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.
ConceptNet represents data in the form of a semantic network, and makes it available to be used in natural language processing and intelligent user interfaces.
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
Bayesian Networks are probabilistic structured representations of domains which have been applied to monitoring and manipulating cause and effects for modelled systems as disparate as the weather, disease and mobile telecommunications networks. Although useful, Bayesian Networks are notoriously difficult to build accurately and efficiently which has somewhat limited their application to real world problems. Ontologies are also a structured representation of knowledge, encoding facts and rules about a given domain. This paper outlines an approach to harness the knowledge and inference capabilities inherent in an ontology model to automate the construction of Bayesian Networks to accurately represent a domain of interest. The approach was implemented in the context of an adaptive, self-configuring network management system in the telecommunications domain. In this system, the ontology model has the dual function of knowledge repository and facilitator of automated workflows and the generated BN serves to monitor effects of management activity, forming part of a feedback look for self-configuration decisions and tasks.
ata are provided as such, without any warranty of consistency from the provider, and the retrievers have to make it consistent before introducing it into their own databases. Other frameworks are also different in this respect: neither the knowledge sharing (when the opportunity to modify the shared knowledge is considered) nor the software agents provide any warranty about the consistency of the knowledge they provide.
DBin is a Semantic Web application that enables groups of users
with a common interest to cooperatively create semantically
structured knowledge bases. These user groups, which we call
“Semantic Web Communities”, are made possible by creating
customized user environments called “Brainlets”. Brainlets
provide user interfaces and domain specific tools (e.g. querying,
viewing and editing facilities) which enable community
participants to interact with the data of interest. Brainlets are
directly created by domain experts using an XML description
language. DBin clients communicate and exchange annotations
using a P2P infrastructure. Access control and digital signatures
put by DBin inside the authored RDF enable trust and information
filtering. In this paper we show a specific use case where a
“Semantic Web Community” is created to enable a group of users
to share their del.icio.us tags and organize them into a
cooperatively built RDFS ontology.
Charter: To define components of the draft standard "RDA - Resource Description and Access" as an RDF vocabulary for use in developing a Dublin Core application profile
"In Semantic Web languages, such as RDF and OWL, a property is a binary relation: it is used to link two individuals or an individual and a value. However, in some cases, the natural and convenient way to represent certain concepts is to use relations to link an individual to more than just one individual or value. These relations are called n-ary relations. For example, we may want to represent properties of a relation, such as our certainty about it, severity or strength of a relation, relevance of a relation, and so on. Another example is representing relations among multiple individuals, such as a buyer, a seller, and an object that was bought when describing a purchase of a book. This document presents ontology patterns for representing n-ary relations in RDF and OWL and discusses what users must consider when choosing these patterns."
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