Semantic Scholar

Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.

Map view:

Zoom: 100%

More settings

Connection length 100% Label size 100% Node size 100% Line width 100% Label width 100%

Export map as:

Semantic Scholar topic overview

The analysis highlights Technology, Applications and Science as prominent areas in the source structure around Semantic Scholar.

Related topics: 31
Source areas: 6
Connected nodes: 37
Extracted relationships: 40
Concept neighborhoods: 18
Bridge connections: 37

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Technology · 10 topics

Overview · 9 topics

Article identifier · 4 topics

Number of users and publications · 4 topics

Basic corpus for AI discovery tools · 3 topics

Indexing · 1 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Created by: Allen Institute for Artificial Intelligence
Launched: November 2, 2015; 10 years ago (2015-11-02)
Type of site: Search engine
URL: semanticscholar.org

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Technology

Scientific literature
Machine vision
Semantic analysis Semantic analysis (linguistics)
Citation analysis
Tables Table extraction
TLDR TL;DR
Google Scholar
PubMed
Microsoft Academic Knowledge Graph Microsoft Academic
SciGraph

Article identifier

Identifier
Doi Doi (identifier)
PMID PMID (identifier)
S2CID S2CID (identifier)

Indexing

Paywall

Number of users and publications

Biomedicine
Amazon Alexa
Microsoft Academic Graph
University of Chicago Press Journals University of Chicago Press

Basic corpus for AI discovery tools

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Semantic Scholar connects Entity context

The extracted context around Semantic Scholar shows recurring relationship patterns in the source. For example, Semantic Scholar → Amazon Alexa, As, At, August, Chicago Press, Chicago Press Journals, Doug Raymond, In, In March, January, Microsoft Academic Graph, PDFs, University Another extracted example is Semantic Scholar → AI, Ai2, Alongside, Asta, Consensus, CrossRef, DOI, Elicit, OpenAlex, ORCID, SciSpace, Undermind. Use these groups to spot repeated connection types before inspecting the individual relationships.

Top relations

related to Number of users and publications · 13

Semantic Scholar → Amazon Alexa, As, At, August, Chicago Press, Chicago Press Journals, Doug Raymond, In, In March, January, Microsoft Academic Graph, PDFs, University

related to Basic corpus for AI discovery tools · 12

Semantic Scholar → AI, Ai2, Alongside, Asta, Consensus, CrossRef, DOI, Elicit, OpenAlex, ORCID, SciSpace, Undermind

related to Article identifier · 4

Semantic Scholar → Each, S2CID, Semantic Scholar Corpus ID, The

related to Technology · 4

Semantic Scholar → Artificial, It, One, The

related to Indexing · 2

Semantic Scholar → Google Scholar, One

Created by · 1

Semantic Scholar → Allen Institute for Artificial Intelligence

Launched · 1

Semantic Scholar → November 2, 2015; 10 years ago (2015-11-02)

Type of site · 1

Semantic Scholar → Search engine

URL · 1

Semantic Scholar → semanticscholar.org

is a · 1

Semantic Scholar → research tool for scientific literature

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

semantic scholar papers science corpus million computer ai scientific research literature paper uses learning natural language processing users also publications

Semantic Scholar relationships Subject–Predicate–Object triples

TTTA extracted 40 structured relationships around Semantic Scholar. Examples in this analysis include Semantic Scholar → Created by → Allen Institute for Artificial Intelligence and Semantic Scholar → Launched → November 2, 2015; 10 years ago (2015-11-02). The table shows each extracted connection, where it came from and its confidence.

Subject	Predicate	Object	Confidence	Src
Semantic Scholar	Created by	Allen Institute for Artificial Intelligence	1.00	infobox
Semantic Scholar	Launched	November 2, 2015; 10 years ago (2015-11-02)	1.00	infobox
Semantic Scholar	Type of site	Search engine	1.00	infobox
Semantic Scholar	URL	semanticscholar.org	1.00	infobox
Semantic Scholar	is a	research tool for scientific literature	0.90	text
Semantic Scholar	related to Article identifier	Each	0.60	section
Semantic Scholar	related to Article identifier	Semantic Scholar Corpus ID	0.60	section
Semantic Scholar	related to Article identifier	S2CID	0.60	section
Semantic Scholar	related to Article identifier	The	0.60	section
Semantic Scholar	related to Basic corpus for AI discovery tools	AI	0.60	section
Semantic Scholar	related to Basic corpus for AI discovery tools	Elicit	0.60	section
Semantic Scholar	related to Basic corpus for AI discovery tools	SciSpace	0.60	section

Related concept clusters Concept neighborhoods

The concept neighborhoods around Semantic Scholar bring nearby vocabulary together. In this analysis, examples include Semantic, Corpus and Papers. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

Semantic Scholar
- Semantic
- Corpus
- Papers
- Computer
- Science
- Scientific
- Project
- Language
- Machine
- Natural
- Processing
- Summaries
semantic scholar
- Semantic
- Corpus
- Computer
- Papers
- Science
- Scientific
- Project
- Also
- Search
- Language
- Machine
- Natural
computer science
- Science
- Engines
- Neuroscience
- Academic
- Search
- Scholar
- Million
- Semantic
- Graph
- Microsoft
- Publications
- Project
scientific literature
- Also
- Literature
- Scientific
- Corpus
- Semantic
- Allen
- Began
- Institute
- Artificial
- Intelligence
- Number
- Publications
semantic analysis
- Corpus
- Papers
- Computer
- Science
- Project
- Language
- Machine
- Natural
- Processing
- Summaries
- Also
- Learning
google scholar
- Semantic
- Corpus
- Computer
- Science
- Papers
- Scientific
- Also
- Project
- Search
- Million
- Neuroscience
- Artificial
basic corpus for ai discovery tools
- Allen
- Institute
- Scholar
- Tools
- Semantic
- Research
- Users
- Also
- Literature
- Search
- Corpus
- Million
human–computer interaction
- Science
- Neuroscience
- Engines
- Scholar
- Search
- Semantic
- Million
- Papers
- Corpus
- Began
- Graph
- Intelligence

Connections between topic areas Semantic bridges

For Semantic Scholar, one of the stronger structural bridges in this analysis connects Semantic Scholar with Technology. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3

Semantic Scholar — Technology · splits 27 ⟂ 11

Semantic Scholar — Overview · splits 28 ⟂ 10

Semantic Scholar — Article identifier · splits 33 ⟂ 5

Semantic Scholar — Number of users and publications · splits 33 ⟂ 5

Semantic Scholar — Basic corpus for AI discovery tools · splits 34 ⟂ 4

Map overview Semantic statistics

Semantic Scholar

Nodes38

Edges37

Triples40

Avg. degree1.95

Density0.052632

Components1

Source & methodology

TTTA analyzes the structure around Semantic Scholar to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Technology, Applications & Science, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Semantic Scholar · EN edition · Analysis: TopicsToTalkAbout