Dontopedia

term

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

term has 34 facts recorded in Dontopedia across 12 references, with 3 live disagreements.

34 facts·12 predicates·12 sources·3 in dispute

Mostly:rdf:type(13), used for(2), preferred over(2)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Inbound mentions (13)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

containsContains(2)

hasQueryTypeHas Query Type(2)

demonstratesDemonstrates(1)

includesIncludes(1)

mentionsQueryTypeMentions Query Type(1)

prefersPrefers(1)

recommendsRecommends(1)

recommendsQueryTypeRecommends Query Type(1)

requiresProperUseOfRequires Proper Use of(1)

usesQueryTypeUses Query Type(1)

usesTermQueryUses Term Query(1)

Other facts (13)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

13 facts
PredicateValueRef
Used forExact Matches[2]
Used forExact Matches[8]
Preferred OverMatch Query[9]
Preferred OverMatch Phrase Query[9]
Performs Exact Matchtrue[3]
Analyzernone[4]
Has Fieldsome_field[7]
Has Valuesome_value[7]
Is Example ofSpecific Query[7]
Has Nested StructureTerm Nested Object[7]
Inverse ofExact Matches[8]
Purposeexact matches[12]
Performance Benefitimprove performance[12]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

typebeam/df7c58f3-fbec-47d0-9088-2916d03b14b6
ex:QueryType
labelbeam/df7c58f3-fbec-47d0-9088-2916d03b14b6
Term Query
typebeam/870d36e1-74c7-4923-a45d-7839861584f0
ex:ExactMatchQuery
usedForbeam/870d36e1-74c7-4923-a45d-7839861584f0
ex:exact-matches
typebeam/7bd85e51-293e-474e-97e0-39e4f7463398
ex:QueryType
typebeam/7bd85e51-293e-474e-97e0-39e4f7463398
ex:ExactMatchQuery
performsExactMatchbeam/7bd85e51-293e-474e-97e0-39e4f7463398
true
typebeam/30cfcb2d-27af-4962-b51a-166d7c86b3a4
ex:ExactMatchQuery
analyzerbeam/30cfcb2d-27af-4962-b51a-166d7c86b3a4
none
typebeam/ef7935db-f389-498e-baf5-aff58f744d6b
ex:ElasticsearchQueryType
labelbeam/ef7935db-f389-498e-baf5-aff58f744d6b
term query
typebeam/67b3880f-4304-41f2-a990-5fffd8b6b339
ex:QueryType
labelbeam/67b3880f-4304-41f2-a990-5fffd8b6b339
term query
typebeam/52477875-5368-4c2c-89e1-08b2f4d72518
ex:QueryType
labelbeam/52477875-5368-4c2c-89e1-08b2f4d72518
term query
hasFieldbeam/52477875-5368-4c2c-89e1-08b2f4d72518
some_field
hasValuebeam/52477875-5368-4c2c-89e1-08b2f4d72518
some_value
isExampleOfbeam/52477875-5368-4c2c-89e1-08b2f4d72518
ex:specific-query
hasNestedStructurebeam/52477875-5368-4c2c-89e1-08b2f4d72518
ex:term-nested-object
typebeam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845
ex:QueryType
usedForbeam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845
ex:exact-matches
inverseOfbeam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845
ex:exact-matches
typebeam/2157dee9-e970-4d48-9c1b-078d02e8d4d8
ex:QueryType
labelbeam/2157dee9-e970-4d48-9c1b-078d02e8d4d8
term
preferredOverbeam/2157dee9-e970-4d48-9c1b-078d02e8d4d8
ex:match-query
preferredOverbeam/2157dee9-e970-4d48-9c1b-078d02e8d4d8
ex:match-phrase-query
typebeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
ex:QueryType
labelbeam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
term query
typebeam/63484f14-f077-4119-aad4-2ec5f59e1801
ex:ElasticsearchQueryType
labelbeam/63484f14-f077-4119-aad4-2ec5f59e1801
term
typebeam/f4eafbd9-2b49-42e3-8a19-d812701aab05
ex:ElasticsearchQueryType
labelbeam/f4eafbd9-2b49-42e3-8a19-d812701aab05
Term Query
purposebeam/f4eafbd9-2b49-42e3-8a19-d812701aab05
exact matches
performanceBenefitbeam/f4eafbd9-2b49-42e3-8a19-d812701aab05
improve performance

References (12)

12 references
  1. ctx:claims/beam/df7c58f3-fbec-47d0-9088-2916d03b14b6
    • full textbeam-chunk
      text/plain1 KBdoc:beam/df7c58f3-fbec-47d0-9088-2916d03b14b6
      Show excerpt
      "number_of_shards": 5, "number_of_replicas": 1, "analysis": { "analyzer": { "default": { "type": "standard", " stopwords
  2. ctx:claims/beam/870d36e1-74c7-4923-a45d-7839861584f0
    • full textbeam-chunk
      text/plain1 KBdoc:beam/870d36e1-74c7-4923-a45d-7839861584f0
      Show excerpt
      "bool": { "must": [ { "match": { "title": "example" } }, { "match": { "content": "example" } } ], "filter": [ { "term": { "status": "active" }} # Assuming there's a status field that can be fil
  3. ctx:claims/beam/7bd85e51-293e-474e-97e0-39e4f7463398
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7bd85e51-293e-474e-97e0-39e4f7463398
      Show excerpt
      "bool": { "must": [ { "match": { "title": "example" } }, { "match": { "content": "example" } } ], "filter": [ { "term": { "status": "active" }} ]
  4. ctx:claims/beam/30cfcb2d-27af-4962-b51a-166d7c86b3a4
  5. ctx:claims/beam/ef7935db-f389-498e-baf5-aff58f744d6b
  6. ctx:claims/beam/67b3880f-4304-41f2-a990-5fffd8b6b339
    • full textbeam-chunk
      text/plain1 KBdoc:beam/67b3880f-4304-41f2-a990-5fffd8b6b339
      Show excerpt
      - Understanding when to use `match`, `term`, `bool`, `filter`, etc. - Proper use of `must`, `should`, `must_not`, and `filter` clauses. 2. **Filter Context**: - Using `filter` context for conditions that can be cached and reused.
  7. ctx:claims/beam/52477875-5368-4c2c-89e1-08b2f4d72518
    • full textbeam-chunk
      text/plain1 KBdoc:beam/52477875-5368-4c2c-89e1-08b2f4d72518
      Show excerpt
      - **Filter Cache**: Use the filter cache for frequently used filters. ### 4. **Monitor and Profile** - **Use the Explain API**: Use the `_explain` API to understand how Elasticsearch is executing your query. - **Use the Profile API**: Use
  8. ctx:claims/beam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845
    • full textbeam-chunk
      text/plain1 KBdoc:beam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845
      Show excerpt
      - Batch documents into groups of 500-1000 for optimal performance. #### Example Code ```python from elasticsearch import Elasticsearch es = Elasticsearch(["http://localhost:9200"]) actions = [ { "_index": "my_index",
  9. ctx:claims/beam/2157dee9-e970-4d48-9c1b-078d02e8d4d8
    • full textbeam-chunk
      text/plain1 KBdoc:beam/2157dee9-e970-4d48-9c1b-078d02e8d4d8
      Show excerpt
      - **Index Shards**: Ensure that the number of shards is appropriate for your data volume. Too many shards can lead to performance degradation. ```json PUT /your-index-name/_settings { "number_of_shards": 5 } ``` ### 2. Query
  10. ctx:claims/beam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1a3ec59a-c5a8-4cc0-9e26-ce87ed77ed86
      Show excerpt
      Ensure your queries are optimized for performance. 1. **Use Efficient Query Types**: Prefer `term` and `terms` queries over `match` and `match_phrase` queries when possible. ```json { "query": { "bool": { "mu
  11. ctx:claims/beam/63484f14-f077-4119-aad4-2ec5f59e1801
  12. ctx:claims/beam/f4eafbd9-2b49-42e3-8a19-d812701aab05
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f4eafbd9-2b49-42e3-8a19-d812701aab05
      Show excerpt
      {"_index": "query_index", "_source": {"query": "How do I find happiness?"}}, # Add more actions as needed ] helpers.bulk(es, actions) ``` ### 4. **Caching** Enable caching to reduce the load on the database for frequently accessed

See also

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.