Agnieszka Ławrynowicz

The Data Mining OPtimization Ontology

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner.

The role of semantics in mining frequent patterns from knowledge bases in description logics with rules

We propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular, we consider the setting of using a language that combines description logics (DLs) with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation of the semantics of the representation formalism to the task of frequent pattern discovery, and for the core of our method, we propose an algorithm that exploits the semantics of the combined knowledge base. We have developed a proof-of-concept data mining implementation of this. Using this we have empirically shown that using the combined knowledge base to perform semantic tests can make data mining faster by pruning useless candidate patterns before their evaluation. We have also shown that the quality of the set of patterns produced may be improved: the patterns are more compact, and there are fewer patterns. We conclude that exploiting the semantics of a chosen representation formalism is key to the design and application of (onto-)relational frequent pattern discovery methods.

Pattern Based Feature Construction in Semantic Data Mining

The authors 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. The authors have developed a tool that implements this approach. Using this the authors 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.

Categorize by: deductive aggregation of semantic web query results

Query answering on a wide and heterogeneous environment such as the Web can return a large number of results that can be hardly manageable by users/agents. The adoption of grouping criteria of the results could be of great help. Up to date, most of the proposed methods for aggregating results on the (Semantic) Web are mainly grounded on syntactic approaches. However, they could not be of significant help, when the values instantiating a grouping criterion are all equal (thus creating a unique group) or are almost all different (thus creating one group for each answer). We propose a novel approach that is able to overcome such drawbacks: given a query in the form of a conjunctive query, grouping is grounded on the exploitation of the semantics of background ontologies during the aggregation of query results. Specifically, we propose a solution where answers are deductively grouped taking into account the subsumption hierarchy of the underlying knowledge base. In this way, the results can be shown and navigated similarly to a faceted search. An experimental evaluation of the proposed method is also reported.

Faster Frequent Pattern Mining from the Semantic Web

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.

Fr-ONT: an algorithm for frequent concept mining with formal ontologies

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 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.

On Reducing Redundancy in Mining Relational Association Rules from the Semantic Web

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.

Frequent pattern discovery from OWL DLP knowledge bases

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.

Towards discovery of frequent patterns in description logics with rules

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.

Query Results Clustering by Extending SPARQL with CLUSTER BY

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.