Agnieszka Ławrynowicz

Poznan University of Technology · Institute of Computing Science

Recent years have shown that deep learning models pre-trained on large text corpora using the language model objective can help solve various tasks requiring natural language understanding. However, many commonsense concepts are underrepresented in online resources because they are too obvious for most humans. To solve this problem, we propose the...

We present a method for constructing synthetic datasets of Competency Questions translated into SPARQL-OWL queries. This method is used to generate BigCQ, the largest set of CQ patterns and SPARQL-OWL templates that can provide translation examples to automate assessing the completeness and correctness of ontologies.

Food Computing is currently a fast-growing field of research. Natural language processing (NLP) is also increasingly essential in this field, especially for recognising food entities. However, there are still only a few well-defined tasks that serve as benchmarks for solutions in this area. We introduce a new dataset -- called \textit{TASTEset} --...

Glossary of Terms extraction from textual requirements is an important step in ontology engineering methodologies. Although initially it was intended to be performed manually, last years have shown that some degree of automatization is possible. Based on these promising approaches, we introduce a novel, human interpretable, rule-based method named...

Competency Questions (CQs) are used in many ontology engineering methodologies to collect requirements and track the completeness and correctness of an ontology being constructed. Although they are frequently suggested by ontology engineering methodologies, the publicly available datasets of CQs and their formalizations in ontology query languages...

Semi-structured text generation is a non-trivial problem. Although last years have brought lots of improvements in natural language generation , thanks to the development of neural models trained on large scale datasets, these approaches still struggle with producing structured, context-and commonsense-aware texts. Moreover, it is not clear how to...

Anti-social online behaviour, such as harassment or vulgarity, leading to conflicts aimed at destroying any merit of the discussions, is a serious problem for the Internet community. Recognising the characteristics of conflict discussions and modelling their trajectory might help to predict and prevent derailing. My PhD thesis focuses on using emot...

Unjustified anti-social behaviour in Internet discussions, such as vulgarisms and insults, is tantamount to the outbreak of an online conflict that destroys the merits of the discussion. Recognising the characteristics of conflict discussions and modelling their dynamics can help to predict and prevent derailing. We propose to use emotion labels as...

This data article reports on a new set of 234 competency questions for ontology development and their formalisation into a set of 131 SPARQL-OWL queries. This is the largest set of competency questions with their linked queries to date, covering several ontologies of different type in different subject domains developed by different groups of quest...

Competency Questions (CQs) are natural language questions outlining and constraining the scope of knowledge represented in an ontology. Despite that CQs are a part of several ontology engineering methodologies, the actual publication of CQs for the available ontologies is very limited and even scarcer is the publication of their respective formaliz...

Ontology authoring is a complex process, where commonly the automated reasoner is invoked for verification of newly introduced changes, therewith amounting to a time-consuming test-last approach. Test-Driven Development (TDD) for ontology authoring is a recent test-first approach that aims to reduce authoring time and increase authoring efficiency....

Competency Questions (CQs) are questions expressed in natural language aimed to indicate ontology’s scope, which are later formalized according to the language used to represent the ontology. One intermediate step that facilitates formalizing CQs, proposed in ontology engineering methodologies, is to extract so-called Glossary of Terms from them, w...

Ontology authoring is a complex process, where commonly the automated reasoner is invoked for verification of newly introduced changes, therewith amounting to a time-consuming test-last approach. Test-Driven Development (TDD) for ontology authoring is a recent {\em test-first} approach that aims to reduce authoring time and increase authoring effic...

Competency Questions (CQs) are natural language questions outlining and constraining the scope of knowledge represented by an ontology. Despite that CQs are a part of several ontology engineering methodologies, we have observed that the actual publication of CQs for the available ontologies is very limited and even scarcer is the publication of the...

The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments. It can be easily extended and specialized and it is also mapped to other more domai...

This volume represents the proceedings of the Main Conference, with Posters and Demonstrations track, of the 14th International Conference on ICT in Education, Research, and Industrial Applications, held in Kyiv, Ukraine, in May 2018. It comprises 47 contributed papers that were carefully peer-reviewed and selected from 119 sub-missions. The volume...

This chapter describes an ontology design pattern for modeling algorithms, their implementations and executions. This pattern is derived from the research results on data mining/machine learning ontologies, but is more generic. We argue that the proposed pattern will foster the development of standards in order to achieve a high level of interopera...

Ontology authoring is a complex task where modellers rely heavily on the automated reasoner for verification of changes, using effectively a time-consuming test-last approach. Test-first with Test-Driven Development aims to speed up such processes, but tools to date covered only a subset of possible OWL 2 DL axioms and provide limited feedback. We...

This paper describes an ontology design pattern for modeling algorithms, their implementations and executions. This pattern is derived from the research results on data mining/machine learning ontologies, but is more generic. We argue that the proposed pattern will foster the development of standards in order to achieve a high level of interoperabi...

Emerging ontology authoring methods to add knowledge to an ontology focus on ameliorating the validation bottleneck. The verification of the newly added axiom is still one of trying and seeing what the reasoner says, because a systematic testbed for ontology authoring is missing. We sought to address this by introducing the approach of test-driven...

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 ont...

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...

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 ont...

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 learning, support vector machines, and graphical models in more detail.We also present example machine learning applications...

We present a prototype system, named ASPARAGUS, that performs aggregation of SPARQL query results on a semantic baseline, that is by an exploitation of the background ontology expressing the semantics of the returned results. The system implements the recent research results on semantic grouping, and semantic clustering. In the former case, results...

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. Ou...

The methods proposed for aggregating results of structured queries are typically grounded on syntactic approaches. This may be inconvenient for an exploratory data retrieval, with often overwhelming number of the returned answers, requiring their further analysis and categorization. For example, if the values instantiating a grouping criterion are...

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 a...

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 with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation o...

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...

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...

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 DL{\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...

Proceedings of the First ESWC Workshop on Inductive Reasoning and Machine Learning on the Semantic Web online: http://ceur-ws.org/Vol-474 ISSN: 1613-0073 Central EURope workshop proceedings

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...

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 patter...

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 knowled...

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. I...

The conceptual architecture of autonomic communications requires a knowledge layer to facilitate effective, transparent and high level self-management capabilities. This pervasive knowledge plane can utilise the behaviour of autonomic communication regimes to monitor and intervene at many differing levels of network granularity. This paper discusse...

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++\mathcal{EL}^{++} description logic language. We also report on the implementation of our method. As description logic provid...

The conceptual architecture of autonomic communications requires a knowledge layer to offer effective, transparent and high level self-management capabilities. This knowledge plane can utilise the behaviour of autonomic communication regimes to monitor and intervene at many differing levels of network granularity. This paper introduces autonomic co...

We present RMonto, an ontological extension to RapidMiner, that provides possibility of machine learning with formal ontologies. RMonto is an easily extendable framework, currently providing support for unsupervised clustering with kernel methods and (frequent) pattern mining in knowledge bases. One important feature of RMonto is that it enables wo...