Semantic Similarity

Similarity Search and Clustering

• Implement vector similarity search (cosine, dot product)
• Configure approximate nearest neighbor (ANN) algorithms
• Design content clustering pipelines
• Implement hierarchical clustering for organization

Related Content Recommendation

• Build content recommendation systems
• Configure "More Like This" functionality
• Implement collaborative filtering with embeddings
• Design hybrid recommendation approaches

Duplicate Detection

• Identify duplicate and near-duplicate content
• Configure similarity thresholds for detection
• Implement deduplication workflows
• Design merge and consolidation strategies

Topic Modeling

• Implement LDA (Latent Dirichlet Allocation)
• Configure BERTopic for modern topic modeling
• Design topic hierarchies and taxonomies
• Enable dynamic topic tracking

Semantic Search Integration

• Configure semantic search pipelines
• Implement hybrid search (keyword + semantic)
• Design query expansion using embeddings
• Enable cross-lingual semantic search

Content Gap Analysis

• Identify missing content through similarity analysis
• Map content coverage using embeddings
• Detect underserved topics and areas
• Design content planning recommendations

Concept Extraction

• Extract key concepts from documents
• Build concept graphs from embeddings
• Implement keyphrase extraction
• Design concept tagging pipelines

Dependencies

• Sentence-transformers library
• OpenAI Embeddings API
• Cohere Embed API
• Pinecone vector database
• Weaviate
• Milvus
• FAISS (Facebook AI Similarity Search)
• scikit-learn for clustering

Process Integration

This skill integrates with:

• search-optimization.js: Semantic search and related content features
• knowledge-base-content.js: Content recommendations and gap analysis
• tacit-to-explicit-conversion.js: Knowledge representation and concept extraction

Usage

Generate Document Embeddings