Elsevier

Knowledge-Based Systems

Volume 20, Issue 1, February 2007, Pages 98-109
Knowledge-Based Systems

Dynamic evolutions based on ontologies

https://doi.org/10.1016/j.knosys.2006.04.017Get rights and content

Abstract

With the continuous changes in application requirements of the enterprises, Web resources must be updated, so do the underlying ontologies that are associated with the Web resources. In the situation, it is very challenging for ontological engineers to specify the changes of ontologies, keep their consistencies and achieve semantic query of Web resources based on the evolving ontologies. We propose a construct called Prioritized Knowledge Base (PKB) based on SHOQ(D) description logic, and discuss some properties of PKB. PKB can be used for describing the evolutions and updates of ontologies with conflicting information. Furthermore, we develop some algorithms for checking conflict rules and performing semantic query based on PKB.

Introduction

The Semantic Web, the next generation of the Web, augments the current Web by giving information a well defined meanings, better enabling computers and people to work in cooperation [1], [2]. Meanwhile, the Semantic Web is a “living organism”, which combines autonomously heterogeneous, highly distributed and evolving data sources/knowledge repositories.

Ontologies play a key role in the Semantic Web by providing a source of shared and precisely defined terms that can be used in description of Web resources. With rising importance of knowledge sharing and interchange, plenty of industrial and academic applications have adopted ontologies as their conceptual backbone. Evolutions, changes and updates of ontologies become important because continuous changes to applications requirements may be fulfilled only by changing the underlying ontologies [3], [4]. This situation is especially true for the Semantic Web applications. Oberle et al. [5] specified the Semantic Web’s dynamic aspects such as evolution, versioning, transactions, which are often neglected by the Semantic Web community. When ontology-based Web application requirements change and new knowledge is added into knowledge bases of the original ontologies, there maybe exist information conflicts in these ontologies. So these ontologies will be modified with respect to these changes. However, these modifications in some parts of ontologies may generate subtle inconsistencies in the remaindering parts of ontologies [6]. Therefore, the dynamic characters of the Semantic Web require (declarative) languages and mechanisms for specifying its changes, updates, evolutions and maintenance. In this paper, we will work towards this goal.

In order to describe ontologies, we need Web ontology languages such as DAML+OIL [7], OIL [8] and OWL [9]. Each of them provides some basic modeling primitives such as classes and roles, and is able to be mapped onto a corresponding Description Logic language (DL). Description logic language has well founded, logic-based semantics and associated inference procedures. For example, as far as the DL SHOIQ(D) is concerned, it corresponds to Web ontology language OWL DL. DL SHIQ is the DL corresponding to OIL, but the mapping is incomplete with respect to concrete data types and named individuals. Hence Horrocks [10] presents a new DL called SHOQ(D) that overcomes the two deficiencies. In this paper, we concentrate on DL SHOQ(D), which provides the supports for data type (D) and named individuals (O), but it does not support inverse roles.

In order to cope with consistencies of evolving knowledge base based on ontologies with conflicting information, we propose the construct called Prioritized Knowledge Base (PKB) for specifying evolutions and updates of these ontologies. A PKB is knowledge base in which the knowledge rules are assigned different priorities when they conflict each other. Furthermore, we propose a corresponding algorithm for automatically checking the rules conflicting with each other. Meanwhile, we discussed some important properties about PKB such as p-satisfiability, monotonicity, decidability. Then we propose the construct called Induced Knowledge Base (IKB), which is derived from PKB. Through the IKB corresponding to the PKB, we can decide whether a PKB is consistent. If the PKB is not consistent, we also can find where conflicts of the ontological rules occur through its IKB. We argue that PKB can specify the Semantic Web’s dynamic aspects such as evolutions and updates of ontologies that are perhaps from multiple heterogeneous information sources. A semantic query based on PKB can be obtained by means of checking the p-satisfiability of concepts and roles contained in the query.

The paper is organized as follows: Section 2 gives an introduction to SHOQ(D) description logic and the definition of knowledge base based on description logic. In Section 3, we propose the definition of Prioritized Knowledge base (PKB), and discuss its some properties such as p-satisfiability, decidability and nonmonotonicity. In Section 4, we concentrate on the problem of keeping consistency of PKB for specifying the evolutions of ontologies based on PKB. Induced Knowledge Base (IKB) of a PKB is proposed for consistency checking. In Section 5, we discuss how to automatically check inconsistent rules, and an algorithm is proposed for addressing the problem in PKB. Section 6 considers resource discovery and semantic queries based on PKB, and further gives an algorithm to obtain answering results of a query. In Section 7, we argue that this approach also can be extended to evolutions of distributed ontologies by distributed description logics. Sections 8 Related work, 9 Conclusion are related work and conclusion, respectively.

Section snippets

SHOQ(D) description logic

The syntax and semantics of DL SHOQ(D) described in [10] are summarized in this section. It is assumed that there is a set of data types D and a set of dD⊆ΔD is associated with each d  D, where ΔD is the domain of all data types. Let C denote the set of concept names, R denote the disjoint union of abstract role names RA and concrete role names RD. A Tbox T is a finite set of terminological axioms of the form C  D, where C and D are SHOQ(D) concept expressions. A role box R is a finite set of

Prioritized knowledge base

We propose an approach to address the inconsistencies of evolution versions of a knowledge base and specify its dynamic evolutions and updates with respect to the changes to business requirements. Firstly, we give the definition of Prioritized Knowledge Base (PKB).

Definition 2

A prioritized knowledge base PKB = (T, R, N, <), where

  • T is a terminological box,

  • R is a role box,

  • A naming function N: (T  R)  Names, maps every axiom in T and R to a distinct name, where Names = {N1, N2,   , Ni,   }, where i  N. And

  • A strict partial

Consistency problem based on PKB

Most of the work made so far in the field of description logic has focus on representing static knowledge, where ontology knowledge does not evolve with time, such as the work of [13]. We argue that ontologies are continually evolving and growing, which is especially true for Semantic Web applications. With the dynamic changes to business requirements, ontologies that may be heterogeneous must be continuously changed for fulfilling theses requirements changes. When changes take place in

Preference settings in PKB

In previous sections, we discussed the problem of dynamically keeping consistency of PKB. Through IKB of PKB, we can iteratively obtain a consistent evolution version of PKB. But there is still a problem that must be addressed – how can we automatically check conflicting rules in an inconsistent PKB? In order to enable intelligent ontology evolution and reduce efforts of maintenance of dynamically evolving ontologies, it is very important for knowledge based systems to automatically check those

Resource discovery based on evolutions of PKB

Because of the evolution and updates of knowledge base with time, semantic queries performed in different stages would get different results. This embodies the dynamic characteristics of semantic queries based on evolution of PKB. In our opinion, resource discovery is obtained by performing semantic queries based on a consistent PKB.

Definition 10

Let V be a set of variables, Inst be the set of individuals in PKB. Let I be an interpretation of PKB. A semantic query Q = QC  QR, where QC is a conjunction of C(x), Q

Brief discussions about evolutions of distributed ontologies

It now seems clear that Semantic Web will not be realized by agreeing on a single global ontology, but rather by weaving together a large collection of partial ontologies, which are distributed across the Web [15]. In practice, single ontology approach providing a shared vocabulary for the specification of Web semantics, is rather difficult in integrated ontologies of multiple and heterogeneous information sources [16]. In the situation, distributed description logics (DDL) [17] can better

Related work

Currently, there are a number of works on evolutions and updates of knowledge base are made. In the logic programming community, logic programming has been regarded as a good logic based formulation for problem solving, knowledge representation and reasoning, and reasoning about changes. Alferes [18], [19] proposed Dynamic Logic Programming (DLP), which is one of earliest efforts that a knowledge base is represented as a logic program. Zhang [20], [21] proposed a Prioritized Logic Programs

Conclusion

The Semantic Web combines autonomously heterogeneous, highly distributed and evolving data sources/knowledge repositories. Evolutions and updates of distributed ontologies are an important aspect, because business dynamics and changes often give rise to continuous changes to applications requirements, which may be fulfilled only by changing the underlying ontologies. This situation is especially true for the Semantic Web applications. In this paper, we propose the definition of prioritized

Acknowledgements

This research is partially supported by the National Grand Fundamental Research Program of China under Grant No. TG1999035805, 2002CB312005, the Chinese National “863” High-Tech Program under Grant No.2001AA113010.

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