Query: Conceptnet

title: 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.

title: ConceptNet at SemEval 2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge
abstract: This paper describes Luminoso's participation in SemEval 2017 Task 2, "Multilingual and Cross lingual Semantic Word Similarity" with a system based on ConceptNet.  knowledge graph that focuses on general knowledge that relates the meanings of words and phrases. Our submission to SemEval was an update of previous work that builds high quality, multilingual word embeddings from a combination of ConceptNet and distributional semantics. Our system took first place in both subtasks. It ranked first in 4 out of 5 of the separate languages, and also ranked first in all 10 of the cross lingual language pairs.

title: Representing General Relational Knowledge in ConceptNet 5
abstract: ConceptNet is a knowledge representation project, providing a large semantic graph that describes general human knowledge and how it is expressed in natural language. This paper presents the latest iteration, ConceptNet 5, including its fundamental design decisions, ways to use it, and evaluations of its coverage and accuracy.

title: ConceptNet 5: A Large Semantic Network for Relational Knowledge
abstract: ConceptNet is a knowledge representation project, providing a large semantic graph that describes general human knowledge and how it is expressed in natural language. Here we present the latest iteration, ConceptNet 5, with a focus on its fundamental design decisions and ways to interoperate with it.

title: Assessing the Difficulty of Classifying ConceptNet Relations in a Multi Label Classification Setting
abstract: Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in CONCEPTNET, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi word arguments of diverse semantic types. We explore a neural open world multi label classification approach that focuses on the evaluation of classification accuracy for individual relations. Based on an in depth study of the specific properties of the CONCEPTNET resource, we investigate the impact of different relation representations and model variations. Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address. We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work.

title: ConceptNet A Practical Commonsense Reasoning Tool Kit
abstract: ConceptNet is a freely available commonsense knowledge base and natural language processing tool kit which supports many practical textual reasoning tasks over real world documents including topic gisting, analogy making, and other context oriented inferences. The knowledge base is a semantic network presently consisting of over 1.6 million assertions of commonsense knowledge encompassing the spatial, physical, social, temporal, and psychological aspects of everyday life. 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.

title: Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness
abstract: In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of information for such characterizations by encoding relations between concepts as edges in a graph. When two concepts are not directly connected by an edge, their relationship can still be described in terms of the paths that connect them. Unfortunately, many of these paths are uninformative and noisy, which means that the success of applications that use such path features crucially relies on their ability to select high quality paths. In existing applications, this path selection process is based on relatively simple heuristics. In this paper we instead propose to learn to predict path quality from crowdsourced human assessments. Since we are interested in a generic task independent notion of quality, we simply ask human participants to rank paths according to their subjective assessment of the paths' naturalness, without attempting to define naturalness or steering the participants towards particular indicators of quality. We show that a neural network model trained on these assessments is able to predict human judgments on unseen paths with near optimal performance. Most notably, we find that the resulting path selection method is substantially better than the current heuristic approaches at identifying meaningful paths.

title: Construction of a Diagnosis Representation Model of Person with Dementia Based on ConceptNet for Deeper Understanding of Physical and Mental Conditions
abstract: Construction of a Diagnosis Representation Model of Person with Dementia Based on ConceptNet for Deeper Understanding of Physical and Mental Conditions 0Naoki Kamiya, Takumi Yoshizawa, Shogo Ishikawa, Hideki Ueno, Mia Kobayashi, Minoru Maeda, Chiaki Nishiyama, Yujun Murakami, Shinya Kiriyama, Yoichi Takebayashi (1. Shizuoka University, 2. Chiba University Hospital, 3. Orange Cross Foundation, 4. The Society of Citizen Informatics for Human Cognitive Disorder)

title: A Concise Conversion Model for Improving the RDF Expression of ConceptNet Knowledge Base
abstract: With the explosive growth of information on the Web, Semantic Web and related technologies such as linked data and commonsense knowledge bases, have been introduced. ConceptNet is a commonsense knowledge base, which is available for public use in CSV and JSON format; it provides a semantic graph that describes general human knowledge and how it is expressed in natural language. Recently, an RDF presentation of ConceptNet called ConceptRDF has been proposed for better use in different fields; however, it has some problems (e.g. information of concepts is sometimes misexpressed) caused by the improper conversion model. In this paper, we propose a concise conversion model to improve the RDF expression of ConceptNet. We convert the ConceptNet into RDF format and perform some experiments with the conversion results. The experimental results show that our conversion model can fully express the information of ConceptNet, which is suitable for developing many intelligent applications.

title: Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries
abstract: Query expansion is an important and commonly used technique for improving Web search results. Existing methods for query expansion have mostly relied on global or local analysis of document collection, click through data, or simple ontologies such as WordNet. In this paper, we present the results of a systematic study of the methods leveraging the ConceptNet knowledge base, an emerging new Web resource, for query expansion. Specifically, we focus on the methods leveraging ConceptNet to improve the search results for poorly performing (or difficult) queries. Unlike other lexico semantic resources, such as WordNet and Wikipedia, which have been extensively studied in the past, ConceptNet features a graph based representation model of commonsense knowledge, in which the terms are conceptually related through rich relational ontology. Such representation structure enables complex, multi step inferences between the concepts, which can be applied to query expansion. We first demonstrate through simulation experiments that expanding queries with the related concepts from ConceptNet has great potential for improving the search results for difficult queries. We then propose and study several supervised and unsupervised methods for selecting the concepts from ConceptNet for automatic query expansion. The experimental results on multiple data sets indicate that the proposed methods can effectively leverage ConceptNet to improve the retrieval performance of difficult queries both when used in isolation as well as in combination with pseudo relevance feedback.