Reasoning with Vague Concepts in Description Logics

2017 ◽  
Vol 6 (2) ◽  
pp. 43-58
Author(s):  
Mohamed Gasmi ◽  
Mustapha Bourahla
Keyword(s):  
Description Logics ◽  
Formal Languages ◽  
The Third ◽  
Open World ◽  
Tableau Algorithm ◽  
Vague Concept ◽  
Open World Assumption

The open world assumption in ontologies representing knowledge may assign deficient (imprecise) meaning for ontology concepts which are language adjectives referring the meaning of classes of objects (individuals). The interpretation of an imprecise (vague) concept is by three subsets of individuals. The first subset of individuals surely belongs to the vague concept, the second subset of individuals surely doesn't belong the vague concept and the third subset is in the borderline. In this paper, the authors will show that is possible to describe ontology vague concepts using well-defined formal languages. The authors will propose also an extension of the Tableau algorithm for reasoning over vague ontologies.

Approaches to Semantics in Knowledge Management

2009 ◽  
pp. 257-281
Author(s):  
Cristiano Fugazza ◽  
Stefano David ◽  
Anna Montesanto ◽  
Cesare Rocchi
Keyword(s):  
Neural Networks ◽  
Knowledge Management ◽  
Artificial Neural Networks ◽  
Database Management ◽  
Description Logics ◽  
Open World ◽  
Open World Assumption ◽  
Relational Database Management ◽  
Static Nature ◽  
Relational Database Management Systems

There are different approaches to modeling a computational system, each providing different semantics. We present a comparison among different approaches to semantics and we aim at identifying which peculiarities are needed to provide a system with uniquely interpretable semantics. We discuss different approaches, namely, Description Logics, Artificial Neural Networks, and relational database management systems. We identify classification (the process of building a taxonomy) as common trait. However, in this chapter we also argue that classification is not enough to provide a system with a Semantics, which emerges only when relations among classes are established and used among instances. Our contribution also analyses additional features of the formalisms that distinguish the approaches: closed versus. open world assumption, dynamic versus. static nature of knowledge, the management of knowledge, and the learning process.

Using the Semantic Web Rule Language in the Development of Ontology-Driven Applications

2010 ◽  
pp. 525-539 ◽  
Author(s):  
Martin O’Connor ◽  
Mark Musen ◽  
Amar Das
Keyword(s):  
Semantic Web ◽  
Description Logic ◽  
Deductive Reasoning ◽  
Description Logics ◽  
Open World ◽  
Closed World ◽  
Amount Of Knowledge ◽  
Open World Assumption ◽  
Formal Characteristic ◽  
Semantic Web Rule Language

The Semantic Web Rule Language (SWRL) is an expressive OWL-based rule language. SWRL allows users to write Horn-like rules that can be expressed in terms of OWL concepts to provide more powerful deductive reasoning capabilities than OWL alone. Semantically, SWRL is built on the same description logic foundation as OWL and provides similar strong formal guarantees when performing inference. Due to its description logics foundation, rule-based applications developed using SWRL have a number of relatively novel characteristics. For example, SWRL shares OWL’s open world assumption so certain types of rules that assume a closed world may be difficult or impossible to write in SWRL. In addition, all inference in SWRL is monotonic so deductions cannot be updated or retracted. These formal characteristic have a strong influence on the development and use of SWRL rules in ontology-driven applications. In this chapter, we describe the primary features of SWRL and outline how, despite some limitations, SWRL can be used to dramatically increase amount of knowledge that be represented in OWL ontologies.

Approaches to Semantics in Knowledge Management

2011 ◽  
pp. 146-171
Author(s):  
Cristiano Fugazza ◽  
Stefano David ◽  
Anna Montesanto ◽  
Cesare Rocchi
Keyword(s):  
Neural Networks ◽  
Knowledge Management ◽  
Artificial Neural Networks ◽  
Database Management ◽  
Description Logics ◽  
Open World ◽  
Open World Assumption ◽  
Relational Database Management ◽  
Static Nature ◽  
Relational Database Management Systems

There are different approaches to modeling a computational system, each providing different semantics. We present a comparison among different approaches to semantics and we aim at identifying which peculiarities are needed to provide a system with uniquely interpretable semantics. We discuss different approaches, namely, Description Logics, Artificial Neural Networks, and relational database management systems. We identify classification (the process of building a taxonomy) as common trait. However, in this chapter we also argue that classification is not enough to provide a system with a Semantics, which emerges only when relations among classes are established and used among instances. Our contribution also analyses additional features of the formalisms that distinguish the approaches: closed versus. open world assumption, dynamic versus. static nature of knowledge, the management of knowledge, and the learning process.

scholarly journals Temporal Query Answering in DL-Lite with Negation

10.29007/2df8 ◽  
2018 ◽  
Author(s):  
Stefan Borgwardt ◽  
Veronika Thost
Keyword(s):  
Temporal Logic ◽  
Domain Knowledge ◽  
Relational Databases ◽  
Query Language ◽  
Description Logics ◽  
Query Answering ◽  
Open World ◽  
Temporal Query ◽  
Temporal Query Language ◽  
Open World Assumption

Ontology-based query answering augments classical query answering in databases by adopting the open-world assumption and by including domain knowledge provided by an ontology. We investigate temporal query answering w.r.t. ontologies formulated in DL-Lite, a family of description logics that captures the conceptual features of relational databases and was tailored for efficient query answering. We consider a recently proposed temporal query language that combines conjunctive queries with the operators of propositional linear temporal logic (LTL). In particular, we consider negation in the ontology and query language, and study both data and combined complexity of query entailment.

scholarly journals Reasoning on vague ontologies using rough set theory

2021 ◽  
Vol 7 (1) ◽  
pp. 0-0
Keyword(s):  
Incomplete Information ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Approximation Spaces ◽  
Automatic Reasoning ◽  
Fuzzy Concept ◽  
Open World ◽  
Tableau Algorithm ◽  
Open World Assumption

Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. The extended Tableau algorithm by this rough set-based vagueness is intended to answer queries even with the presence of incomplete information.

scholarly journals Closed- and Open-world Reasoning in DL-Lite for Cloud Infrastructure Security

2021 ◽  
Author(s):  
Claudia Cauli ◽  
Magdalena Ortiz ◽  
Nir Piterman
Keyword(s):  
Description Logics ◽  
Conjunctive Query ◽  
Cloud Infrastructure ◽  
First Order ◽  
Open World ◽  
Closed World ◽  
Security Properties ◽  
Open World Assumption ◽  
Core Part ◽  
Natural Description

Infrastructure in the cloud is deployed through configuration files, which specify the resources to be created, their settings, and their connectivity. We aim to model infrastructure before deployment and reason about it so that potential vulnerabilities can be discovered and security best practices enforced. Description logics are a good match for such modeling efforts and allow for a succinct and natural description of cloud infrastructure. Their open-world assumption allows capturing the distributed nature of the cloud, where a newly deployed infrastructure could connect to pre-existing resources not necessarily owned by the same user. However, parts of the infrastructure that are fully known need closed-world reasoning, calling for the usage of expressive formalisms, which increase the computational complexity of reasoning. Here, we suggest an extension of DL-LiteF that is tailored for capturing such cloud infrastructure. Our logic allows combining a core part that is completely defined (closed-world) and interacts with a partially known environment (open-world). We show that this extension preserves the first-order rewritability of DL-LiteF for knowledge-base satisfiability and conjunctive query answering. Security properties combine universal and existential reasoning about infrastructure. Thus, we also consider the problem of conjunctive query satisfiability and show that it can be solved in logarithmic space in data complexity.

scholarly journals Incomplete Information Management Using an Improved Belief Entropy in Dempster-Shafer Evidence Theory

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 993 ◽  
Author(s):  
Bin Yang ◽  
Dingyi Gan ◽  
Yongchuan Tang ◽  
Yan Lei
Keyword(s):  
Data Fusion ◽  
Incomplete Information ◽  
Information Fusion ◽  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Open World ◽  
Belief Entropy ◽  
Open World Assumption ◽  
Deng Entropy

Quantifying uncertainty is a hot topic for uncertain information processing in the framework of evidence theory, but there is limited research on belief entropy in the open world assumption. In this paper, an uncertainty measurement method that is based on Deng entropy, named Open Deng entropy (ODE), is proposed. In the open world assumption, the frame of discernment (FOD) may be incomplete, and ODE can reasonably and effectively quantify uncertain incomplete information. On the basis of Deng entropy, the ODE adopts the mass value of the empty set, the cardinality of FOD, and the natural constant e to construct a new uncertainty factor for modeling the uncertainty in the FOD. Numerical example shows that, in the closed world assumption, ODE can be degenerated to Deng entropy. An ODE-based information fusion method for sensor data fusion is proposed in uncertain environments. By applying it to the sensor data fusion experiment, the rationality and effectiveness of ODE and its application in uncertain information fusion are verified.

scholarly journals Adapted Rules for UML Modelling of Geospatial Information for Model-Driven Implementation as OWL Ontologies

2019 ◽  
Vol 8 (9) ◽  
pp. 365 ◽  
Author(s):  
Jetlund ◽  
Onstein ◽  
Huang
Keyword(s):  
Spatial Information ◽  
Unified Modeling Language ◽  
Transport Systems ◽  
Geospatial Information ◽  
Unified Modeling ◽  
Model Driven ◽  
Open World ◽  
Ontology Language ◽  
Data Files ◽  
Open World Assumption

This study aims to improve the implementation of models of geospatial information in Web Ontology Language (OWL). Large amounts of geospatial information are maintained in Geographic Information Systems (GIS) based on models according to the Unified Modeling Language (UML) and standards from ISO/TC 211 and the Open Geospatial Consortium (OGC). Sharing models and geospatial information in the Semantic Web will increase the usability and value of models and information, as well as enable linking with spatial and non-spatial information from other domains. Methods for conversion from UML to OWL for basic concepts used in models of geospatial information have been studied and evaluated. Primary conversion challenges have been identified with specific attention to whether adapted rules for UML modelling could contribute to improved conversions. Results indicated that restrictions related to abstract classes, unions, compositions and code lists in UML are challenging in the Open World Assumption (OWA) on which OWL is based. Two conversion challenges are addressed by adding more semantics to UML models: global properties and reuse of external concepts. The proposed solution is formalized in a UML profile supported by rules and recommendations and demonstrated with a UML model based on the Intelligent Transport Systems (ITS) standard ISO 14825 Geographic Data Files (GDF). The scope of the resulting ontology will determine to what degree the restrictions shall be maintained in OWL, and different conversion methods are needed for different scopes.

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