Vojtěch Svátek

From natural language to formal proof goal : Structured goal formalisation applied to medical guidelines

Invited Talks.- Information and Influence in Social Networks.- Learning, Logic, and Probability: A Unified View.- Knowledge Acquisition.- KARaCAs: Knowledge Acquisition with Repertory Grids and Formal Concept Analysis for Dialog System Construction.- Capturing Quantified Constraints in FOL, Through Interaction with a Relationship Graph.- Assisting Domain Experts to Formulate and Solve Constraint Satisfaction Problems.- Knowledge Acquisition Evaluation Using Simulated Experts.- Stochastic Foundations for the Case-Driven Acquisition of Classification Rules.- From Natural Language to Formal Proof Goal.- Reuse: Revisiting Sisyphus-VT.- Ontology Engineering.- Role Organization Model in Hozo.- Verification and Refactoring of Ontologies with Rules.- Ontology Selection for the Real Semantic Web: How to Cover the Queens Birthday Dinner?.- Ontology Engineering, Scientific Method and the Research Agenda.- Ontology Learning.- Ontology Enrichment Through Automatic Semantic Annotation of On-Line Glossaries.- Discovering Semantic Sibling Groups from Web Documents with XTREEM-SG.- Designing and Evaluating Patterns for Ontology Enrichment from Texts.- Ontology Mapping and Evolution.- Semantic Metrics.- Matching Unstructured Vocabularies Using a Background Ontology.- Distributed Multi-contextual Ontology Evolution - A Step Towards Semantic Autonomy.- An Evaluation Method for Ontology Complexity Analysis in Ontology Evolution.- Semantic Search.- Semantic Search Components: A Blueprint for Effective Query Language Interfaces.- SemSearch: A Search Engine for the Semantic Web.- Rich Personal Semantic Web Clients: Scenario and a Prototype.- User Interfaces.- i dee: An Integrated and Interactive Data Exploration Environment Used for Ontology Design.- Evaluating a Thesaurus Browser for an Audio-visual Archive.- Knowledge Discovery.- Frequent Pattern Discovery from OWL DLP Knowledge Bases.- Engineering and Learning of Adaptation Knowledge in Case-Based Reasoning.- A Methodological View on Knowledge-Intensive Subgroup Discovery.- Iterative Bayesian Network Implementation by Using Annotated Association Rules.- Semantics from Networks and Crowds.- Multilayered Semantic Social Network Modeling by Ontology-Based User Profiles Clustering: Application to Collaborative Filtering.- Towards Knowledge Management Based on Harnessing Collective Intelligence on the Web.- A Formal Approach to Qualitative Reasoning on Topological Properties of Networks.- Applications.- Towards a Knowledge Ecosystem.- A Tool for Management and Reuse of Software Design Knowledge.- The ODESeW Platform as a Tool for Managing EU Projects: The Knowledge Web Case Study.

Discovery of Lexical Entries for Non-taxonomic Relations in Ontology Learning

Ontology learning from texts has recently been proposed as a new technology helping ontology designers in the modelling process. Discovery of non–taxonomic relations is understood as the least tackled problem therein. We propose a technique for extraction of lexical entries that may give cue in assigning semantic labels to otherwise ‘anonymous’ relations. The technique has been implemented as extension to the existing Text-to-Onto tool, and tested on a collection of texts describing worldwide geographic locations from a tour–planning viewpoint.

MultiFarm: A benchmark for multilingual ontology matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages-Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish-we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism.

Step-by-step mark-up of medical guideline documents

The quality of document-centric formalisation of medical guidelines can be improved using a decomposition of the whole process into several explicit steps. We present a methodology and a software tool supporting the step-by-step formalisation process. The knowledge elements can be marked up in the text with increasing level of detail, rearranged into an XML knowledge base and exported into the operational representation. Semi-automated transitions can be specified by means of rules. The approach has been tested in a hypertension application.

Analysing Ontological Structures through Name Pattern Tracking

Concept naming over the taxonomic structure is a useful indicator of the quality of design as well as source of information exploitable for various tasks such as ontology refactoring and mapping. We analysed collections of OWL ontologies with the aim of determining the frequency of several combined name&graph patterns potentially indicating underlying semantic structures. Such structures range from simple set-theoretic subsumption to more complex constructions such as parallel taxonomies of different entity types. The final goal is to help refactor legacy ontologies as well as to ease automatic alignment among different models. The results show that in most ontologies there is a significant number of occurrences of such patterns. Moreover, their detection even using very simple methods has precision sufficient for a semi-automated analysis scenario.

Roadmapping and Navigating in the Ontology Visualization Landscape

Proper visualization is essential for ontology development, sharing and usage; various use cases however pose specific requirements on visualization features. We analyzed several visualization tools from the perspective of use case categories as well as low-level functional features and OWL expressiveness. A rule-based recommender was subsequently developed to help the user choose a suitable visualizer. Both the analysis results and the recommender were evaluated via a questionnaire.

Ontology-Enhanced association mining

The roles of ontologies in KDD are potentially manifold. We track them through different phases of the KDD process, from data understanding through task setting to mining result interpretation and sharing over the semantic web. The underlying KDD paradigm is association mining tailored to our 4ft-Miner tool. Experience from two different application domains-medicine and sociology-is presented throughout the paper. Envisaged software support for prior knowledge exploitation via customisation of an existing user-oriented KDD tool is also discussed.

Pattern-based ontology transformation service exploiting OPPL and OWL-API

Exploitation of OWL ontologies is often difficult due to their modelling style even if the underlying conceptualisation is adequate. We developed a generic framework and collection of services that allow to define and execute ontology transformation (in particular) with respect to modelling style. The definition of transformation is guided by transformation patterns spanning between mutually corresponding patterns in the source and target ontology, the detection of an instance of one leading to construction of an instance of the other. The execution of axiom-level transformations relies on the functionality of the OPPL processor, while entity-level transformations, including sophisticated handling of naming and treatment of annotations, are carried out directly through the OWL API. A scenario of applying the transformation in the specific context of ontology matching is also presented.

Dataset Summary Visualization with LODSight

We present a web-based tool that shows a summary of an RDF dataset as a visualization of a graph formed from classes, datatypes and predicates used in the dataset. The visualization should allow to quickly and easily find out what kind of data the dataset contains and its structure. It also shows how vocabularies are used in the dataset.

The Ex Project: Web Information Extraction Using Extraction Ontologies

Extraction ontologies represent a novel paradigm in web information extraction (as one of ‘deductive’ species of web mining) allowing to swiftly proceed from initial domain modelling to running a functional prototype, without the necessity of collecting and labelling large amounts of training examples. Bottlenecks in this approach are however the tedium of developing an extraction ontology adequately covering the semantic scope of web data to be processed and the difficulty of combining the ontology-based approach with inductive or wrapper-based approaches. We report on an ongoing project aiming at developing a web information extraction tool based on richly-structured extraction ontologies and with additional possibility of (1) semi-automatically constructing these from third-party domain ontologies, (2) absorbing the results of inductive learning for subtasks where pre-labelled data abound, and (3) actively exploiting formatting regularities in the wrapper style.