ConceptNet Knowledge Base
A ConceptNet Knowledge Base is a semantic network based on the information in the OMCS database produced by the OMCS project.
- AKA: ConceptNet.
- Context:
- Website: https://conceptnet.media.mit.edu
- …
- Example(s):
- Counter-Example(s):
- See: CommonSense KB, ConceptNet Semantic Relation Ontology, Informal Ontology, Lexical Database.
References
2022a
- (ConceptNet, 2022) ⇒ https://conceptnet.media.mit.edu Retrieved:2022-11-27.
- QUOTE: ConceptNet is a freely-available semantic network, designed to help computers understand the meanings of words that people use.
ConceptNet originated from the crowdsourcing project Open Mind Common Sense, which was launched in 1999 at the MIT Media Lab. It has since grown to include knowledge from other crowdsourced resources, expert-created resources, and games with a purpose.
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ConceptNet is used to create word embeddings -- representations of word meanings as vectors, similar to word2vec, GloVe, or fastText, but better.
These word embeddings are free, multilingual, aligned across languages, and designed to avoid representing harmful stereotypes. Their performance at word similarity, within and across languages, was shown to be state of the art at SemEval 2017.
- QUOTE: ConceptNet is a freely-available semantic network, designed to help computers understand the meanings of words that people use.
2022b
- (Wikipedia, 2022) ⇒ https://en.wikipedia.org/wiki/Open_Mind_Common_Sense Retrieved:2022-11-27.
- Open Mind Common Sense (OMCS) is an artificial intelligence project based at the Massachusetts Institute of Technology (MIT) Media Lab whose goal is to build and utilize a large commonsense knowledge base from the contributions of many thousands of people across the Web. It has been active from 1999 to 2016.
Since its founding, it has accumulated more than a million English facts from over 15,000 contributors in addition to knowledge bases in other languages. Much of OMCS's software is built on three interconnected representations: the natural language corpus that people interact with directly, a semantic network built from this corpus called ConceptNet, and a matrix-based representation of ConceptNet called AnalogySpace that can infer new knowledge using dimensionality reduction.[1] The knowledge collected by Open Mind Common Sense has enabled research projects at MIT and elsewhere.
- Open Mind Common Sense (OMCS) is an artificial intelligence project based at the Massachusetts Institute of Technology (MIT) Media Lab whose goal is to build and utilize a large commonsense knowledge base from the contributions of many thousands of people across the Web. It has been active from 1999 to 2016.
- ↑ Robyn Speer, Catherine Havasi, and Henry Lieberman. AnalogySpace: Reducing the Dimensionality of Common Sense Knowledge Archived 2010-07-09 at the Wayback Machine. AAAI 2008.
2022c
- (Wikipedia, 2022) ⇒ https://en.wikipedia.org/wiki/Open_Mind_Common_Sense#ConceptNet Retrieved:2022-11-27.
- ConceptNet is a semantic network based on the information in the OMCS database. ConceptNet is expressed as a directed graph whose nodes are concepts, and whose edges are assertions of common sense about these concepts. Concepts represent sets of closely related natural language phrases, which could be noun phrases, verb phrases, adjective phrases, or clauses.[1]
ConceptNet is created from the natural-language assertions in OMCS by matching them against patterns using a shallow parser. Assertions are expressed as relations between two concepts, selected from a limited set of possible
relations. The various relations represent common sentence patterns found in the OMCS corpus, and in particular, every "fill-in-the-blanks" template used on the knowledge-collection Web site is associated with a particular relation.[1]
The data structures that make up ConceptNet were significantly reorganized in 2007, and published as ConceptNet 3.[1] The Software Agents group currently distributes a database and API for the new version 4.0.[2]
In 2010, OMCS co-founder and director Catherine Havasi, with Robyn Speer, Dennis Clark and Jason Alonso, created Luminoso, a text analytics software company that builds on ConceptNet.[3] [4] [5] [6] It uses ConceptNet as its primary lexical resource in order to help businesses make sense of and derive insight from vast amounts of qualitative data, including surveys, product reviews and social media.[3][7] [8]
- ConceptNet is a semantic network based on the information in the OMCS database. ConceptNet is expressed as a directed graph whose nodes are concepts, and whose edges are assertions of common sense about these concepts. Concepts represent sets of closely related natural language phrases, which could be noun phrases, verb phrases, adjective phrases, or clauses.[1]
- ↑ 1.0 1.1 1.2 Catherine Havasi, Robyn Speer and Jason Alonso. ConceptNet 3: a Flexible, Multilingual Semantic Network for Common Sense Knowledge. Proceedings of Recent Advances in Natural Language Processing, 2007. try ConceptNet 3:... Archived 2015-05-29 at the Wayback Machine
- ↑ Commonsense Computing Initiative (2009-02-24). "ConceptNet API in Launchpad". Retrieved 2009-10-07.
- ↑ 3.0 3.1 Lohr, Steve (27 June 2014). "The U.S.-Germany Match Through a Social Media Lens". New York Times. Retrieved 3 March 2015.
- ↑ Rusli, Evelyn (14 April 2014). "Firms Use Artificial Intelligence to Tap Shoppers' Views". The Wall Street Journal. Retrieved 3 March 2015.
- ↑ Alba, Davey (12 February 2015). "The Startup That Helps You Analyze Twitter Chatter in Real Time". Wired. Retrieved 3 March 2015.
- ↑ Noyes, Katherine (11 February 2015). "Luminoso to enterprises: Here's what all that chatter really means". PC World. Retrieved 3 March 2015.
- ↑ Miller, Ron (2 July 2014). "Luminoso Lands $6.5M In Series A To Keep Building Cloud Text Analytics Service". TechCrunch. Retrieved 3 March 2015.
- ↑ Darrow, Barb (11 February 2015). "Luminoso brings its text analysis smarts to streaming data". GigaOm. Retrieved 3 March 2015.
2015
- (GitHub, 2015) ⇒ https://github.com/commonsense/conceptnet5/wiki
- QUOTE: ConceptNet is a multilingual knowledge base, representing words and phrases that people use and the common-sense relationships between them. The knowledge in ConceptNet is collected from a variety of resources, including crowd-sourced resources (such as Wiktionary and Open Mind Common Sense), games with a purpose (such as Verbosity and nadya.jp), and expert-created resources (such as WordNet and JMDict).
2007
- (Havasi et al., 2007) ⇒ C. Havasi, R. Speer, and J. Alonso. (2007). “ConceptNet 3: a Flexible, Multilingual Semantic Network for Common Sense Knowledge.” In: Proceedings of Recent Advances in Natural Languges Processing.
2004a
- (Liu & Sing, 2004a) ⇒ H. Liu, and P. Singh. (2004). “ConceptNet: A Practical Commonsense Reasoning Toolkit.” In: BT Technology Journal, 22.
2004b
- (Liu & Sing, 2004b) ⇒ H. Liu, and P. Singh. (2004). “Commonsense Reasoning in and over Natural Language.” In: Proceedings of the 8th International Conference on Knowledge-based Intelligent Information & Engineering Systems (KES'2004).