Marek Kozłowski

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.

SnS: A Novel Word Sense Induction Method

The paper is devoted to the word sense induction problem. We propose a knowledge-poor method, called SenseSearcher (SnS), which induces senses of words from text corpora, based on closed frequent sets. The algorithm discovers a hierarchy of senses, rather than a flat list of concepts, so the results are easier to comprehend. We have evaluated the SnS quality by performing experiments for web search result clustering task with the datasets from SemEval-2013 Task 11.

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.

A novel method for dictionary translation

The paper addresses the problem of automatic dictionary translation.The proposed method translates a dictionary by means of mining repositories in the source and target languages, without any directly given relationships connecting the two languages. It consists of two stages: (1) translation by lexical similarity, where words are compared graphically, and (2) translation by semantic similarity, where contexts are compared. In the experiments Polish and English version of Wikipedia were used as text corpora. The method and its phases are thoroughly analyzed. The results allow implementing this method in human-in-the-middle systems.

A Seed Based Method for Dictionary Translation

The paper refers to the topic of automatic machine translation. The proposed method enables translating a dictionary by means of mining repositories in the source and target repository, without any directly given relationships connecting two languages. It consists of two stages: (1) translation by lexical similarity, where words are compared graphically, and (2) translation by semantic similarity, where contexts are compared. Polish and English version of Wikipedia were used as multilingual corpora. The method and its stages are thoroughly analyzed. The results allow implementing this method in human-in-the-middle systems.

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.

Semantic Enriched Short Text Clustering

The paper is devoted to the issue of clustering short texts, which are free answers gathered during brain storming seminars. Those answers are short, often incomplete, and highly biased toward the question, so establishing a notion of proximity between texts is a challenging task. In addition, the number of answers is counted up to hundred instances, which causes sparsity. We present three text clustering methods in order to choose the best one for this specific task, then we show how the method can be improved by a semantic enrichment, including neural-based distributional models and external knowledge resources. The algorithms have been evaluated on the unique seminar’s data sets.

Word Sense Induction with Closed Frequent Termsets

The article is devoted to the problem of word sense induction. We propose a method for inducing senses from a raw text corpus. The proposed sense induction algorithm (called SenseSearcher, or SnS) is based on closed frequent sets, and as a result, it provides a multilevel sense representation. To a large extent, it is a knowledge‐poor approach, as it does not need any kind of structured knowledge base about senses and there is no deep language knowledge embedded. By discovering a hierarchy of senses, the algorithm enables identifying subsenses (fine‐grained senses). SnS discovers not only frequent (dominating) senses but also infrequent ones (dominated). The method was evaluated in two main areas: lexicography and information retrieval. With the use of the SnS algorithm, we provide a tool able to induce from a textual corpus a structure of senses, with a varying number of granularity levels. In the area of information retrieval, SnS can be used for clustering search result, according to the discovered senses. The experiments have shown that SnS performs better than the methods participating in the SemEval2013 WSI Task 11 competition, and most of the known search result clustering methods.

Web Search Results Clustering Using Frequent Termset Mining

We present a novel method for clustering web search results based on frequent termsets mining. First, we acquire the senses of a query by means of a word sense induction method that identify meanings as trees of closed frequent termsets. Then we cluster the search results based on their lexical and semantic intersection with induced senses. We show that our approach is better or comparable with state-of-the-art classical search result clustering methods in terms of both clustering quality and degree of diversification.

Retrieval and Management of Scientific Information from Heterogeneous Sources

The paper describes the process of automated retrieval and management of scientific information from various sources including the Internet. Application of semantic methods in different phases of the process is described. The system envisaged in the project is a scientific digital library, with automated retrieval and hosting capabilities. An overall architecture for the system is proposed.