Storing fuzzy description logic ontology knowledge bases in fuzzy relational databases

2017 ◽  
Vol 48 (1) ◽  
pp. 220-242 ◽  
Author(s):  
Fu Zhang ◽  
Z. M. Ma ◽  
Qiang Tong ◽  
Jingwei Cheng
Keyword(s):  
Relational Databases ◽  
Description Logic ◽  
Knowledge Bases ◽  
Fuzzy Description

NUMERIC PREDICTION ON OWL KNOWLEDGE BASES THROUGH TERMINOLOGICAL REGRESSION TREES

2012 ◽  
Vol 06 (04) ◽  
pp. 429-446 ◽  
Author(s):  
NICOLA FANIZZI ◽  
CLAUDIA D'AMATO ◽  
FLORIANA ESPOSITO ◽  
PASQUALE MINERVINI
Keyword(s):  
Relational Databases ◽  
Description Logic ◽  
Regression Trees ◽  
Knowledge Bases ◽  
Semantic Knowledge ◽  
The Novel ◽  
Regression Problems ◽  
Logical Regression ◽  
Definition Of ◽  
New Framework

In the context of semantic knowledge bases, among the possible problems that may be tackled by means of data-driven inductive strategies, one can consider those that require the prediction of the unknown values of existing numeric features or the definition of new features to be derived from the data model. These problems can be cast as regression problems so that suitable solutions can be devised based on those found for multi-relational databases. In this paper, a new framework for the induction of logical regression trees is presented. Differently from the classic logical regression trees and the recent fork of the terminological classification trees, the novel terminological regression trees aim at predicting continuous values, while tests at the tree nodes are expressed with Description Logic concepts. They are intended for multiple uses with knowledge bases expressed in the standard ontology languages for the Semantic Web. A top-down method for growing such trees is proposed as well as algorithms for making predictions with the trees and deriving rules. The system that implements these methods is experimentally evaluated on ontologies selected from popular repositories.

Deciding Query Entailment for Fuzzy Description Logic Knowledge Bases：The f-SH Family

2012 ◽  
Vol 35 (4) ◽  
pp. 767-785
Author(s):  
Jing-Wei CHENG ◽  
Zong-Min MA ◽  
Li YAN ◽  
Fu ZHANG
Keyword(s):  
Description Logic ◽  
Fuzzy Description

An Improved Set-based Reasoner for the Description Logic 𝒟ℒD4,׆

2021 ◽  
Vol 178 (4) ◽  
pp. 315-346
Author(s):  
Domenico Cantone ◽  
Marianna Nicolosi-Asmundo ◽  
Daniele Francesco Santamaria
Keyword(s):  
Semantic Web ◽  
Set Theory ◽  
Description Logic ◽  
Knowledge Bases ◽  
Query Answering ◽  
Conjunctive Query ◽  
Classification Problems ◽  
Previous Version ◽  
Decidable Fragment ◽  
Stratified Set

We present a KE-tableau-based implementation of a reasoner for a decidable fragment of (stratified) set theory expressing the description logic 𝒟ℒ〈4LQSR,×〉(D) (𝒟ℒD4,×, for short). Our application solves the main TBox and ABox reasoning problems for 𝒟ℒD4,×. In particular, it solves the consistency and the classification problems for 𝒟ℒD4,×-knowledge bases represented in set-theoretic terms, and a generalization of the Conjunctive Query Answering problem in which conjunctive queries with variables of three sorts are admitted. The reasoner, which extends and improves a previous version, is implemented in C++. It supports 𝒟ℒD4,×-knowledge bases serialized in the OWL/XML format and it admits also rules expressed in SWRL (Semantic Web Rule Language).

scholarly journals How Fuzzy Is My Fuzzy Description Logic?

2012 ◽  
pp. 82-96 ◽  
Author(s):  
Stefan Borgwardt ◽  
Felix Distel ◽  
Rafael Peñaloza
Keyword(s):  
Description Logic ◽  
Fuzzy Description

scholarly journals Improving the Quality of Linked Data Using Statistical Distributions

2018 ◽  
pp. 1638-1664 ◽  
Author(s):  
Heiko Paulheim ◽  
Christian Bizer
Keyword(s):  
Knowledge Base ◽  
Linked Data ◽  
Relational Databases ◽  
Knowledge Bases ◽  
Structured Data ◽  
Data Sources ◽  
Data Sets ◽  
Statistical Distributions ◽  
The Web

Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Neither of the algorithms uses external knowledge, i.e., they operate only on the data itself. We evaluate the algorithms on the DBpedia and NELL knowledge bases, showing that they are both accurate as well as scalable. Both algorithms have been used for building the DBpedia 3.9 release: With SDType, 3.4 million missing type statements have been added, while using SDValidate, 13,000 erroneous RDF statements have been removed from the knowledge base.

scholarly journals Capability-oriented architectural analysis method based on fuzzy description logic

2016 ◽  
Vol 13 (1) ◽  
pp. 287-308 ◽  
Author(s):  
Zhang Tingting ◽  
Liu Xiaoming ◽  
Wang Zhixue ◽  
Dong Qingchao
Keyword(s):  
Business Strategy ◽  
Domain Knowledge ◽  
Description Logic ◽  
Description Logics ◽  
Model Verification ◽  
Architectural Analysis ◽  
Requirements Modeling ◽  
Strategy Model ◽  
Domain Specific Modeling ◽  
Fuzzy Description

A number of problems may arise from architectural requirements modeling, including alignment of it with business strategy, model integration and handling the uncertain and vague information. The paper introduces a method for modeling architectural requirements in a way of ontology-based and capability-oriented requirements elicitation. The requirements can be modeled within a three-layer framework. The Capability Meta-concept Framework is provided at the top level. The domain experts can capture the domain knowledge within the framework, forming the domain ontology at the second level. The domain concepts can be used for extending the UML to produce a domain-specific modeling language. A fuzzy UML is introduced to model the vague and uncertain features of the capability requirements. An algorithm is provided to transform the fuzzy UML models into the fuzzy Description Logics ontology for model verification. A case study is given to demonstrate the applicability of the method.

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