Krzysztof Goczyła

The cartographer algorithm for processing and querying description logics ontologies

One of the most popular formalisms of describing knowledge is Description Logics. It has become even more popular when OWL standard has emerged. The paper presents a new reasoning algorithm – the Cartographer Algorithm – that allows for inferring implicit knowledge from a terminology (TBox) and assertions (ABox) of an ontology. The paper describes the way of processing ontologies in terms of binary signatures and an efficient way of querying ontologies containing numerous individuals. In addition, results of experiments comparing the Cartographer Algorithm with other reasoners are presented.

Algebra of Ontology Modules for Semantic Agents

In the environment of Semantic Web the problem of precise manipulation of units of knowledge gains importance. In this paper we discuss a possibility of adoption for this purpose the flexible and powerful apparatus of relational algebra. We discuss similarities and differences between knowledge and data bases and their underlying information-processing models. On the basis of the discussion we draw and investigate a special analogy between these two important methods. We use this analogy to introduce the special algebra of ontological modules which may be used to exchange, align and merge the knowledge among agents.

The knowledge cartography – a new approach to reasoning over description logics ontologies

The paper presents a new method for representation and processing ontological knowledge – Knowledge Cartography. This method allows for inferring implicit knowledge from both: terminological part (TBox) and assertional part (ABox) of a Description Logic ontology. The paper describes basics of the method and gives some theoretical background of the method. Knowledge Cartography stores and processes ontologies in terms of binary signatures, which gives efficient way of querying ontologies containing numerous individuals. Knowledge Cartography has been applied in KaSeA – a knowledge management system that is being developed in course of a European integrated research project called PIPS. Results of efficiency experiments and ideas of further development of the system are presented and discussed.

Terminological and assertional queries in KQL knowledge access language

One of the directions of development of information systems in recent years is the evolution of data-based systems into the knowledge-based systems. As a part of this process there is ongoing work on a whole range of languages for accessing knowledge bases. They can be used in a variety of applications, however their main drawback is the lack of clearly defined algebra representing a theoretical basis for them. For instance, such a basis is the relational algebra for SQL. The paper presents a new language for accessing knowledge bases that is built on a solid, theoretical foundation - the algebra of ontological modules.

Managing Data from Heterogeneous Data Sources Using Knowledge Layer

In the process of data integration using ontologies it is important to manage data from external data sources in the same way as data stored in the Knowledge Base. In previous papers [1], [2] the way of inference from data stored in the Knowledge Base, using Knowledge Cartography idea has been presented. However, this solution requires loading all data to the Knowledge Base. The solution presented in this paper shows how the Knowledge Cartography can be used to infer from data stored in external data sources, without loading them to the Knowledge Base. The presented solution is to enrich each data source with an additional layer that allows managing data using signatures. The paper additionally describes the results of tests comparing times of inference when data are loaded to the Knowledge Base and when data are fetched on demand.

Modularized Knowledge Bases Using Contexts, Conglomerates and a Query Language

The paper presents a novel approach to design and development of a modularized knowledge base. It is assumed that the knowledge base consists of a terminology (axioms) and a world description (assertions), both formulated in a Description Logics (DL) dialect. The approach is oriented towards decomposition of a knowledge base into logical components called contexts and further into semantic components called conglomerates. Both notions were elaborated separately elsewhere. The paper shows how contexts and conglomerates concepts can work in harmony to create a maintainable knowledge base. An architecture of a system that conforms to this approach, which additionally uses a query language called KQL (Knowledge Query Language), is presented. The approach is intended to be used to build a prototypical system that aims at integrating knowledge on cultural heritage coming from digital libraries, including user-defined libraries. The thorough discussion of related work is also given.

Processing and Querying Description Logic Ontologies Using Cartographic Approach

Description Logic (DL) is a formalism for knowledge representation that recently has gained widespread recognition among knowledge engineers. After a brief introduction to DL, the paper presents a DL reasoner developed at Gdansk University of Technology (GUT). The reasoner, called KaSeA, is based on an original idea called Knowledge Cartography. The paper presents basics of Knowledge Cartography, its potentials and limitations, and compares the solution with other DL reasoners.

Serendipitous Recommendations through Ontology-based Contextual Pre-filtering

Context-aware Recommender Systems aim to provide users with better recommendations for their current situation. Although evaluations of recommender systems often focus on accuracy, it is not the only important aspect. Often recommendations are overspecialized, i.e. all of the same kind. To deal with this problem, other properties can be considered, such as serendipity. In this paper, we study how an ontology-based and context-aware pre-filtering technique which can be combined with existing recommendation algorithm performs in ranking tasks. We also investigate the impact of our method on the serendipity of the recommendations. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can improve the accuracy and serendipity.

Theoretical and Architectural Framework for Contextual Modular Knowledge Bases

The paper presents the approach aimed at building modularized knowledge bases in a systematic, context-aware way. The paper focuses on logical modeling of such knowledge bases, including an underlying SIM metamodel. The architecture of a comprehensive set of tools for knowledgebase systems engineering is presented. The tools enable an engineer to design, create and edit a knowledge base schema according to a novel context approach presented elsewhere by the authors. It is explained how a knowledge base built according to SIM (Structured-Interpretation Model) paradigm is processed by a prototypical reasoner Conglo-S, which is a custom version of widely known Pellet reasoner extended with support for modules of ontologies called tarsets (also introduced elsewhere under the name of conglomerates). The user interface of the system is a plug-in to Protege ontology editor that is a standard tool for development of Semantic Web ontologies. Possible applications of the presented framework to development of knowledge bases for culture heritage and scientific information dissemination are also discussed.

A distributed description of facts in a distributed Semantic Web ontology

During the development of semantic Web there emerged a need for creating semantic descriptions of large domains of interest. However, it turned out that the large size of ontologies containing such descriptions led to many negative effects. As a result, the topics of distributing ontologies and ontology modularization gained major importance. The purpose of this paper is to present a novel approach to describe semantics of modules in distributed ontologies. We categorize problems that are encountered while working with ontology modules and describe some of the best known methods of importing data from a source ontology to a target one. Then we describe our contextual framework that generalizes our approach to the contextualization. This framework introduces a hierarchy of contexts accompanied by a hierarchy of context instances. We show how such a structure helps to solve some crucial problems with modularization, like constraining visibility and avoiding inconsistencies or incoherencies.