, but because both the prediction space and the gold-standard space are Wikidata QIDs, the metric is partly a self-consistency check rather than an external validation.

  2. self definitional [Section 3.5 (Iteration), Section 4 (Evaluation), and Supplementary Figure 7]
    "Because identical concepts receive the exact same suggested label from the model, this mechanism naturally groups separate but matching specializations together into a unified sub-graph in the subsequent Epoch. This loop repeats until the orphan queue is empty, achieving full semantic convergence. ... the final post-consolidation phase was recently introduced to the pipeline and has yet to be formally benchmarked."

    The ORPHAN_ID is derived from the LLM's generated suggested_pref_label, so the claimed grouping of duplicates is guaranteed only under the unstated premise that the model produces identical labels for identical concepts. The convergence claim is therefore a restatement of that premise, not an empirically validated outcome. The paper does not test label stability across batches or epochs, and it explicitly states that the post-consolidation phase has not been benchmarked.

full rationale

The core reconciliation and canonicalization stages are not circular in the sense of fitting a parameter and then predicting it: the gold standard includes human expert annotation, and the Wikidata grounding is a stated design choice rather than a fitted output. However, two partial circularities reduce the score. First, the evaluation gold standard is Wikidata-based while the pipeline is anchored to Wikidata, so the reported coverage numbers partly measure the model's conformity to the same ontology used to construct the gold standard. Second, the central 'self-healing' and 'full semantic convergence' claims for the orphan-recovery loop reduce to the assumption that the LLM deterministically emits identical suggested labels for identical concepts; the paper admits this consolidation phase has not been benchmarked, and the supplementary prompt's sequential SYNTH_SKILL_XXX identifier format conflicts with the stated hash-based routing. There are no load-bearing self-citations, and the pipeline still has substantial independent content such as the multilingual canonicalization and human-reviewed curation stages. Overall, the paper is partially circular but not wholly reducible to its inputs.

Assumptions & free parameters 3 free parameters · 4 assumptions · 1 invented entities

The pipeline relies on several empirical thresholds and on the stability of LLM outputs that are not independently verified. The synthetic orphan entities are the core of the claimed novelty but lack external validation.

free parameters (3)
  • top_k_cooccurring_skills = 15
    Section 3.1: 'top fifteen (an empirical threshold selected to maximize semantic signal while filtering out long-tail profile noise)'. No sensitivity analysis is provided.
  • top_m_professional_categories = 5
    Section 3.1: 'top five overarching professional categories'. Chosen by hand; no sensitivity analysis.
  • human_review_rejection_rate_threshold = 50%
    Section 3.3: 'any node with a rejection rate exceeding 50% is automatically flagged for human-in-the-loop review'. Chosen without stated justification.
assumptions (4)
  • domain assumption Wikidata provides a sufficiently complete and correct ontology for the five target languages and all skill domains
    Section 3 intro: 'By utilizing Wikidata as a semantic anchor, chosen for its broad multilingual domain coverage'. If Wikidata lacks or mislabels concepts, the reconciliation and gold standard are biased.
  • domain assumption LLM outputs at temperature 0 are deterministic and stable enough to assign the same synthetic labels for identical concepts across runs
    Section 3.5: 'Because identical concepts receive the exact same suggested label from the model, this mechanism naturally groups separate but matching specializations'. This is an empirical claim without evidence.
  • domain assumption The five domain experts' gold standard annotations are correct and unbiased
    Section 4.2: gold standard 'independently curated by five domain experts without overlap'. No inter-annotator agreement is reported.
  • domain assumption The Malt platform usage distributions are representative of the labor market
    Used as empirical weights for canonicalization and Pareto analysis; no external validation of representativeness.
invented entities (1)
  • Synthetic orphan nodes (SYNTH_SKILL_XXX and ORPHAN_ID sub-graphs)
    purpose: Represent emerging long-tail skills not covered by Wikidata as first-class graph entities with stable identifiers.
    Section 3.5 and prompts Fig.7: the LLM invents synthetic identifiers and preferred labels; these are self-referential and not validated against external sources or post-consolidation benchmarking.

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Cite this review

Pith. "Pith review of An Agentic Hybrid Top-Down and Bottom-Up Approach to Knowledge Graph Generation." pith.science (2026). https://pith.science/paper/PX5Y3QG3

@misc{pith2026260807023,
  author       = {Pith},
  title        = {Pith review of: An Agentic Hybrid Top-Down and Bottom-Up Approach to Knowledge Graph Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PX5Y3QG3}},
  note         = {Machine review of arXiv:2608.07023}
}
read the original abstract

Organizing thousands of unstandardized, multilingual expertise declarations is a persistent challenge for Human Resources (HR) platforms, directly impacting downstream tasks like accurate talent matching. To address this, we propose a hybrid knowledge graph generation pipeline that grounds a Large Language Model (LLM) in the Wikidata multilingual Knowledge Graph (KG) while employing an agentic reflexion pattern to synthesize emerging concepts and their associated metadata. Unlike rigid top-down methods or fragmented bottom-up approaches, our system anchors recognized concepts to stable Knowledge Graph entities while dynamically creating new nodes and relational metadata for unrecognized skills. Executed across five stages, entity reconciliation, multilingual canonicalization, active curation, deduplication, and the iterative recovery of unmapped concepts, the system autonomously adapts to rapidly evolving, noisy skill mentions across five European languages. Ultimately, this pipeline provides a highly scalable, explicable, and self-healing framework for generating a comprehensive skills knowledge graph, from which a structured taxonomy is derived, using unstructured, noisy text.

Figures

Figures reproduced from arXiv: 2608.07023 by the authors.

Figure 1
Figure 1. Comparison of structural modeling paradigms via a project management narrative. (A) Static Top￾Down: Misses emerging specializations due to static ontological boundaries. (B) Unconstrained Bottom-Up: Lacks guardrails, causing semantic fragmentation (separating “Project Mgmt” and “Web PM”) and link hallucinations (“Excel”). (C) Proposed Hybrid: Achieves complete convergence. The baseline concept is anchored to a Wiki… view at source ↗
Figure 2
Figure 2. The 5-stage hybrid pipeline architecture, traced via the project management example. The system grounds the model in Wikidata (Stage 1) and uses agentic reflection (Stage 3) to capture outliers. These are converted into stable synthetic identifiers to instantiate new sub-graph entities (Stage 4) and rerouted (Stage 5) for continuous, autonomous self-healing of the knowledge graph structure. results could likely be r… view at source ↗
Figure 3
Figure 3. The Agentic Reflection and Orphan Lifecycle. Rejected skills trigger an active reflection loop (Steps 3–5) where the LLM justifies the outlier status and synthesizes a suggested_pref_label. This generates a stable synthetic ORPHAN_ID routed into the next Epoch for autonomous self-healing of the knowledge graph layout [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Semantic Reconciliation Prompt B Canonicalization details Following reconciliation, the canonicalization phase groups validated inputs by their resolved QIDs to synthesize localized, human-readable preferred labels across the five target languages. To handle the divers…
Figure 5
Figure 5. Figure 5: Canonicalization Prompt - Single QID You are an expert **Multilingual Taxonomy Linguist and HR Specialist**. Your ONLY task is to analyze a combination of Wikidata concepts and the raw skills associated with them to determine the best unified Preferred Label (‘pref_lab…
Figure 6
Figure 6. Figure 6: Canonicalization Prompt - Multiple QID [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Canonicalization Prompt - Orphans C Curation details To maintain structural purity, an active curation agent validates the strict equivalence of every raw skill against its broader canonical grouping. As shown in the first prompt, skills that fail this check are reject…
Figure 8
Figure 8. Figure 8: Curation Prompt You are an expert **Multilingual Taxonomy Linguist and HR Specialist**. Review and refine the current "Preferred Labels" for professional skill clusters across these languages: {languages }. ### DIRECTIVES Act as a local HR specialist in the country of …
Figure 10
Figure 10. Figure 10: Consolidation Prompt [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]

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    False Friends

    **CRITICAL LANGUAGE RULE (False Friends)**: - Beware of "False Friends" (Faux amis) across languages. Do not map a foreign word to an English Wikidata concept just because they are spelled similarly if the professional meaning is different. - Example: The French "Rédaction" me...

  28. [36]

    {expertise}

    **Analyze "{expertise}"**: Based on the definitions above, decide if it’s a skill

  29. [37]

    **Identify Compound Skills**: If it IS a skill, determine if it is a compound skill

  30. [38]

    Use all provided context (co-occurring skills, job categories, Wikidata info)

    **Evaluate Candidates**: Carefully evaluate each candidate QID using the Selection Rules above. Use all provided context (co-occurring skills, job categories, Wikidata info)

  31. [39]

    {expertise}

    **Select & Score**: - Choose the best QID(s). For a compound skill, you MUST return one entry for each distinct skill identified. - For each selected QID, provide a score and a concise reasoning. ### Scoring Rubric: - **0.0**: The best matching wikidata item(s) is not really a...

  32. [40]

    - Fallback to the **Wikidata label** if the usage-based terms are ambiguous, informal, or inappropriate

    **Preferred Label Selection** - Prioritize the most **frequently used** label from the Malt usage counts if it is professional. - Fallback to the **Wikidata label** if the usage-based terms are ambiguous, informal, or inappropriate. - Create a **Synthetic** (new) professional ...

  33. [41]

    malt"‘: based on an existing high-usage profile expertise. - ‘

    **Provenance Tracking (‘source‘)** - For every localized label, specify its origin: - ‘"malt"‘: based on an existing high-usage profile expertise. - ‘"wikidata"‘: selected the official Wikidata description or language variant. 10 E. Jouffroy et al. - ‘"gemini"‘: you synthesize...

  34. [42]

    English to French Translation

    **Compound Label Selection** - You MUST choose a Preferred Label that is coherent with the **majority of the high-usage skills** within the cluster. - **Directionality Rule (CRITICAL):** If ‘Is Directional Process‘ is ‘True‘ in the Input Data, the order of the concepts strictl...

  35. [43]

    - If you modify or add to this identifier, use ‘&‘ (AND) for integrated practices, and ‘|‘ (OR) for independent traits

    **Define Logic for the Identifier** - The Baseline Composite ID groups the provided QIDs (e.g., ‘{qid_combo}‘). - If you modify or add to this identifier, use ‘&‘ (AND) for integrated practices, and ‘|‘ (OR) for independent traits

  36. [44]

    **Synthetic Identifiers** - You must exclusively use the provided Wikidata QIDs whenever possible. - However, if the majority of the skills introduce a critical, distinct concept that is NOT covered by any provided QID, you MUST invent a concise, uppercase English textual iden...

  37. [45]

    Prioritize usage counts, fallback to Wikidata, or synthesize a professional HR term if needed

    **Create Preferred Labels** - Localize names for the final composite identifier (including those with synthetic textual IDs appended). Prioritize usage counts, fallback to Wikidata, or synthesize a professional HR term if needed

  38. [46]

    malt"‘: derived from an existing profile expertise. - ‘

    **Provenance Tracking (‘source‘)** - For every localized label, specify its origin: - ‘"malt"‘: derived from an existing profile expertise. - ‘"wikidata"‘: taken from Wikidata context. - ‘"gemini"‘: a new synthesized composite term. --- ### FORMATTING RULES (CRITICAL) - **No I...

  39. [47]

    - DO NOT assume these skills fit into their previous Wikidata QIDs

    **Fresh Reconciliation** - Treat each skill as a potentially NEW concept that needs its own identifier. - DO NOT assume these skills fit into their previous Wikidata QIDs. - The Wikidata context provided is from their ORIGINAL (failed) matching - use it only as reference to kn...

  40. [48]

    - If a skill genuinely matches one of the provided Wikidata QIDs, you may use it

    **Synthetic ID Creation** - Create new synthetic identifiers using the format: ‘SYNTH_SKILL_XXX‘ (where XXX is a unique number). - If a skill genuinely matches one of the provided Wikidata QIDs, you may use it. - If a skill combines multiple concepts, use ‘&‘ notation (e.g., ‘...

  41. [49]

    gemini"‘ for synthesized labels. - Set ‘source‘ to ‘

    **Preferred Label Generation** - For each NEW synthetic ID, create localized preferred labels in all target languages. - Set ‘source‘ to ‘"gemini"‘ for synthesized labels. - Set ‘source‘ to ‘"malt"‘ if you’re using a high-frequency Malt term. - Set ‘source‘ to ‘"wikidata"‘ onl...

  42. [50]

    - Use ‘rejection_reason‘: ‘NOT_A_SKILL‘, ‘SEMANTIC_MISMATCH‘, or ‘AMBIGUOUS‘

    **Skill Validation** - Set ‘is_skill: false‘ if the text is NOT a valid professional skill. - Use ‘rejection_reason‘: ‘NOT_A_SKILL‘, ‘SEMANTIC_MISMATCH‘, or ‘AMBIGUOUS‘. --- ### FORMATTING RULES (CRITICAL) - **No Internal Quotes:** Do NOT wrap label values in quotes (write ‘Da...

  43. [51]

    Social media

    **What to KEEP (‘is_equivalent: true‘, ‘rejection_reason: null‘):** - **Translations (CRITICAL)**: Direct translations of the core concept in English, French, Spanish, German, or Dutch MUST be kept together in the same cluster (e.g., KEEP "Social media" or "Redes sociales" ins...

  44. [52]

    Web Project Management

    **What to REMOVE (‘is_equivalent: false‘) and Categorize (‘rejection_reason‘):** - ‘DOMAIN_SPECIALIZATION‘: A specific domain or industry that requires distinct technical knowledge. Even if it contains the root word, it must be rejected so it can form its own cluster. (e.g., R...

  45. [53]

    Select a ’rejection_reason’ (e.g., DOMAIN_SPECIALIZATION)

  46. [54]

    Preferred Labels

    When rejecting a skill, you MUST provide a ‘suggested_pref_label‘. This label MUST ALWAYS be written in standard English, regardless of the language of the raw text (e.g., if the raw text is ’audit RH’, the suggested label must be ’HR Audit’). --- ### FORMATTING RULES - You MU...

  47. [55]

    **Official over Synthetic:** Prioritize Wikidata QIDs (e.g., Q123) over Orphan IDs (e.g., ORPHAN_ITER1_...)

  48. [56]

    **Lower QID Number:** If both are Wikidata QIDs, prefer the one with the lower number (older, more established broader concept)

  49. [57]

    **Stability:** If merging an Orphan into a QID, the QID MUST be the ‘surviving_composite_id‘. ### INPUT DATA **CLUSTER A:** - ID: {pair_data[’cluster_1_id’]} - Refined Labels (Filtered): {cluster_1_labels} - Validated Skills in this Cluster: {json.dumps(pair_data[’cluster_1_sk...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.