Video semantic content analysis framework based on ontology combined MPEG-7

The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standard, MPEG-7, provides the rich functionalities to enable the generation of audiovisual descriptions and is expressed solely in XML Schema which provides little support for expressing semantic knowledge. In this paper, a video semantic content analysis framework based on ontology combined MPEG-7 is presented. Domain ontology is used to define high level semantic concepts and their relations in the context of the examined domain. MPEG-7 metadata terms of audiovisual descriptions and video content analysis algorithms are expressed in this ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how low-level features and algorithms for video analysis should be applied according to different perception content. Temporal Description Logic is used to describe the semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in sports video domain and shows promising results

Towards a multimedia knowledge-based agent with social competence and human interaction capabilities

We present work in progress on an intelligent embodied conversation agent in the basic care and healthcare domain. In contrast to most of the existing agents, the presented agent is aimed to have linguistic cultural, social and emotional competence needed to interact with elderly and migrants. It is composed of an ontology-based and reasoning-driven dialogue manager, multimodal communication analysis and generation modules and a search engine for the retrieval of multimedia background content from the web needed for conducting a conversation on a given topic.The presented work is funded by the European Commission under the contract number H2020-645012-RIA

From fuzzy to annotated semantic web languages

The aim of this chapter is to present a detailed, selfcontained and comprehensive account of the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions