with the ConceptNet Flash Browser Now! Click here!
is ConceptNet? [top]
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
(e.g. a news article containing the concepts, “gun,”
“convenience store,” “demand money”
and “make getaway” might suggest the topics “robbery”
(e.g. this email is sad and angry),
(e.g. “scissors,” “razor,” “nail
clipper,” and “sword” are perhaps like a “knife”
because they are all “sharp,” and can be used to
other context-oriented inferences
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 authors.
is a unique resource in that it captures a wide range of commonsense
concepts and relations, such as those found in the Cyc
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!
about ConceptNet [top]:
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 Publishers.
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)
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
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
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.
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, OMCSNet=ConceptNet.
Versions of ConceptNet
and Tools for Previous Versions of ConceptNet
Concept Processing Toolkit
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 Edison Thomaz.
/ CGI Interface to OMCSNet - Query
the OMCSNetCPP Semantic Network and inference tools from a web
form / cgi-bin! Written by Stuart Horner, using OMCSNetCPP.
wordsense disambiguated version of OMCSNet, merged with WordNet
data, and annotated with part-of-speech information. Written
by Elliot Turner
Using ConceptNet [top]
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)
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.
Stocky, Alexander Faaborg, Henry Lieberman (2004). A Commonsense
Approach to Predictive Text Entry. Conference on Human Factors
in Computing Systems (CHI 04), Vienna, Austria. (website)
Liu (2003). Unpacking Meaning from Words: A Context-Centered Approach
to Computational Lexicon Design. CONTEXT 2003: 218-232
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).
Musa, Madleina Scheidegger, Andrea Kulas, Yoan Anguilet (2003).
GloBuddy, a Dynamic Broad Context Phrase Book. Proceedings of
the International and Interdisciplinary Conference on Modeling Context.
pp. 467-474 (paper)
Wang. (2002). Turning-taking in a Collaborative Storytelling Agent.
Masters Thesis. MIT Department of Electrical Engineering and