In the last 10 years, the information generated on weblog sites has increased exponentially, resulting in a clear need for intelligent approaches to analyse and organise this massive amount of information. In this work, we present a methodology to cluster weblog posts according to the topics discus sed therein, which we derive by text analysis. We have called the methodology Prototype/Topic Based Clustering, an approach which is based on a generative probabilistic model in conjunction with a Self-Term Expansion methodology. The usage of the Self-Term Expansion methodology is to improve the representation of the data and the generative probabilistic model is employed to identify relevant topics discussed in the weblogs. We have modified the generative probabilistic model in order to exploit predefined initialisations of the model and have performed our experiments in narrow and wide domain subsets. The results of our approach have demonstrated a considerable improvement over the pre-defined baseline and alternative state of the art approaches, achieving an improvement of up to 20% in many cases. The experiments were performed on both narrow and wide domain datasets, with the latter showing better improvement. However in both cases, our results outperformed the baseline and state of the art algorithms.

Subjective language contains information about private states. The goal of subjective language identification is to determine that a private state is expressed, without considering its polarity or specific emotion. A component of word meaning, “Personal Sense,” has clear potential in the field of subjective language identification, as it reflects a meaning of words in terms of unique personal experience and carries personal characteristics. In this paper we investigate how Personal Sense can be harnessed for the purpose of identifying subjectivity in news titles. In the process, we develop a new Personal Sense annotation framework for annotating and classifying subjectivity, polarity, and emotion. The Personal Sense framework yields high performance in a fine‐grained subsentence subjectivity classification. Our experiments demonstrate lexico‐syntactic features to be useful for the identification of subjectivity indicators and the targets that receive the subjective Personal Sense.

The management and retrieval of Web data has received significant attention. Among the various approaches, systems have been proposed whose main goal is to provide a framework to integrate different and heterogeneous information sources into a common domain model. The Web-At-A-Glance (WAG) system falls into this category, its key characteristic being that instead of requiring an explicit description of the sources, it attempts to semi-automatically classify the information gathered from various sites based on the conceptual model of the domain of interest. The initial WAG prototype dealt exclusively with "standard" Web pages, which typically are HTML or XML documents and present non- or semi-structured information, and they vary widely in their means of presenting information to the user. We present the extension of the WAG system to deal with form pages. In particular, we describe how the system semiautomatically first extracts a conceptual schema for the form page, and then fills and submits the form in consequence of a user query expressed on the domain conceptual schema.

Recent years have seen the publication of several methodologies for query processing in a Multidatabase (MDB) environment. Whilst different approaches are pertinent to specific CIS architectures, the issue of efficiency of processing has received relatively little attention. This is somewhat surprising considering the potentially large costs associated with MDB queries. In this paper, we consider the efficiency of processing queries in the VENUS (Esprit 6398) environment. The goal of this system is to allow a user to query multiple data sources by interacting with a conceptually single database, and by providing a query interface based on the use of visual formalisms. This system takes advantage of existing multidatabase query processing strategies, and deals with optimization issues by considering the exploitation of inter- and intra-schema semantics. We present a methodology which harnesses knowledge of the set-theoretic relationships between classes participating in a query, and demonstrate that significant saving can be made in MDB query processing. The methodology presented here is based on a powerful data model, the Graph Model, which we argue is suitable as the canonical model of a MDB. We also introduce a logic language to formally express interdependencies between classes belonging to different schemas. These assertions form the "backbone" of the MDB and, we may benefit in several ways from the possibility of reasoning about them, specifically by applying them in MDB query processing.