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FrameNet: Applications & Measurement
FrameNet is a group of online lexical databases based upon the theory of meaning known as Frame semantics, developed by linguist Charles J. Fillmore. The project's fundamental notion is simple: most words' meanings may be best understood in terms of a semantic frame, which is a description of a certain kind of event, connection, or item and its actors.
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FrameNet topic overview
The analysis highlights Applications and Measurement as prominent areas in the source structure around FrameNet. 1 topic appears in more than one source area, which can help identify connections that are less obvious in a linear reading.
Related topics
20
Source areas
3
Connected nodes
24
Extracted relationships
78
Concept neighborhoods
8
Bridge connections
24
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.
Concepts · 9 topics
Applications · 7 topics
Overview · 5 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.
Owner
Collin Baker (current project manager)
Location
International Computer Science Institute in Berkeley, California
Commercial?
No (freely available for download)
Established
1997; 29 years ago (1997)
Founder
Charles J. Fillmore
Mission statement
Building a lexical database based on a theory of meaning called frame semantics.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
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.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How FrameNet connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around FrameNet shows recurring relationship patterns in the source. For example, FrameNet → Anything, Arrest, Causative, Connects, Death, For, Her, Inchoative, Inheritance, Judgment, Metaphor, Perspective, Precedes, See, She, Some, Statement, Suasion, Subframe, These Another extracted example is FrameNet → Archived, Baker, Berkeley, CA, Christopher, Collin, Ellsworth, Extended Theory, FrameNet II, International Computer Science Institute, Jan, Johnson, Josef, Michael, Miriam, November, PDF, Petruck, Practice, Ruppenhofer. Use these groups to spot repeated connection types before inspecting the individual relationships.
FrameNet
Top relations
related to Frame relations· 24
FrameNet → Anything, Arrest, Causative, Connects, Death, For, Her, Inchoative, Inheritance, Judgment, Metaphor, Perspective, Precedes, See, She, Some, Statement, Suasion, Subframe, These
related to Further reading· 21
FrameNet → Archived, Baker, Berkeley, CA, Christopher, Collin, Ellsworth, Extended Theory, FrameNet II, International Computer Science Institute, Jan, Johnson, Josef, Michael, Miriam, November, PDF, Petruck, Practice, Ruppenhofer
has application· 14
FrameNet → Daniel Gildea, Daniel Jurafsky, English FrameNets, German, Japanese, John, Mary, Natural Language Toolkit, Polish, Semantic Role Labeling, Since, Spanish, SRL, The
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 78 structured relationships around FrameNet. Examples in this analysis include FrameNet → Commercial? → No (freely available for download) and FrameNet → Established → 1997; 29 years ago (1997). The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
FrameNet
Commercial?
No (freely available for download)
1.00
infobox
FrameNet
Established
1997; 29 years ago (1997)
1.00
infobox
FrameNet
Founder
Charles J. Fillmore
1.00
infobox
FrameNet
Location
International Computer Science Institute in Berkeley, California
1.00
infobox
FrameNet
Mission statement
Building a lexical database based on a theory of meaning called frame semantics.
1.00
infobox
FrameNet
Owner
Collin Baker (current project manager)
1.00
infobox
FrameNet
Type of project
Lexical database (containing: frames, frame elements(FE), lexical units (LU), examples sentences, and frame relations)
1.00
infobox
FrameNet
Website
framenet.icsi.berkeley.edu
1.00
infobox
FrameNet
is a
group of online lexical databases based upon the theory of meaning known as Frame semantics
0.90
text
FrameNet
has application
John
0.60
section
FrameNet
has application
Mary
0.60
section
FrameNet
has application
Semantic Role Labeling
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around FrameNet bring nearby vocabulary together. In this analysis, examples include Frame, Semantic and Elements. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
FrameNet
Frame
Semantic
Elements
Lexical
Sentences
Example
Theory
Based
Labeling
Like
Role
Relations
framenet
Frame
Semantic
Elements
Lexical
Sentences
Example
Theory
Based
Labeling
Like
Role
Relations
frame semantics
Theory
Elements
Example
Database
Lexical
Sentences
Framenet
Frames
Specific
Associated
Born
Relations
subcategorization frames
Sentences
Relations
Lexical
Semantic
Database
Examples
Need
One
Word
Fe
Role
Associated
lexical units
Lexical
Units
Relations
Specific
Database
Lus
Sentences
Semantics
Elements
Theory
Example
Also
lexical databases
Units
Relations
Specific
Sentences
Semantics
Elements
Database
Lus
Theory
Example
Meaning
Associated
charles j. fillmore
Semantics
Database
Examples
Fillmore
Lexical
Theory
Fe
Meaning
Relations
Units
Sentences
Elements
semantic role labeling
Labeling
Role
Semantic
One
Sentences
Word
Like
Sentence
Example
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For FrameNet, one of the stronger structural bridges in this analysis connects FrameNet with Concepts. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
FrameNet
Nodes25
Edges24
Triples78
Avg. degree1.92
Density0.08
Components1
Source & methodology
TTTA analyzes the structure around FrameNet to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications & Measurement, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
