Query the Semantica knowledge graph using SPARQL, Cypher, and keyword search.
npx skills add https://github.com/semantica-agi/semantica --skill query-semantica-agi
Please help me install this Agent Skill. Skill: query Source: https://github.com/semantica-agi/semantica/tree/main/plugins/skills/query Command: npx skills add https://github.com/semantica-agi/semantica --skill query-semantica-agi
Queries the Semantica knowledge graph to retrieve information using SPARQL, Cypher, and keyword search.
Ask the AI to run a SPARQL query against the knowledge graph to retrieve information for a given entity.
High-intent search queries and answers about installing and using this skill.
You can query a knowledge graph using SPARQL, Cypher, and keyword search to retrieve information across linked entities. This Skill supports multiple query modes and structured graph patterns to model traversal and filtering for data discovery.
Graph data discovery is the process of retrieving information across linked entities using query languages like SPARQL. You need it for decision-support workflows requiring traceable, expressive queries and explainable results from a knowledge graph.
Yes, you can use keyword search as an alternative to SPARQL or Cypher. The Skill supports flexible query modes including keyword search, SPARQL, and Cypher-like patterns to access and retrieve graph data.
You model graph traversal and filtering by applying structured graph patterns within your SPARQL or Cypher queries. This approach supports common graph query patterns to retrieve and filter linked entities with readable, extensible outputs.
Yes, querying the knowledge graph with SPARQL and Cypher directly supports decision-support workflows. It provides traceable, expressive queries that deliver explainable results for analytics and data discovery.
Keyword search retrieves graph data but lacks the structured graph patterns available in SPARQL and Cypher. For complex graph analytics requiring specific traversal, filtering, and traceable query outputs, structured query languages provide more expressive and extensible results.
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