--In this paper, we give an overview of the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the gap in terms of a classical architecture for multimedia processing and discuss a structured approach to bridge it. This approach combines machine learning (for mapping signals to objects) and symbolic AI (for linking objects to meanings). Our main goal is to raise awareness and discuss the challenges involved in this structured approach to multimedia understanding, especially in the view of the latest developments in machine learning and symbolic AI. A classic problem in multimedia representation and understanding is the semantic gap problem .
However, a number of issues are repeated across chapters, and it is not clear that the authors of each chapter had a chance to read the other chapters while they wrote theirs. The different parts of the book could have been better (more explicitly) named; for example, domains on its own means little to me! The book has an advantage in that it provides a collection of chapters on the foundations of cognitive science written by different people; hence, we see differing points of view from experts in given areas, which could not be achieved by a single author. However, a criticism of the book is that nearly all the chapters are by authors with a U.S. affiliation, with a few from England, and I find it difficult to believe that leading cognitive scientists in other countries could not have written something. Thus, we get an American-Anglo view of cognitive science rather than an international one, such as that given in Ó'Nualláin (1995).
In this chapter, we give an introduction to symbolic artificial intelligence (AI) and discuss its relation and application to multimedia. We begin by defining what symbolic AI is, what distinguishes it from non-symbolic approaches, such as machine learning, and how it can used in the construction of advanced multimedia applications. We then introduce description logic (DL) and use it to discuss symbolic representation and reasoning. DL is the logical underpinning of OWL, the most successful family of ontology languages. After discussing DL, we present OWL and related Semantic Web technologies, such as RDF and SPARQL. We conclude the chapter by discussing a hybrid model for multimedia representation, called Hyperknowledge. Throughout the text, we make references to technologies and extensions specifically designed to solve the kinds of problems that arise in multimedia representation.
Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metaknowledge in multimedia datasets. Our collaboration has been an on-going task of studying the relational patterns between datapoints based on metafeatures extracted from metaknowledge in multimedia datasets. Those selected are significant to suit the mining technique we applied, Golay Code algorithm. In this research paper we summarize findings in optimization of metaknowledge representation for 23-bit representation of structured and unstructured multimedia data in order to