ConceptNet 5.5: An Open Multilingual Graph of General Knowledge

Abstract

Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies.

ISEEQ: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval and Knowledge Graphs

“…Inputs to ISEEQ are IS queries described in natural language. For instance, an IS query can be described with SQE: We expand the possibly short user input queries with the help of ConceptNet Commonsense Knowledge Graph (CNetKG) (Speer, Chin, and Havasi 2017). We first extract the entity set E d in a user query description d using CNetKG.…”

HEAL: A Knowledge Graph for Distress Management Conversations

“…2 YAGO (Fabian et al 2007), Freebase (Bollacker et al 2008), and Wikidata (Vrandečić and Krötzsch 2014) are some other examples of knowledge graphs built on general knowledge extracted from the web. More recent knowledge graphs such as Con-ceptNet (Speer, Chin, and Havasi 2017), ATOMIC (Sap et al 2019), and ASER ) focus on representing different types of commonsense knowledge. Works by Liu et al (2018) and leverage the factoid and commonsense knowledge present in these graphs to develop open-domain conversational agents that produces more semantic and informative responses.…”

“…Though large-scale knowledge graphs such as Concept-Net (Speer, Chin, and Havasi 2017) and ATOMIC (Sap et al 2019) exist, they mainly assist in open-domain conversation generation by capturing factual knowledge and embedding chatbot models with simple commonsense reasoning capabilities. Since they were not developed to capture norms of empathetic exchanges, this field lacks linguistic resources and models to assist distress management and empathetic response generation.…”

Experiencing the Conceptual Wealth of Non‐Derived Denominal Verbs: A Multi‐Level, Simulation‐Based Approach*

“…‘cause to come into existence’) is active in the ‘information’ reading “to write for a newspaper”. An open multilingual graph of general knowledge that is based on qualia structures was developed by Speer, Chin & Havasi (2017). The relations specified in this network, which were automatically obtained from a variety of sources including expert‐created resources, crowd‐sourcing, and games with a purpose, provide an adequate starting‐point for the creation of low‐level situations involving the base‐noun participant.…”