Jakub Koperwas

Intelligent information processing for building university knowledge base

There are many ready-to-use software solutions for building institutional scientific information platforms, most of which have functionality well suited to repository needs. However, there have already been discussions about various problems with institutional digital libraries. As a remedy, an approach that is researcher-centric (rather than document-centric) has been proposed recently in some systems. This paper is devoted to research aimed at tools for building knowledge bases for university research. We focus on the AI methods that have been elaborated and applied practically within our platform for building such knowledge bases. In particular we present a novel approach to data acquisition and the semantic enrichment of the acquired data. In addition, we present the algorithms applied in the real life system for experts profiling and retrieval.

Development of a University Knowledge Base

This paper summarizes deployment and a one-year-exploitation of repository platform for knowledge and scientific resources. The platform was developed under SYNAT project and designed for the purpose of Warsaw University of Technology. First we present functionalities of the platform with accordance to the SYNAT objectives and the requirements of Warsaw University of Technology. Those functionalities are then compared to other well established systems like dLibra, Fedora Commons Repository and DSpace. The architecture of the platform and the aspect of integration with other systems are also discussed. Finally we discuss how the platform can be reused for deployment for the purposes of any other university with respect to the specific resources it produces and archives.

Clustering of Leaf-Labelled Trees

This paper introduces novel methodology for the clustering of data represented as leaf-labelled trees on the same leaf-set. We define an abstract term - the representative tree, which can be represented with a variety of trees, depending on applications. The quality of tree-clustering is based on Information Gain, which measures the increase of information contained by representative trees of the resulting clusters compared to a single representative tree of the whole dataset. Finally, we propose the k-best algorithm the objective function of which is to maximize the information gain. We show how it can be constructed for two different representative trees, well- known in phylogenetic analysis. Developed algorithms yield very promissing results.

Tree edit distance for leaf-labelled trees on free leafset and its comparison with frequent subsplit dissimilarity and popular distance measures

BackgroundThis paper is devoted to distance measures for leaf-labelled trees on free leafset. A leaf-labelled tree is a data structure which is a special type of a tree where only leaves (terminal) nodes are labelled. This data structure is used in bioinformatics for modelling of evolution history of genes and species and also in linguistics for modelling of languages evolution history. Many domain specific problems occur and need to be solved with help of tree postprocessing techniques such as distance measures.ResultsHere we introduce the tree edit distance designed for leaf labelled trees on free leafset, which occurs to be a metric. It is presented together with tree edit consensus tree notion. We provide statistical evaluation of provided measure with respect to R-F, MAST and frequent subsplit based dissimilarity measures as the reference measures.ConclusionsThe tree edit distance was proven to be a metric and has the advantage of using different costs for contraction and pruning, therefore their properties can be tuned depending on the needs of the user. Two of the presented methods carry the most interesting properties. E(3,1) is very discriminative (having a wide range of values) and has a very regular distance distribution which is similar to a normal distribution in its shape and is good both for similar and non-similar trees. NFC(2,1) on the other hand is proportional or nearly proportional to the number of mutation operations used, irrespective of their type.

Clustering of Leaf-Labelled Trees on Free Leafset

This paper focuses on the clustering of leaf-labelled trees on free leafset. It extends the previously proposed algorithms, designed for trees on the same leafset. The term z-equality is proposed and all the necessary consensus and distance notions are redefined with respect to z-equality. The clustering algorithms that focus on maximizing the quality measure for two representative trees are described, together with the measure itself. Finally, the promising results of experiments on tandem duplication trees are presented.

AI Platform for Building University Research Knowledge Base

This paper is devoted to the 3-years research performed at Warsaw University of Technology, aimed at building of an advanced software for university research knowledge base. As a result, a text mining platform has been built, enabling research in the areas of text mining and semantic information retrieval. In the paper some of the implemented methods are tested from the point of view of their applicability in a real life system.

University Knowledge Base: Two Years of Experience

This chapter is devoted to the 2-years development and exploitation of the repository platform built at Warsaw University of Technology for the purpose of gathering University research knowledge. The platform has been developed under the SYNAT project, aimed at building nation-wide scientific information infrastructure. The implementation of the platform in the form of the advanced information system is discussed. New functionalities of the knowledge base are presented.

Integrating IR with CRIS – a novel researcher-centric approach

Purpose The purpose of this paper is to present a solution for building an institutional information system (IIS) for the university, so that it combines the functionality of institutional repository (IR) with the functionality of current research information system (CRIS). The paper presents functionality of a system that has been implemented at Warsaw University of Technology (WUT), which solves the requirements of both system types. In addition, applied AI technologies aiming at providing features attractive for the system beneficiaries are presented. Design/methodology/approach The authors have reviewed various approaches to IIS, analyzed the problems observed by researchers in combining CRIS with IR, and have shown how the problems can be solved within a system that integrates various functionalities. Based on this analysis, the authors have implemented software Ω-ΨR (OMEGA-PSIR) for an academic IIS, which integrates requirements of both system types, and then deployed it at WUT. Findings It is shown that although a classical repository is an important part of the CRIS/IR system, the essential value of the solution is in providing analytical tools for “research management.” Based on the example of OMEGA-PSIR, the authors have also presented how the researcher-centric approach influences the acceptance rate of the academic community. It is also shown how the researcher-centric approach can take advantage from integrating the conflicting functionalities of IR and CRIS. Practical implications The paper bridges the gap between theory and practice in the area of IIS for academic institutions. It constructively discusses the role of institutional IR and it provides guides how to develop a system combining functionalities of CRIS and IR, as well as how to make IIS more attractive for the users by making the system researcher centric. Originality/value The survey of various approaches to IIS is unique. The research-centric approach and its implementation within OMEGA-PSIR system are original. Lessons learned from deploying the software at the WUT are of great value for institutions planning to install IR/CRIS solutions. A survey research concerning the system usability is provided, showing practical usefulness of the proposed approach.

Phylogenetic trees dissimilarity measure based on strict frequent splits set and its application for clustering

This paper focuses on clustering of leaf-labelled trees. As opposed to many other approaches from literature it is suitable not only for trees on the same leafset, but also for trees where the leafset varies. A new dissimilarity measure, constructed on the frequent subsplit term is used as the fundament of clustering technique. The clustering algorithm is designed to maximize the clustering quality measure. The computational time saving improvements are used. The initial results on phylogenetic and duplication trees are presented.

Web-Enabled Discovery and Launch

WebDiAL is a specification which tries to enable web applications to detect browsers within local network area and provide means for launching URLs by the application on the devices. Example applications and implementation, as well as security considerations, are also presented in this paper.