Agnieszka Ławrynowicz | Poznan University of Technology (original) (raw)

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Papers by Agnieszka Ławrynowicz

We propose a new method for knowledge acquisition and ontology refinement for the Semantic Web ut... more We propose a new method for knowledge acquisition and ontology refinement for the Semantic Web utilizing Linked Data available through remote SPARQL endpoints. This method is based on combination of the attribute exploration algorithm from formal concept analysis and the active learning approach from machine learning.

Perspectives On Ontology Learning, 2014

In this chapter we provide an overview on some of the main issues in machine learning. We discuss... more In this chapter we provide an overview on some of the main issues in machine learning. We discuss machine learning both from a formal and a statistical perspective. We describe some aspects of machine learning such as concept learn- ing, support vector machines, and graphical models in more detail. We also present example machine learning applications to the Semantic Web.

We propose a new method for mining sets of patterns for classification, where patterns are repres... more We propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. We have developed a tool that implements this approach. Using this we have conducted an experimental evaluation including comparison of our method to state-of- the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

The paper proposes the framework for clustering results of queries submitted over Semantic Web da... more The paper proposes the framework for clustering results of queries submitted over Semantic Web data. As an instantiation of a framework, an approach is proposed for clustering the results of conjunctive queries submitted to knowledge bases represented in Web Ontology Language (OWL). As components of the approach, a method for the construction of a set of semantic features, as well as a novel method for feature selection are presented. The proposed approach is implemented, and its feasibility is tested empirically.

The paper introduces a task of frequent concept mining: mining frequent patterns of the form of (... more The paper introduces a task of frequent concept mining: mining frequent patterns of the form of (complex) concepts expressed in description logic. We devise an algorithm for mining frequent patterns expressed in standard \(\mathcal{EL}^{++}\) description logic language. We also report on the implementation of our method. As description logic provides the theorethical foundation for standard Web ontology language OWL, and description logic concepts correspond to OWL classes, we envisage the possible use of our proposed method on a broad range of data and knowledge intensive applications that exploit formal ontologies.

The task of dynamic clustering of the search results proved to be useful in the Web context, wher... more The task of dynamic clustering of the search results proved to be useful in the Web context, where the user often does not know the granularity of the search results in advance. The goal of this paper is to provide a declarative way for invoking dynamic clustering of the results of queries submitted over Semantic Web data. To achieve this goal the paper proposes an approach that extends SPARQL by clustering abilities. The approach introduces a new statement, CLUSTER BY, into the SPARQL grammar and proposes semantics for such extension.

This paper contains the experimental investigation of an approach, named SEMINTEC, to frequent pa... more This paper contains the experimental investigation of an approach, named SEMINTEC, to frequent pattern mining in combined knowledge bases represented in description logic with rules (so-called \({\mathcal DL}\) -safe ones). Frequent patterns in this approach are the conjunctive queries to a combined knowledge base. In this paper, first, we prove that the approach introduced in our previous work for the DLP fragment of description logic family of languages, is also valid for more expressive languages. Next, we present the experimental results under different settings of the approach, and on knowledge bases of different sizes and complexities.

The Semantic Web technology should enable publishing of numerous resources of scientific and othe... more The Semantic Web technology should enable publishing of numerous resources of scientific and other, highly formalized data on the Web. The application of mining these huge, networked Web repositories seems interesting and challenging. In this paper we present and discuss an inductive reasoning procedure for mining frequent patterns from the knowledge bases represented in OWL DLP. OWL DLP, also known as Description Logic Programs, lies at the intersection of the expressivity of OWL DL and Logic Programming. Our method is based on a special trie data structure inspired by similar, efficient structures used in classical and relational data mining settings. Conjunctive queries to OWL DLP knowledge bases are the language of frequent patterns.

This paper follows the research direction that has received a growing interest recently, namely a... more This paper follows the research direction that has received a growing interest recently, namely application of knowledge discovery methods to complex data representations. Among others, there have been methods proposed for learning in expressive, hybrid languages, combining relational component with terminological (description logics) component. In this paper we present a novel approach to frequent pattern discovery over the knowledge base represented in such a language, the combination of the basic subset of description logics with DL-safe rules, that can be seen as a subset of Semantic Web Rule Language. Frequent patterns in our approach are represented as conjunctive DL-safe queries over the hybrid knowledge base. We present also an illustrative example of our method based on the financial dataset.

In this paper we propose a method for frequent pattern discovery from the knowledge bases represe... more In this paper we propose a method for frequent pattern discovery from the knowledge bases represented in OWL DLP. OWL DLP, known also as Description Logic Programs, is the intersection of the expressivity of OWL DL and Logic Programming. Our method is based on a special form of a trie data structure. A similar structure was used for frequent pattern discovery in classical and relational data mining settings giving significant gain in efficiency. Our approach is illustrated on the example ontology.

The predictive accuracy of classifiers is determined among others by the quality of data. This im... more The predictive accuracy of classifiers is determined among others by the quality of data. This important property of data is strongly affected by such factors as the number of erroneous or missing attributes present in the dataset. In this paper we show how those factors can be handled by introducing the levels of abstraction in data definition. Our approach is especially valuable in cases where giving the precise value of an attribute is impossible for a number of reasons as for example lack of time or knowledge. Furthermore, we show that increasing the level of precision for an attribute significantly increase predictive accuracy, especially when it is done for the attribute with high information gain.

In this paper we discuss how to reduce redundancy in the process and in the results of mining the... more In this paper we discuss how to reduce redundancy in the process and in the results of mining the Semantic Web data. In particular, we argue that the availability of the domain knowledge should not be disregarded during data mining process. As the case study we show how to integrate the semantic redundancy reduction techniques into our approach to mining association rules from the hybrid knowledge bases represented in OWL with rules.

We describe the Data Mining OPtimization Ontology (DMOP), which was developed to support informed... more We describe the Data Mining OPtimization Ontology (DMOP), which was developed to support informed decision-making at various choice points of the knowledge discovery (KD) process. It can be used as a reference by data miners, but its primary purpose is to automate algorithm and model selection through semantic meta-mining, i.e., ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. DMOP contains in-depth descriptions of DM tasks (e.g., learning, feature selection), data, algorithms, hypotheses (mined models or patterns), and workflows. Its development raised a number of non-trivial modeling problems, the solution to which demanded maximal exploitation of OWL 2 representational potential. We discuss a number of modeling issues encountered and the choices made that led to version 5.3 of the DMOP ontology.

Perspectives On Ontology Learning, 2014