Recent years have witnessed the release of many open-source and enterprise-driven knowledge graphs with a dramatic increase of applications of knowledge representation and reasoning in fields such as natural language processing, computer vision, and bioinformatics. With those large-scale knowledge graphs,...
In the predicate logic system of knowledge representation, it is assumed that the word contains object, relations, and functions. The Predicate logic is a symbolized reasoning in which we can divide the sentence into a well-defined subject and predicate. The subject is defined by the predicate. ...
Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language. Learning There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For ...
Knowledge representation & reasoning (KRR) provides the foundation for NLP, IE and TI to represent knowledge symbolically and enable automated reasoning and computation over the representation. Over the years, we have worked closely with our product team partners at Office 365 (opens in new tab),...
This approach is scalable and can handle millions of pieces of knowledge to find the common sense responses for a given situation.Rakesh GuptaVasco Calais PedroLink AnalysisGupta, R., Pedro, V.C.: Knowledge Representation and Bayesian Inference for Response to Situations. In: AAAI 2005 Workshop...
Velardi, Representation and control strategies for large knowledge domains: an application to NLP, Applied Artificial Intelligence, v.2 n.3-4, p.213-249, 1988 [doi>10.1080/08839518808949909]Representation and Control Strategies for large Knowledge Domains: An Application to NLP - Antonacci, Russo,...
The function of the Embedding Layer (EL) is to convert the sentence tree into an embedding representation that can be fed into the Mask-Transformer. Similar to BERT, the embedding representation of K-BERT is the sum of three parts: token embedding, position embedding, and segment embedding, ...
The knowledge representation module reorganizes and converts triple knowledge into LLM-friendly formats, reducing the complexity of reasoning. Therefore, KnowledgeNavigator can be directly used for various LLMs and KGs without retraining or fine-tuning. KnowledgeNavigator is evaluated on various KGQA ...
combining the two reasoning methods. In this survey, we take a thorough look at the development of the symbolic, neural and hybrid reasoning on knowledge graphs. We survey two specific reasoning tasks — knowledge graph completion and question answering on knowledge graphs, and explain them in a...
For this purpose other kinds of knowledge representation and reasoning are needed, as will be further discussed in Section 3. Show moreView article Review article Law and logic: A review from an argumentation perspective Artificial Intelligence Journal2015, Artificial Intelligence Henry Prakken, Giovanni...