Implemented Many-to-Many Search with Fuzzy Matching

• Day: 2024-07-12
• Time: 03:05 to 03:55
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Fuzzy Matching, Elasticsearch, Python, Data Processing

Description

Session Goal

The session aimed to implement a many-to-many search mechanism linking individuals to relevant pages using Elasticsearch and fuzzy matching techniques.

Key Activities

• Developed a structured approach for many-to-many search using data preparation, NLP feature extraction, and Elasticsearch indexing and querying.
• Implemented fuzzy matching between names in DataFrames using Python’s pandas and fuzzywuzzy libraries, enabling efficient entity recognition and matching.
• Created Python code snippets to handle lists of tuples in DataFrames, focusing on matching entities from two datasets.
• Utilized Elasticsearch for optional indexing and efficient querying of matched entities.

Achievements

• Successfully implemented a fuzzy matching function in Python to search for names within a cleaned text column of a DataFrame.
• Completed the workflow for loading data, defining a matching function, applying it, and displaying results.

Pending Tasks

• Further optimization of the Elasticsearch indexing process for improved performance.
• Exploration of additional NLP techniques to enhance feature extraction and matching accuracy.

Evidence

• source_file=2024-07-12.sessions.jsonl, line_number=0, event_count=0, session_id=b513a5b9ba9ff2b0624ad1907930a8686e38c4b4ce997343745d961832f1b029
• event_ids: []