Skill Similarity Assessment

• Skill similarity assessment is the quantitative and qualitative evaluation of skill relatedness using observable data, semantic embeddings, and performance measures.
• Methodologies leverage machine learning, deep ranking networks, and multi-metric comparisons, enabling robust clustering and cross-domain mapping of skills.
• Applications span HR, education, robotics, and knowledge mapping, providing standardized frameworks for aligning competencies and benchmarking performance.

Skill similarity assessment refers to quantitatively or qualitatively evaluating the relatedness, equivalence, or discriminability of skills across individuals, artifacts, or systems. It underpins diverse domains including human resources, education, robotics, and semantic knowledge mapping. Methodologies span comparison of behavioral trajectories, semantic embeddings, learning analytics, and performance data, often leveraging advanced machine learning, representation learning, and statistical techniques. Approaches vary in granularity, applicable data types, and operational objectives, from ranking demonstrations by proficiency to intrinsic comparison of knowledge components.

1. Theoretical Foundations and Definitions

Skill similarity assessment is rooted in the assumption that observed artifacts—demonstrations, outcomes, textual statements, or usage patterns—encode information about latent competencies. A core challenge is to define or learn a metric or mapping $s(\cdot, \cdot)$ that reflects meaningful relatedness. Approaches must resolve the following axes:

• Observable Space: Video, text, trajectory, feature vectors, or performance logs.
• Relatedness Concept: Semantic similarity (e.g., “HTML” and “CSS”), functional equivalence (e.g., cross-platform skill mapping), or behavioral similarity (e.g., similar control policies).
• Granularity: Individual items, skill clusters, or entire profiles.
• Data Availability: Annotated pairs, co-occurrence statistics, performance matrices, or external ontologies.
• Symmetry: Some frameworks permit asymmetric similarity (e.g., prerequisite relations) while most current work targets symmetric, undirected similarity.

Ambiguities in definition are reflected in evaluation: binary relatedness, continuous similarity, or context-dependent equivalence.

2. Methodological Approaches

2.1. Representation and Embedding Methods

A dominant theme is the encoding of skills or artifacts as dense representations in $\mathbb{R}^d$, followed by metric-based comparison. Approaches include:

• Contextual and Static Semantic Embeddings:
  • SkillMatch introduces contrastive self-supervised adaptation of Sentence-BERT, leveraging job-adjacency to embed skill terms such that related pairs have high cosine similarity. Domain-adapted fastText or Word2Vec embeddings also provide robust baselines (Decorte et al., 7 Oct 2024).
  • Semantic Synergy employs SentenceTransformer “all-MiniLM-L6-v2” to produce L2-normalized embeddings for both document chunks and ontology-defined skills. Cosine similarity is operationalized as the dot product over unit vectors, with a fixed threshold determining relatedness (Koundouri et al., 13 Mar 2025).
• Problem Content and Context-Based Vectors:
  • Learning Skill Equivalencies Across Platform Taxonomies proposes hybrid approaches—Bag-of-Words, TF–IDF, average word embeddings (“Content2vec”), and clickstream-trained skipgram models (“Skill2vec”). Matrix factorization (TAMF) fuses content and co-occurrence statistics. For taxonomic mapping, linear transformations align embedding spaces across platforms (Li et al., 2021).

2.2. Statistical and Behavioral Metrics

• Performance-Based Item Similarity:
  • Kappa Learning defines a novel chance-corrected metric for item similarity under “learning assumption,” where a transition from incorrect to correct across two items is classified as agreement rather than disagreement. This approach produces a skill-similarity matrix suitable for clustering, outperforming conventional item similarity metrics under latent-trait dynamics (Nazaretsky et al., 2018).
• Multi-Metric Trajectory and Behavioral Similarity:
  • Similarity-Aware Skill Reproduction in LfD constructs a similarity region around a demonstration, deploying a battery of 11 quantitative metrics (e.g., area between curves, DTW, Fréchet, curvature, endpoint error) to compare trajectory reproductions. Multi-representational generalization leverages SVM-classified subregions optimizing chosen similarity definitions (Hertel et al., 2021).
  • Turtle Score leverages normalized behavioral feature vectors spanning code repositories, error resolution, learning logs, supervisory assessments, and puzzle performance, applying battery of metric-space distances (Chebyshev, Euclidean, Cosine, Minkowski, etc.) for candidate–candidate similarity (Varshini et al., 2022).

2.3. Ranking and Deep Learning Approaches

• Pairwise and Overall Ranking via Deep Networks:
  • Who’s Better? Who’s Best? formulates skill discrimination as a deep-pairwise ranking problem with a Siamese two-stream architecture. A dual-component loss, combining margin-based pairwise ranking for ordered pairs and a similarity regularizer for indistinguishable pairs, yields robust video-to-video skill comparison with up to 83% accuracy across diverse tasks (Doughty et al., 2017).

3. Evaluation Benchmarks and Metrics

Intrinsic and extrinsic evaluation protocols are employed, including:

Task	Typical Metric	References
Skill semantic similarity	AUC-PR, MRR on human-labeled skill pairs	(Decorte et al., 7 Oct 2024)
Taxonomic mapping	Recall@k, MRR vs. expert crosswalks	(Li et al., 2021)
Skill extraction/association	Precision, Recall, F1 for explicit/implicit detection	(Koundouri et al., 13 Mar 2025)
Trajectory/behavioral	Domain-dependent (e.g., mean similarity, ARI)	(Hertel et al., 2021, Nazaretsky et al., 2018)
Skill ranking from demonstration	Pairwise precision/accuracy	(Doughty et al., 2017)

SkillMatch provides a large benchmark of binary-labeled skill pairs; clustering and mapping tasks use Adjusted Rand Index (ARI) or similar indices to compare discovered vs. reference partitions. In cross-platform taxonomy mapping, recall@5 indicates if true equivalents appear among top k candidates.

4. Comparative Analysis of Assessment Paradigms

Approaches to skill similarity vary by domain requirements:

• Semantic and Knowledge Workflows: Embedding + cosine similarity with domain adaptation and proper negative mining (e.g., SkillMatch, Semantic Synergy) yields state-of-the-art discrimination of related skills. Sentence-BERT fine-tuned with in-batch negatives on co-occurrence pairs achieves substantial improvement (AUC-PR 0.969) over static vectors (Decorte et al., 7 Oct 2024).
• Educational Assessment: Kappa Learning outperforms classical item similarity under sequential or adaptive learning, yielding ARI 10–60% higher than Pearson/Yule/Cohen kappa on real tutor data (Nazaretsky et al., 2018).
• Demonstration and Robotics: Multi-metric frameworks (e.g., SAMLfD) reveal that no single representation or similarity measure suffices across initial/generalization regions; combining representations and adaptive metric selection achieve the broadest coverage (Hertel et al., 2021).
• Recruitment and Candidate Analytics: Multidimensional feature vectors spanning observable actions (GitHub, Kaggle, error logs, job shadowing) compared by cosine or Bray–Curtis distance closely align with expert similarity judgments (Varshini et al., 2022).
• Taxonomic Mapping: Content-based, context-based, and hybrid embedding approaches facilitate transfer across incompatible skill inventories, with linear “translation” models reducing manual crosswalk effort (Li et al., 2021).

5. Practical Limitations and Open Challenges

Notable limitations include:

• Binary Relatedness Labels: Most current benchmarks assume dichotomous similarity; real-world relatedness is often continuous and hierarchical (Decorte et al., 7 Oct 2024).
• Domain and Language Transfer: Most frameworks lack explicit modeling of cross-linguistic or cross-cultural variability (Decorte et al., 7 Oct 2024, Koundouri et al., 13 Mar 2025).
• Representation Biases: Embedding models and underlying ontologies may encode unintended biases. Semantic Synergy foregrounds bias-detection modules yet acknowledges this as a persistent limitation (Koundouri et al., 13 Mar 2025).
• Boundary and Generalization Effects: In LfD, reproduction similarity depends sensitively on initialization or context. The SAMLfD approach reveals that metric and representation choice must adapt to boundary conditions for consistent generalization (Hertel et al., 2021).
• Labeling Costs: Skill ranking from video or cross-taxonomic mapping still relies on significant human annotation effort or expert crosswalks, motivating research into semi/self-supervised alternatives (Doughty et al., 2017, Li et al., 2021).

6. Prospects and Extensions

Emerging directions observed across the literature include:

• Ontology-Driven and Modular Pipelines: As in Semantic Synergy, modular pipelines incorporating flexible ontologies, retrainable embeddings, and scalable retrieval (e.g., FAISS indexes) generalize skill similarity to new domains with minimal code change (Koundouri et al., 13 Mar 2025).
• Active and Human-in-the-Loop Refinement: Hybrid schemes that involve user feedback can dynamically adjust thresholds and improve skill detection/relatedness (Koundouri et al., 13 Mar 2025, Li et al., 2021).
• Benchmark Diversification: Release of public resources such as SkillMatch enables reproducible, fine-grained monitoring of progress and facilitates the construction of multi-level, multilingual, or contextually nuanced benchmarks (Decorte et al., 7 Oct 2024).
• Metric and Representation Learning: Both Turtle Score and SAMLfD acknowledge the need for feature weighting, learned compositional similarity, and richer context integration as skill inventories, trajectories, and scenarios increase in complexity (Varshini et al., 2022, Hertel et al., 2021).
• Integration with Downstream Applications: Improved assessment of skill similarity underpins recommendation, alignment of job roles, personalized upskilling, curriculum generation, and autonomous system generalization.

7. Domain-Specific Implementations

A non-exhaustive mapping of implementation domains and corresponding methodologies:

Application Domain	Methodological Backbone	Primary Reference
Educational item mapping	Kappa Learning, clustering	(Nazaretsky et al., 2018)
Skill equivalence across edtech	Content/context embeddings, linear mapping	(Li et al., 2021)
Video-based skill ranking	Siamese deep ranking, custom loss	(Doughty et al., 2017)
Semantic extraction from text	Transformer embeddings, FAISS, ontology	(Koundouri et al., 13 Mar 2025)
HR/candidate analytics	Multifeature vectors, metric-space similarity	(Varshini et al., 2022)
Skill relatedness benchmarks	Contrastively adapted SBERT, static embeddings	(Decorte et al., 7 Oct 2024)
Robotics/LfD	Multi-representational, trajectory metric	(Hertel et al., 2021)

A plausible implication is that future general-purpose skill similarity assessment frameworks will increasingly hybridize semantic, behavioral, and performance-driven representations, tuned to domain and operational requirements.