Multimedia Web Ontology Language

Abstract: Preservation of intangible cultural heritage, such as music and dance, requires encoding of background knowledge together with digitized records of the performances. We present an ontology-based approach for designing a cultural heritage repository for that purpose. Since dance and music are recorded in multimedia format, we use Multimedia Web Ontology Language (MOWL) to encode the domain knowledge. We propose an architectural framework that includes a method to construct the ontology with a labeled set of training data and use of the ontology to automatically annotate new instances of digital heritage artifacts. The annotations enable creation of a semantic navigation environment in a cultural heritage repository. We have demonstrated the efficacy of our approach by constructing an ontology for the cultural heritage domain of Indian classical dance, and have developed a browsing application for semantic access to the heritage collection of Indian dance videos.

Abstract: Several multimedia applications need to reason with concepts and their media properties in specific domain contexts. Media properties of concepts exhibit some unique characteristics that cannot be dealt with conceptual modeling schemes followed in the existing ontology representation and reasoning schemes. We have proposed a new perceptual modeling technique for reasoning with media properties observed in multimedia instances and the latent concepts. Our knowledge representation scheme uses a causal model of the world where concepts manifest in media properties with uncertainties. We introduce a probabilistic reasoning scheme for belief propagation across domain concepts through observation of media properties. In order to support the perceptual modeling and reasoning paradigm, we propose a new ontology language, Multimedia Web Ontology Language (MOWL). Our primary contribution in this article is to establish the need for the new ontology language and to introduce the semantics of its novel language constructs. We establish the generality of our approach with two disperate knowledge-intensive applications involving reasoning with media properties of concepts.

Abstract: In this work, we offer an approach to combine standard multimedia analysis techniques with knowledge drawn from conceptual metadata provided by domain experts of a specialized scholarly domain, to learn a domain-specific multimedia ontology from a set of annotated examples. A standard Bayesian network learning algorithm that learns structure and parameters of a Bayesian network is extended to include media observables in the learning. An expert group provides domain knowledge to construct a basic ontology of the domain as well as to annotate a set of training videos. These annotations help derive the associations between high-level semantic concepts of the domain and low-level MPEG-7 based features representing audio-visual content of the videos. We construct a more robust and refined version of this ontology by learning from this set of conceptually annotated videos. To encode this knowledge, we use MOWL, a multimedia extension of Web Ontology Language (OWL) which is capable of describing domain concepts in terms of their media properties and of capturing the inherent uncertainties involved. We use the ontology specified knowledge for recognizing concepts relevant to a video to annotate fresh addition to the video database with relevant concepts in the ontology. These conceptual annotations are used to create hyperlinks in the video collection, to provide an effective video browsing interface to the user.

Abstract: This paper exhibits a novel context-aware service framework for IoT based Smart Traffic Management using ontology to regulate smooth traffic flow in smart cities by analyzing real-time traffic environment. The proposed approach makes smarter use of transport networks to achieve objectives related to performance of transport system. This requires efficient traffic planning measures which relate to the actions designed to adjust the demand and capacity of the network in time and space by use of IoT technologies. The adoption of sensors and IoT devices in Smart Traffic System helps to capture the user's preferences and context information which can be in the form of travel time, weather conditions or real-life driving patterns. We have employed multimedia ontology to derive higher level descriptions of traffic conditions and vehicles from perceptual observation of traffic information which provides important grounds for our proposed IoT framework. The multimedia ontology encoded in Multimedia Web Ontology Language(MOWL) helps to define classes, properties, and structure of a possible traffic environment to provide insights across the transportation network. MOWL supports Dynamic Bayesian networks (DBN) to deal with time-series data and uncertainties linked with context observations which fits the definition of an intelligent IoT system. Thus, our proposed smart traffic framework aggregates information corresponding to traffic domain such as traffic videos captured using CCTV cameras and allows automatic prediction of dynamically changing situations which helps to make traffic authorities more responsive. We have illustrated use of our approach by utilizing contextual information, to assess real-time congestion situation on roads thus allowing to visualize planning services. Once the congestion situation is predicted, alternate congestion free routes which are in accordance with the coveted criteria are suggested that can be propagated through text-messages or e-mails to the users.

Abstract: Ubiquitous intelligent devices have enabled provision of smart services to people in seamless way. Context-awareness helps understand current state-of-affairs or the situation in which presently the system is. This understanding helps the IoT application provide more relevant and smarter services based on situations that change over a period of time. In this paper, we propose a novel context-aware situation-tracking framework that makes use of an ontology. The ontology represents the conceptual model of a dynamic world, where situations evolve over time in changing contexts. The ontology provides the reasoning framework to infer about a situation based on the input context data as well as the past information of earlier situations. Future situations can be predicted with some belief based on current situation and incoming context data. The context data is acquired from sensor devices and external inputs. For every recognized situation, system recommends some actions to provide context-aware service. We use Multimedia Web Ontology Language (MOWL) to represents the ontology. MOWL proposes a probabilistic framework for reasoning with uncertainties linked with observation of context. It makes use of Dynamic Bayesian networks to predict and track the dynamically changing situations. We illustrate use of this framework for Smart Mirror use case.