Graph-based methods for Significant Concept Selection

It is well known in information retrieval area that one important issue is the gap between the query and document vocabularies. Concept-based representation of both the document and the query is one of the most effective approaches that lowers the effect of text mismatch and allows the selection of relevant documents that deal with the shared semantics hidden behind both. However, identifying the best representative concepts from texts is still challenging. In this paper, we propose a graph-based method to select the most significant concepts to be integrated into a conceptual indexing system. More specifically, we build the graph whose nodes represented concepts and weighted edges represent semantic distances. The importance of concepts are computed using centrality algorithms that levrage between structural and contextual importance. We experimentally evaluated our method of concept selection using the standard ImageClef2009 medical data set. Results showed that our approach significantly improves the retrieval effectiveness in comparison to state-of-the-art retrieval models

Metadata Augmentation for Semantic- and Context- Based Retrieval of Digital Cultural Objects

Cultural objects are increasingly stored and generated in digital form, yet effective methods for their indexing and retrieval still remain an open area of research. The main problem arises from the disconnection between the content-based indexing approach used by computer scientists and the description-based approach used by information scientists. There is also a lack of representational schemes that allow the alignment of the semantics and context with keywords and low-level features that can be automatically extracted from the content of these cultural objects. This paper presents an integrated approach to address these problems, taking advantage of both computer science and information science approaches. The focus is on the rationale and conceptual design of the system and its various components. In particular, we discuss techniques for augmenting commonly used metadata with visual features and domain knowledge to generate high-level abstract metadata which in turn can be used for semantic and context-based indexing and retrieval. We use a sample collection of Vietnamese traditional woodcuts to demonstrate the usefulness of this approach

The Epistemological Foundations of Knowledge Representations

This paper looks at the epistemological foundations of knowledge representations embodied in retrieval languages. It considers questions such as the validity of knowledge representations and their effectiveness for the purposes of retrieval and automation. The knowledge representations it considers are derived from three theories of meaning that have dominated twentieth-century philosophy.published or submitted for publicatio

Multimedia search without visual analysis: the value of linguistic and contextual information

This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features