Personalized classification refers to allowing users to define their own categories and automating the assignment of documents to these categories. In this paper, we examine the use of keywords to define personalized categories and propose the use of Support Vector Machine (SVM) to perform personalized classification. Two scenarios have been investigated. The first assumes that the personalized...

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike thi...

Many users and applications require the integration of semistructured data from autonomous, heterogeneous Web sources. Over the last years mediator systems have emerged that use domain knowledge to overcome the problem of structural heterogeneity. However, many users of these systems do not have a thorough knowledge of the complex global schemas and of the comprehensive query languages. Consequ...

Semantic clustering of objects such as documents, web sites and movies based on their keywords is a challenging problem. This requires a similarity measure between two sets of keywords. We present a new measure based on matching the words of two groups assuming that a similarity measure between two individual words is available. The proposed matching similarity measure avoids the problems of tr...

This paper addresses the problem of detecting keywords in unconstrained speech without explicit modeling of non-keyword segments. The proposed algorithms are based on recent developments in confidence measures using local posterior probabilities, and searches for the segment maximizing the average observation posterior along the most likely path in the hypothesized keyword model. We can also us...

Keyword-based queries are an important means to retrieve information from XML collections with unknown or complex schemas. Relevance Feedback integrates relevance information provided by a user to enhance retrieval quality. For keyword-based XML queries, feedback engines usually generate an expanded keyword query from the content of elements marked as relevant or nonrelevant. This approach that...

The paper summarizes our research in the area of unsupervised categorization of Wikipedia articles. As a practical result of our research, we present an application of spectral clustering algorithm used for grouping Wikipedia search results. The main contribution of the paper is a representation method for Wikipedia articles that has been based on combination of words and links and used for cat...

In this paper we describe Keymantic, a framework for translating keyword queries into SQL queries by assuming that the only available information is the source metadata, i.e., schema and some external auxiliary information. Such a framework finds application when only intensional knowledge about the data source is available like in Data Integration Systems.

We present a novel method for translating keyword queries over relational databases into SQL queries with the same intended semantic meaning. In contrast to the majority of the existing keyword-based techniques, our approach does not require any a-priori knowledge of the data instance. It follows a probabilistic approach based on a Hidden Markov Model for computing the top-K best mappings of th...

Keyword search is an intuitive paradigm for searching linked data sources on the web. We propose to route keywords only to relevant sources to reduce the high cost of processing keyword search queries over all sources. We propose a novel method for computing top-k routing plans based on their potentials to contain results for a given keyword query. We employ a keyword-element relationship summa...