Robert Bembenik

Intelligent Tools for Building a Scientific Information Platform

This book is a selection of results obtained within two years of research per- formed under SYNAT - a nation-wide scientific project aiming at creating an infrastructure for scientific content storage and sharing for academia, education and open knowledge society in Poland. The selection refers to the research in artificial intelligence, knowledge discovery and data mining, information retrieval and natural language processing, addressing the problems of implementing intelligent tools for building a scientific information platform.This book is a continuation and extension of the ideas presented in Intelligent Tools for Building a Scientific Information Platform published as volume 390 in the same series in 2012. It is based on the SYNAT 2012 Workshop held in Warsaw. The papers included in this volume present an overview and insight into information retrieval, repository systems, text processing, ontology-based systems, text mining, multimedia data processing and advanced software engineering.

FARICS: a method of mining spatial association rules and collocations using clustering and Delaunay diagrams

The paper presents problems pertaining to spatial data mining. Based on the existing solutions a new method of knowledge extraction in the form of spatial association rules and collocations has been worked out and is proposed herein. Delaunay diagram is used for determining neighborhoods. Based on the neighborhood notion, spatial association rules and collocations are defined. A novel algorithm for finding spatial rules and collocations has been presented. The approach allows eliminating the parameters defining neighborhood of objects, thus avoiding multiple “test and trial” repetitions of the process of mining for various parameter values. The presented method has been implemented and tested. The results of the experiments have been discussed.

Intelligent Tools for Building a Scientific Information Platform: From Research to Implementation

This book is a selection of results obtained within three years of research performed under SYNATa nation-wide scientific project aiming at creating an infrastructure for scientific content storage and sharing for academia, education and open knowledge society in Poland. The book is intended to be the last of the series related to the SYNAT project. The previous books, titled Intelligent Tools for Building a Scientific Information Platform and Intelligent Tools for Building a Scientific Information Platform: Advanced Architectures and Solutions, were published as volumes 390 and 467 in Springers Studies in Computational Intelligence. Its contents is based on the SYNAT 2013 Workshop held in Warsaw. The papers included in this volume present an overview and insight into information retrieval, repository systems, text processing, ontology-based systems, text mining, multimedia data processing and advanced software engineering, addressing the problems of implementing intelligent tools for building a scientific information platform.

SYNAT System Ontology: Design Patterns Applied to Modeling of Scientific Community, Preliminary Model Evaluation

The paper presents the extended version of the SYNAT system ontology, used design patterns, modeling choices and preliminary evaluation of the model. SYNAT system ontology was designed to define semantic scope of the SYNAT platform. It covers concepts related to scientific community and its activities i.e.: people in science and their activities, scientific and sciencerelated documents, academic and non-academic organizations, scientific events and data resources, geographic notions necessary to characterize facts about science as well as classification of scientific topics. In its current version SYNAT system ontology counts 472 classes and 296 properties, its consistency was verified using Pellet and HermiT reasoners.

Methods and Tools for Ontology Building, Learning and Integration – Application in the SYNAT Project

One of the main goals of the SYNAT project is to equip scientific community with a knowledge-based infrastructure providing fast access to relevant scientific information. We have started building an experimental platform where different kinds of stored knowledge will be modeled with the use of ontologies, e.g. reference/system ontology, domain ontologies and auxiliary knowledge including lexical language ontology layers. In our platform we use system ontology defining “system domain” (a kind of meta knowledge) for the scientific community, covering concepts and activities related to the scientific life and domain ontologies dedicated to specific areas of science. Moreover the platform is supposed to include a wide range of tools for building and maintenance of ontologies throughout their life cycle as well as interoperation among the different introduced ontologies.

Methods for mining co–location patterns with extended spatial objects

Abstract The paper discusses various approaches to mining co-location patterns with extended spatial objects. We focus on the properties of transaction-free approaches EXCOM and DEOSP, and discuss the differences between the method using a buffer and that employing clustering and triangulation. These theoretical differences between the two methods are verified experimentally. In the performed tests three different implementations of EXCOMare compared with DEOSP, highlighting the advantages and downsides of both approaches.

Associations between Texts and Ontology

Intelligent automatic text processing methods require linking between texts (written in natural languages) and concepts representing semantics of particular words and phrases. Concepts are supposed to be independent from languages and can be expressed as ontologies. The mapping of concepts to a specific language may be expressed as a linguistic layer of a given ontology. The paper presents a model of lexical layer that establishes relations between terms (words or phrases) and entities of a given ontology. Moreover, the layer contains representations not only of terms but also their contexts of usage. It also provides associations with commonly used lexical knowledge resources, such as WordNet, Wikipedia and DBPedia.

Mining Spatial Association Rules

This paper focuses on finding spatial association rules. In the first part of the article the specifics of spatial databases are discussed and the existing methods for finding spatial association rules are reviewed. Also different classes of spatial relations used to express the relationships between objects in spatial databases are presented. Farther a method for mining spatial association rules has been proposed and special data structure, called T-tree, for storing frequent and potentially frequent itemsets has been introduced. Spatial objects are represented here as points. Non-spatial items contain information considering spatial elements. Discovered associations contain information including both spatial and non-spatial elements.

Towards Automatic Argument Extraction and Visualization in a Deliberative Model of Online Consultations for Local Governments

Automatic extraction and visualization of arguments used in a long online discussion, especially if the discussion involves a large number of participants and spreads over several days, can be helpful to the people involved. The main benefit is that they do not have to read all entries to get to know the main topics being discussed and can refer to existing arguments instead of introducing them anew. Such discussions take place, i.e., on a deliberative platform being developed under the ‘In Dialogue’ project. In this paper we propose a framework allowing for automatic extraction of arguments from deliberations and visualization. The framework assumes extraction of arguments and argument proposals, sentiment analysis to predict whether argument is negative or positive, classification to decide how the arguments are related and the use of ontology for visualization.

Automatic Translation of Multi-word Labels

Application of semantic resources often requires linking phrases expressed in a natural language to formally defined notions. In case of ontologies lexical layers may be used for that purpose. In the paper we propose an automatic machine translation method for translating multi-word labels from lexical layers of domain ontologies. In the method we take advantage of Wikipedia and dictionaries services available on the Internet in order to provide translations of thematic texts from a given area of interest. Experimental evaluation shows usefulness of the proposed method in translating specialized thematic dictionaries.