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Retrieve Earth science datasets from NASA's CMR using specific keywords, date ranges, and DAAC filters to streamline data discovery and access.

Execute a SPARQL SELECT query against the DanNet triplestore. This tool provides direct access to DanNet's RDF data through SPARQL queries. The query is automatically prepended with common namespace prefix declarations, so you can use short prefixes instead of full URIs in your queries. PERFORMANCE OPTIMIZATION WARNING: Avoid queries that create cartesian products (cross joins) as they can cause timeouts. Common problematic patterns include: - Comparing all entities with labels containing word X to all entities with labels containing word Y - Multiple unconnected graph patterns without shared variables (e.g., ?x a Type1. ?y a Type2.) - Excessive OPTIONAL clauses that multiply result combinations - Broad FILTER operations on string matching across large result sets Instead, use specific entity URIs (e.g., dn:synset-2228), connect patterns with shared variables, apply LIMIT clauses, and prefer VALUES lists over broad FILTER conditions. Start with targeted queries on known entities, then explore their direct relationships using specific property paths. KNOWN PREFIXES (automatically declared): - rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns# - rdfs: http://www.w3.org/2000/01/rdf-schema# - owl: http://www.w3.org/2002/07/owl# - wn: https://globalwordnet.github.io/schemas/wn# - ontolex: http://www.w3.org/ns/lemon/ontolex# - skos: http://www.w3.org/2004/02/skos/core# - lexinfo: http://www.lexinfo.net/ontology/3.0/lexinfo# - marl: http://www.gsi.upm.es/ontologies/marl/ns# - olia: http://purl.org/olia/olia.owl# - dcat: http://www.w3.org/ns/dcat# - vann: http://purl.org/vocab/vann/ - foaf: http://xmlns.com/foaf/0.1/ - dc: http://purl.org/dc/terms/ - dc11: http://purl.org/dc/elements/1.1/ - cc: http://creativecommons.org/ns# - ili: http://globalwordnet.org/ili/ - lime: http://www.w3.org/ns/lemon/lime# - schema: http://schema.org/ - synsem: http://www.w3.org/ns/lemon/synsem# - enl: https://en-word.net/lemma/ - en: https://en-word.net/id/ - enold: http://wordnet-rdf.princeton.edu/id/ - cor: https://ordregister.dk/id/ - dds: https://wordnet.dk/sentiment/ - dn: https://wordnet.dk/dannet/data/ - dnc: https://wordnet.dk/dannet/concepts/ - dns: https://wordnet.dk/dannet/schema/ - tr: https://wordnet.dk/dannet/translations/ COMMON QUERY PATTERNS: # Find all synsets for a word: SELECT ?synset ?definition WHERE { ?entry ontolex:canonicalForm/ontolex:writtenRep "hund"@da . ?entry ontolex:sense/ontolex:isLexicalizedSenseOf ?synset . ?synset skos:definition ?definition . } # Find hypernyms (broader concepts): SELECT ?hypernym ?label WHERE { dn:synset-3047 wn:hypernym ?hypernym . ?hypernym rdfs:label ?label . } # Find functional relations (DanNet-specific "used for" patterns): SELECT ?tool ?toolLabel ?purpose ?purposeLabel WHERE { ?tool dns:usedFor ?purpose . ?tool rdfs:label ?toolLabel . ?purpose rdfs:label ?purposeLabel . } # Discover thematic role patterns (agents of actions): SELECT ?action ?actionLabel ?agent ?agentLabel WHERE { ?action wn:involved_agent ?agent . ?action rdfs:label ?actionLabel . ?agent rdfs:label ?agentLabel . } # Find co-occurrence patterns (systematic co-occurrences): SELECT ?concept ?conceptLabel ?coAgent ?coAgentLabel WHERE { ?concept wn:co_agent_instrument ?coAgent . ?concept rdfs:label ?conceptLabel . ?coAgent rdfs:label ?coAgentLabel . } # Explore cross-cutting categories (orthogonal hypernyms): SELECT ?specific ?specificLabel ?ortho ?orthoLabel WHERE { ?specific dns:orthogonalHypernym ?ortho . ?specific rdfs:label ?specificLabel . ?ortho rdfs:label ?orthoLabel . } # Find synsets with ontological types (stored as RDF Bags): SELECT ?synset ?label ?type WHERE { ?synset dns:ontologicalType ?typeNode . ?typeNode rdf:_0 ?type . ?synset rdfs:label ?label . } # Explore causality chains: SELECT ?cause ?causeLabel ?effect ?effectLabel WHERE { ?cause wn:causes ?effect . ?cause rdfs:label ?causeLabel . ?effect rdfs:label ?effectLabel . } Args: query: SPARQL SELECT query string (prefixes will be automatically added) timeout: Query timeout in milliseconds (default: 5000, max: 10000) max_results: Maximum number of results to return (default: 100, max: 100) Returns: Dict containing SPARQL results in standard JSON format: - head: Query metadata with variable names - results: Bindings array with variable-value mappings Each value includes type (uri/literal) and language information when applicable FASCINATING RELATIONSHIP EXAMPLES FROM DANNET: Functional Relations (dns:usedFor): - {idrætshal; sportshal} used for {idræt; sport} (sports hall used for sports) - {faldskærm; skærm} used for {idræt; sport} (parachute used for sports) - {sportstøj} used for {idræt; sport} (sportswear used for sports) Thematic Roles (wn:involved_agent): - {kommunikere} typically involves agent {presseattaché} (communication involves press attaché) - {skrive} typically involves agent {informationsmedarbejder} (writing involves information worker) Co-occurrence Patterns (wn:co_agent_instrument): - {krig} co-occurs with agent-instrument {våben} (war systematically co-occurs with weapons) - {verdenskrig} co-occurs with agent-instrument {våben} (world war co-occurs with weapons) Cross-cutting Categories (dns:orthogonalHypernym): - {fiskefartøj} has orthogonal hypernym {fartøj} (fishing vessel has cross-cutting category vessel) - {handelsfartøj} has orthogonal hypernym {fartøj} (merchant vessel has cross-cutting category vessel) These examples show how DanNet captures not just taxonomic relationships but also functional, instrumental, and systematic co-occurrence patterns in Danish language and culture. Example Usage: # Simple entity lookup result = sparql_query("SELECT ?s ?p ?o WHERE { dn:synset-3047 ?p ?o } LIMIT 10") # Complex semantic query exploring functional relations result = sparql_query(''' SELECT ?tool ?toolLabel ?purpose ?purposeLabel WHERE { ?tool dns:usedFor ?purpose . ?tool rdfs:label ?toolLabel . ?purpose rdfs:label ?purposeLabel . FILTER(CONTAINS(?purposeLabel, "sport")) } ''') Note: Only SELECT queries are supported. The query is validated before execution.