the ConceptNet Flash Browser Now! Click
is ConceptNet? [top]
ConceptNet is a freely available commonsense knowledgebase
and natural-language-processing toolkit which supports many
practical textual-reasoning tasks over real-world documents right
out-of-the-box (without additional statistical training) including
- topic-jisting (e.g. a news
article containing the concepts, “gun,” “convenience store,”
“demand money” and “make getaway” might suggest the topics
“robbery” and “crime”),
- affect-sensing (e.g. this email
is sad and angry),
- analogy-making (e.g. “scissors,”
“razor,” “nail clipper,” and “sword” are perhaps like a “knife”
because they are all “sharp,” and can be used to “cut
- text summarization
- contextual expansion
- causal projection
- cold document classification
- and other context-oriented inferences
The ConceptNet knowledgebase is a semantic
network presently available in two versions: concise (200,000
assertions) and full (1.6 million assertions). Commonsense knowledge
in ConceptNet encompasses the spatial, physical, social, temporal,
and psychological aspects of everyday life. Whereas similar
large-scale semantic knowledgebases like Cyc and WordNet are
carefully handcrafted, ConceptNet is generated automatically from
the 700,000 sentences of the Open Mind Common Sense
Project – a World Wide Web based collaboration with over 14,000
ConceptNet is a unique resource in that it captures a wide
range of commonsense concepts and relations, such as those found in
knowledgebase, yet this knowledge is structured not as a complex
and intricate logical framework, but rather as a simple, easy-to-use
semantic network, like WordNet. While ConceptNet still supports many
of the same applications as WordNet, such as
query expansion and determining semantic similarity, its focus on
concepts-rather-than-words, its more diverse relational ontology,
and its emphasis on informal conceptual-connectedness over formal
linguistic-rigor allow it to go beyond WordNet to make practical,
context-oriented, commonsense inferences over real-world
At the end of the day, we want ConceptNet to be simply useful
to AI Researchers and computer enthusiasts who want to experiment
with adding commonsense to make their smart robots and programs
smarter. And it's working! ConceptNet is currently driving tens of
new innovative research projects at MIT and elsewhere!
Papers about ConceptNet [top]:
A good overview paper of ConceptNet v2.1, Liu, H.
& Singh, P. (2004) ConceptNet: A Practical Commonsense Reasoning Toolkit.
BT Technology Journal, To Appear. Volume 22, forthcoming
issue. Kluwer Academic
Focusing on ConceptNet's natural language knowledge
representation, Liu, H. & Singh, P. (2004).
Commonsense Reasoning in and over Natural Language
Proceedings of the 8th International Conference on Knowledge-Based
Intelligent Information & Engineering Systems (KES'2004).
Wellington, New Zealand. September 22-24. Lecture Notes in
Artificial Intelligence, Springer 2004 (paper)
Download ConceptNet [top]
ConceptNet v2.1 is different from previous
versions in that it is integrated and distributed with the MontyLingua
natural language toolkit. Unfortunately, ConceptNet v2.1 datafiles
are not backward-compatible, but previous versions may be downloaded
ConceptNet v2.1 is written in the Python programming language (to download Python, click
here). It is distributed as a Python API and a standalone XML-RPC
Server. If you wish to access ConceptNet from other programming
languages such as Java, C++, or the .NET Platform, simply launch
ConceptNet's XML-RPC Server (self-documenting) and then interface
with ConceptNet via a simple XML-RPC client, which is available for
all major programming languages. Sample client code is available in
this great XML-RPC
Please fill out the following information to proceed to
the download of ConceptNet Version 2.1
for Python and the standalone XML-RPC Server. This
information will not be shared or sold with others.
ConceptNet Extensions [top]
Thanks to contributors from the ConceptNet community,
ConceptNet is implemented in several programming languages, and we
now have a variety of ConceptNet-related tools and browsers. If you
would like your tool added to this list, please email us. Remember,
Versions of ConceptNet
and Tools for Previous Versions of ConceptNet
RubyCon: Concept Processing Toolkit -
RubyCon builds upon the work of the ConceptNet project
(version 1.3.1), implementing ConceptNet’s semantic network of
280,000+ assertions and graph-processing algorithms into a set of
reusable objects in the Ruby programming language.
Browser for MacOS X - A simple
Cocoa-based knowledge browser for OMCSNet. The package includes
source code, project files, and OMCSNet data files. It shows how
to incorporate OMCSNet within an Objective-C app. Written by
Web / CGI
Interface to OMCSNet - Query the
OMCSNetCPP Semantic Network and inference tools from a web form /
cgi-bin! Written by Stuart Horner, using OMCSNetCPP.
- A wordsense disambiguated version of OMCSNet, merged with
WordNet data, and annotated with part-of-speech information.
Written by Elliot Turner
Research Using ConceptNet [top]
S. A. Inverso, N. Hawes, J. Kelleher, R. Allen, and K.
Haase."Think And Spell: Context-Sensitive Predictive Text for an
Ambiguous Keyboard Brain-Computer Interface Speller", to appear in
the Journal of Biomedizinische Technik special issue Proceedings of
the 2nd International Brain-Computer Interface Workshop and Training
Course, Graz, Austria, September 16-18, 2004 (paper)
Ashwani Kumar, Sharad C. Sundararajan, Henry Lieberman
(2004). Common Sense Investing: Bridging the Gap Between Expert and
Novice. Conference on Human Factors in Computing Systems (CHI
04), Vienna, Austria. (paper)
Tom Stocky, Alexander Faaborg, Henry Lieberman (2004). A
Commonsense Approach to Predictive Text Entry. Conference on
Human Factors in Computing Systems (CHI 04), Vienna, Austria.
Hugo Liu (2003). Unpacking Meaning from Words: A
Context-Centered Approach to Computational Lexicon Design.
CONTEXT 2003: 218-232 (paper)
Nathan Eagle, Push Singh, and Alex (Sandy) Pentland (2003).
Common sense conversations: understanding casual conversation using
a common sense database. Proceedings of the Artificial
Intelligence, Information Access, and Mobile Computing Workshop
(IJCAI 2003). Acapulco, Mexico.
Rami Musa, Madleina Scheidegger, Andrea Kulas, Yoan Anguilet
GloBuddy, a Dynamic Broad Context Phrase Book.
Proceedings of the International and Interdisciplinary
Conference on Modeling Context. pp. 467-474 (paper)
Austin Wang. (2002). Turning-taking in a Collaborative
Storytelling Agent. Masters Thesis. MIT Department of Electrical
Engineering and Computer Science.