International Journal of Semantic Computing

The performance of ranking the retrieval results based on context and/or content information is usually limited because it depends heavily on the quality of the extracted information. Therefore, re-ranking approaches have been developed to improve the retrieval performance, but how to re-rank the retrieval results remains a challenge. In this paper, the inclusive and exclusive relationships between semantic concepts are utilized to re-rank the retrieval results. The inclusive relationship refers to the high co-occurrence relation between a target semantic concept and a reference semantic concept; while the exclusive relationship refers to the low or none co-occurrence relation. Moreover, multiple reference concepts provide more information from different aspects than only one reference concept does. Multiple Correspondence Analysis (MCA) is applied in this paper to capture the correlation in terms of the impact weights between attributes and the co-occurrence classes, and the average value of all the impact weights is utilized to re-rank the retrieved results for the target semantic concept. Experimental results on the MediaMill Challenge problem and the TRECVID 2011 video collections demonstrate that our proposed framework can enhance the retrieval results for a number of target semantic concepts.

To be effective multimedia retrieval mechanisms, index methods must provide not only efficient access but also meaningful retrieval by addressing challenges in multimedia retrieval. This article presents the AH⁺-tree, a height-balanced, tree-based index structure that efficiently incorporates high-level affinity information to support Content-Based Image Retrieval (CBIR) through similarity queries. The incorporation of affinity information allows the AH⁺-tree to address the problems of semantic gap and user perception subjectivity inherent to multimedia retrieval. Based on the Affinity-Hybrid Tree (AH-Tree), the AH⁺-tree utilizes affinity information in a novel way to eliminate the I/O overhead of the AH-Tree while maintaining the same functionality and quality of results. We explain the structure of the AH⁺-tree and implement and analyze algorithms for tree construction and similarity queries (range and nearest neighbor). Experimental results demonstrate the superior I/O efficiency of the AH⁺-tree over that of the AH-Tree and the M-tree without a detrimental impact on real-time costs of the retrieval process.

We propose a method for discriminating between a speech shot and a narrated shot to extract genuine speech shots from a broadcast news video. Speech shots in news videos contain a wealth of multimedia information of the speaker, and could thus be considered valuable as archived material. In order to extract speech shots from news videos, there is an approach that uses the position and size of a face region. However, it is difficult to extract them with only such an approach, since news videos contain non-speech shots where the speaker is not the subject that appears in the screen, namely, narrated shots. To solve this problem, we propose a method to discriminate between a speech shot and a narrated shot in two stages. The first stage of the proposed method directly evaluates the inconsistency between a subject and a speaker based on the co-occurrence between lip motion and voice. The second stage of the proposed method evaluates based on the intra- and inter-shot features that focus on the tendency of speech shots. With the combination of both stages, the proposed method accurately discriminates between a speech shot and a narrated shot. In the experiments, the overall accuracy of speech shots extraction by the proposed method was 0.871. Therefore, we confirmed the effectiveness of the proposed method.

This paper introduces a novel framework for Ontology-Driven Software Engineering. This framework is grounded on the prior related work that studied the interplay between the model-driven engineering and the ontological modeling. Our framework makes a contribution by incorporating a more flexible means for ontological modeling that also has a higher performance in processing, and by incorporating a wider range of ontology types into ODSE. As a result, it extends the power and speed of the classification and the model consistency checking ontological services enabled by the prior work, and brings new ontological services: semantic search in model repositories, three kinds of semi-automated model composition services: task-based, result-based, and opportunistic, and the policy enforcement service. The primary intended use for this framework is to be implemented as part of model-driven engineering tools to support software engineers. We describe our reference implementation of such a tool called Smart Modeller, and report on a performance evaluation of our framework carried out with the help of it.

Semantic web services (SWS) have recently attracted much attention because of their potential to automate all common service tasks such as discovery, composition, invocation and execution. The successful implementation of SWS is profoundly based on the availability of appropriate methods for SWS description. There is, however, no consensus in the SWS arena on the significant service elements that make SWS description comprehensive. Furthermore, semantic annotation of those elements is still a manual process where human involvement is a must. Therefore, automating the annotation process is highly desired as the manual annotation is a difficult, costly and time-consuming process. Few approaches exist aiming to semi-automate the annotation task. These approaches have limitations preventing their wide adoption by the research and practice communities. This paper contributes by analysing and comparing those existing semi-automatic Web service annotation approaches in order to highlight the major challenges and provide fruitful guidelines to aid future research. Particularly, a synthesis of service elements that require semantic annotation is provided and then used to assess the completeness of semi-automated approaches. The comparison leads to significant implications for theory and practice.