One of the most important tasks in pattern, machine learning, and data mining is
classification problem. Introducing a general classifier is a challenge for pattern recognition …

Abstract Machine processable data that narrate digital/non-digital resources are termed as
metadata. Different metadata standards exist for describing various types of digital objects …

Web search result clustering aims to facilitate information search on the Web. Rather than
the results of a query being presented as a flat list, they are grouped on the basis of their …

We address the ad hoc document retrieval task by devising novel types of entity-based
language models. The models utilize information about single terms in the query and …

Large knowledge bases consisting of entities and relationships between them have become
vital sources of information for many applications. Most of these knowledge bases adopt the …

In this paper, we present a novel approach to Web search result clustering based on the
automatic discovery of word senses from raw text, a task referred to as Word Sense …

We present a novel framework for the query-performance prediction task. That is, estimating
the effectiveness of a search performed in response to a query in lack of relevance …

Result diversification is a retrieval strategy for dealing with ambiguous or multi‐faceted
queries by providing documents that cover as many facets of the query as possible. We …

Over the years, various meta-languages have been used to manually enrich documents with
conceptual knowledge of some kind. Examples include keyword assignment to citations or …

An important challenge in cluster-based document retrieval is ranking document clusters by
their relevance to the query. We present a novel cluster ranking approach that utilizes …