Property talk:P6611

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Semantic Scholar topic ID
identifier for a research topic, in the Semantic Scholar database

Represents	Semantic Scholar (Q22908627)
Associated item	Semantic Scholar (Q22908627)
Applicable "stated in" value	Semantic Scholar (Q22908627)
Has quality	numeric identifier (Q93868746)
Data type	External identifier
Allowed values	[1-9]\d*
Example	Wikidata (Q2013) → 590752 artificial intelligence (Q11660) → 8286 graphene (Q169917) → 1184 Plasmodium falciparum (Q311383) → 2884
Source	https://www.semanticscholar.org/
Formatter URL	https://www.semanticscholar.org/topic/$1
Tracking: usage	Category:Pages using Wikidata property P6611 (Q62311326)
See also	Semantic Scholar paper ID (P4011), Semantic Scholar author ID (P4012), Semantic Scholar corpus ID (P8299)
Lists	Items with the most statements of this property Count of items by number of statements (chart) Items with the most statements Items with the fewest statements Count of items by number of sitelinks (chart) Items with the most sitelinks Items with the most sitelinks, but no link to one Wikipedia (sample: Welsh Wikipedia, cywiki) Items with the fewest sitelinks Most linked wikis Items with the most identifier properties Items with no other external identifier Items with no other statements Most recently created items Items with novalue claims Items with unknown value claims Usage history (total) Usage history (main statement) Chart by item creation date Database reports/Constraint violations/P6611 Map Random list
Proposal discussion	Proposal discussion
Current uses	Total 480 Main statement 471 98.1% of uses Qualifier 4 0.8% of uses Reference 5 1% of uses
Search for values

[create Create a translatable help page (preferably in English) for this property to be included here]

Distinct values: this property likely contains a value that is different from all other items. (Help)

Exceptions are possible as rare values may exist. Exceptions can be specified using exception to constraint (P2303).
List of violations of this constraint: Database reports/Constraint violations/P6611#Unique value, SPARQL (every item), SPARQL (by value)

Single value: this property generally contains a single value. (Help)

Exceptions are possible as rare values may exist. Exceptions can be specified using exception to constraint (P2303).
List of violations of this constraint: Database reports/Constraint violations/P6611#Single value, SPARQL

Format “[1-9]\d*”: value must be formatted using this pattern (PCRE syntax). (Help)

Exceptions are possible as rare values may exist. Exceptions can be specified using exception to constraint (P2303).
List of violations of this constraint: Database reports/Constraint violations/P6611#Format, SPARQL

Conflicts with “instance of (P31): Wikimedia list article (Q13406463)”: this property must not be used with the listed properties and values. (Help)

Exceptions are possible as rare values may exist. Exceptions can be specified using exception to constraint (P2303).
List of violations of this constraint: Database reports/Constraint violations/P6611#Conflicts with P31, search, SPARQL

Allowed entity types are Wikibase item (Q29934200): the property may only be used on a certain entity type (Help)

Exceptions are possible as rare values may exist. Exceptions can be specified using exception to constraint (P2303).
List of violations of this constraint: Database reports/Constraint violations/P6611#Entity types

Scope is as main value (Q54828448), as reference (Q54828450): the property must be used by specified way only (Help)

Exceptions are possible as rare values may exist. Exceptions can be specified using exception to constraint (P2303).
List of violations of this constraint: Database reports/Constraint violations/P6611#Scope, SPARQL

Old an new types of topics

The old topics seem to be referenced nowhere anymore on the site. How could you find them originally?

Instead there are new topic ids, linking to https://topics-beta.apps.semanticscholar.org/topic/‌$newTopicId

Example

• Old Topic: https://www.semanticscholar.org/topic/Wikidata/590752 None of the papers linked there links back to this topic.
• Current link to Wikidata in the topics section: https://www.semanticscholar.org/paper/Wikidata-Vrande%C4%8Di%C4%87-Kr%C3%B6tzsch/dab7e605237ad4f4fe56dcba2861b8f0a57112be#paper-topics
• Currently linked topic https://topics-beta.apps.semanticscholar.org/topic/26731605765?corpusId=14494942 Frlgin (talk) 15:01, 8 November 2024 (UTC)

Info from semanticscholar.org

Semantic Scholar FAQ

https://www.semanticscholar.org/faq#what-are-topic-pages

What are Topic pages? Topics are phrases in research papers that are often accompanied by citations. Topic pages aim to help scholars quickly understand a field by synthesizing concepts from several papers that mention or explain the topic with generative AI, including:

Definition of the topic Related topics Papers often cited for this topic Recent papers that mention this topic

1. why-dont-i-see-topics-for-a-paper-im-interested-in

Why don’t I see topics for a paper I’m interested in? Currently, only topics in the Computer Science field are available. To see if a topic is available, you can directly search for it from the search bar on any Topic page.

1. how-are-topic-pages-generated

How are Topic pages generated? We use large language models (LLMs) to extract a list of topics from the title and abstract of a paper. Subsequently, we leverage these models to generate definitions and descriptions using relevant snippets from the single most cited paper for a topic.

Semantic Scholar Product page

https://www.semanticscholar.org/product#topics

Explore Topics with AI Topic pages aim to help you explore topics of interest by collecting AI-generated definitions, papers often cited for the topic, recent papers, and related topics.

In the Topics section on each paper page, you can find topics relevant to the paper and navigate to the topic pages. You can also directly search for a topic in the search bar. Currently, only topics in the Computer Science field are available.

Topics Examples SPECTER: Document-level Representation Learning using Citation-informed Transformers TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension Longformer: The Long-Document Transformer