Slavko Žitnik

Timotej Knez, Marko Bajec, Slavko Žitnik

Natural language processing is used for solving a wide variety of problems. Some scholars and interest groups working with language resources are not well versed in programming, so there is a need for a good graphical framework that allows users to quickly design and test natural language processing pipelines without the need for programming. The existing frameworks do not satisfy all the requirements for such a tool. We, therefore, propose a new framework that provides a simple way for its users to build language processing pipelines. It also allows a simple programming language agnostic way for adding new modules, which will help the adoption by natural language processing developers and researchers. The main parts of the proposed framework consist of (a) a pluggable Docker-based architecture, (b) a general data model, and (c) APIs description along with the graphical user interface. The proposed design is being used for implementation of a new natural language processing framework, called ANGLEr.

Slavko Žitnik, Lovro Šubelj, Dejan Lavbič et al.

Due to numerous public information sources and services, many methods to combine heterogeneous data were proposed recently. However, general end-to-end solutions are still rare, especially systems taking into account different context dimensions. Therefore, the techniques often prove insufficient or are limited to a certain domain. In this paper we briefly review and rigorously evaluate a general framework for data matching and merging. The framework employs collective entity resolution and redundancy elimination using three dimensions of context types. In order to achieve domain independent results, data is enriched with semantics and trust. However, the main contribution of the paper is evaluation on five public domain-incompatible datasets. Furthermore, we introduce additional attribute, relationship, semantic and trust metrics, which allow complete framework management. Besides overall results improvement within the framework, metrics could be of independent interest.

Lovro Šubelj, Slavko Žitnik, Neli Blagus et al.

Large software projects are among most sophisticated human-made systems consisting of a network of interdependent parts. Past studies of software systems from the perspective of complex networks have already led to notable discoveries with different applications. Nevertheless, our comprehension of the structure of software networks remains to be only partial. We here investigate correlations or mixing between linked nodes and show that software networks reveal dichotomous node degree mixing similar to that recently observed in biological networks. We further show that software networks also reveal characteristic clustering profiles and mixing. Hence, node mixing in software networks significantly differs from that in, e.g., the Internet or social networks. We explain the observed mixing through the presence of groups of nodes with common linking pattern. More precisely, besides densely linked groups known as communities, software networks also consist of disconnected groups denoted modules, core/periphery structures and other. Moreover, groups coincide with the intrinsic properties of the underlying software projects, which promotes practical applications in software engineering.