CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents

Rosaci, Domenico

doi:10.1016/j.is.2006.06.003

Cited by 46 publications

(32 citation statements)

References 34 publications

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“…For analyzers, it is difficult to understand the trained neural networks. So, symbolic knowledge extraction from trained neural networks is generally used for solving "Black Boxes" problems appearing in neural networks 28 . Through structural analysis method and performance analysis method, symbolic knowledge can be extracted from trained neural networks 29 .…”

Section: Discussionmentioning

confidence: 99%

A Performance Comparison of Neural Networks in Forecasting Stock Price Trend

Wu¹,

Duan²

2017

IJCIS

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The stock price shows the character of complex non-linear system, along with changes of internal and external environmental factors in stock market. As a form of artificial intelligence, neural network can fully reveal the complex relationship between investors and price fluctuations. After comparing network structures of different neural networks, the conclusions show Elman neural network has an obvious advantage over BP neural network in predicting price trend of Chinese stock market both in theory and practice.

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Section: Discussionmentioning

confidence: 99%

A Performance Comparison of Neural Networks in Forecasting Stock Price Trend

Wu¹,

Duan²

2017

IJCIS

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“…Background knowledge or domain knowledge such as likings of the user is very necessary for understanding situations and problems in Information management systems. To understand mutual monitoring for enhancing recommendation quality in autonomous multiagent systems, [9] focused on ontologies and ANNs to serve as behavior and interests of users.…”

Section: Related Workmentioning

confidence: 99%

Automatic Extraction of Attributes and Entities for Product Differentiation

Thakare¹,

Deshpande²

2018

IJCIS

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This paper introduces a novel concept of web-spreadsheet, which extracts product information by crawling through related web pages and generates information like a spreadsheet where each row represents product information and each column represents product attributes. Using Decision Tree based classifier, the accuracy achieved for extracting entities and attribute-value pairs is 98.71% and 99.99% respectively. This strategy will benefit the product companies for performing successful product differentiation and save a lot of time of the customers for comparing different products of same product class.

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“…Communication between agents, apart from being a time-consuming task, is a major obstacle when they are supported by different ontology. Endowing the system with inter-ontology capabilities [42] or semantic negotiation [22] are some solutions to this problem. Besides, Ontology support is a useful way of enhancing the model capabilities [23], unifying different terms for the same concept and enriching the available information regarding the current user.…”

Section: State Of the Art In User Modelingmentioning

confidence: 99%

Statistical user model supported by R-Tree structure

et al. 2013

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This paper is about developing a group user model able to predict unknown features (attributes, preferences, or behaviors) of any interlocutor. Specifically, for systems where there are features that cannot be modeled by a domain expert within the human computer interaction. In such cases, statistical models are applied instead of stereotype user models. The time consumption of these models is high, and when a requisite of bounded response time is added most common solution involves summarizing knowledge. Summarization involves deleting knowledge from the knowledge base and probably losing accuracy in the medium-term. This proposal provides all the advantages of statistical user models and avoids knowledge loss by using an R-Tree structure and various search spaces (universes of users) of diverse granularity for solving inferences with enhanced success rates. Along with the formalization and evaluation of the approach, main advantages will be discussed, and a perspective for its future evolution is provided. In addition, this paper provides a framework to evaluate statistical user models and to enable performance comparison among different statistical user models.

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CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents

Cited by 46 publications

References 34 publications

A Performance Comparison of Neural Networks in Forecasting Stock Price Trend

A Performance Comparison of Neural Networks in Forecasting Stock Price Trend

Automatic Extraction of Attributes and Entities for Product Differentiation

Statistical user model supported by R-Tree structure

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