Electronic Business: Concepts, Methodologies, Tools, and Applications (4-Volumes) P253

Semantic Knowledge Transparency in E-Business Processes Semantic Interoperability. Semantic in-teroperability³LVDG\QDPLFHQWHUSULVHFDSDELOLW\ derivate from the application of special software technologies (such as reasoners, inference engines, ontologies, and models) that infer, relate, and classify the implicit meanings of digital content without human involvement—which in turn drive adaptive business processes, enterprise knowledge, business rules, and software ap-plication interoperability” (Pollock & Hodgson, 2004, p. 6) Semantic Knowledge Transparency. Se-mantic knowledge transparency LVGH¿QHGDV the G\QDPLFRQGHPDQGDQGVHDPOHVVÀRZRIUHOHYDQW and unambiguous, machine-interpretable knowl-edge resources within organizations and across inter-organizational systems of business partners engaged in collaborative processes. TBox. TBox contains intentional knowledge in the form of a terminology and is built through declarations that describe general properties of concepts (Baader et al., 2003; Gomez-Perez et al., 2004). This work was previously published in Semantic Web Technologies and E-Business: Toward the Integrated Virtual Organiza-tion and Business Process Automation, edited by A. Salam and J. Stevens, pp. 255-286, copyright 2007 by IGI Publishing (an imprint of IGI Global). 2454 2455 Chapter 8.7 Enhancing E-Business on the Semantic Web through Automatic Multimedia Representation Manjeet Rege Wayne State University, USA Ming Dong Wayne State University, USA Farshad Fotouhi Wayne State University, USA ABSTRACT With the evolution of the next generation Web— the Semantic Web—e-business can be expected to grow into a more collaborative effort in which businesses compete with each other by collabo-rating to provide the best product to a customer. Electronic collaboration involves data interchange with multimedia data being one of them. Digital multimedia data in various formats have increased tremendously in recent years on the Internet. An automated process that can represent multimedia data in a meaningful way for the Semantic Web is highly desired. In this chapter, we propose an automatic multimedia representation system for the Semantic Web. The proposed system learns DVWDWLVWLFDOPRGHOEDVHGRQWKHGRPDLQVSHFL¿F training data and performs automatic semantic annotation of multimedia data using eXtensible Markup Language (XML) techniques. We dem-onstrate the advantage of annotating multimedia data using XML over the traditional keyword based approaches and discuss how it can help e-business. Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Enhancing E-Business on the Semantic Web through Automatic Multimedia Representation INTRODUCTION An Internet user typically conducts separate in-dividual e-business transactions to accomplish a certain task. A tourist visiting New York might purchase airfare tickets and tickets to a concert in New York separately. With the evolution of the Semantic Web, as shown in Figure 1, the user can conduct one collaborative e-business transaction for the two purchases. Moreover, he/she can also take a virtual tour of New York city online, which actually might be a collection of all videos, images, and songs on New York appearing anywhere on the World Wide Web. With the continuing growth and reach of the Web, the multimedia data avail-able on it continue to grow on a daily basis. For a successful collaborative e-business, in addition to other kinds of data, it is important to be able to organize and search the multimedia data for the Semantic Web. With the Semantic Web being the future of the World Wide Web of today, there has to be an HI¿FLHQWZD\WRUHSUHVHQWWKHPXOWLPHGLDGDWD automatically for it. Multimedia data impose a great challenge to document indexing and retrieval as it is highly unstructured and the semantics are implicit in the content of it. Moreover, most of the multimedia contents appearing on the Web have no description available with it in terms of keywords or captions. From the Semantic Web point of view, this information is crucial because it describes the content of multimedia data and would help represent it in a semantically meaning-ful way. Manual annotation is feasible on a small set of multimedia documents but is not scalable as the number of multimedia documents increases. Hence, performing manual annotation of all Web PXOWLPHGLDGDWDZKLOH³PRYLQJ´WKHPWRWKH6H-mantic Web domain is an impossible task. This we believe is a major challenge in transforming today’s Web multimedia data into tomorrow’s Semantic Web data. In this chapter, we propose a generic auto-matic multimedia representation solution for the Semantic Web—an XML-based (Bray, Paoli, & Sperberg-McQueen, 1998) automatic multimedia representation system. The proposed system is implemented using images as an example and SHUIRUPVGRPDLQVSHFL¿FDQQRWDWLRQXVLQJ;0/ 6SHFL¿FDOO\RXUV\VWHP³OHDUQV´IURPDVHWRI GRPDLQVSHFL¿FWUDLQLQJLPDJHVPDGHDYDLODEOHWR it a priori. Upon receiving a new image from the Web that belongs to one of the semantic catego-ries the system has learned, the system generates appropriate XML-based annotation for the new LPDJHPDNLQJLW³UHDG\´IRUWKH6HPDQWLF:HE Although the proposed system has been described from the perspective of images, in general it is Figure 1. Collaborative e-business scenario on the Semantic Web 2456 Enhancing E-Business on the Semantic Web through Automatic Multimedia Representation applicable to many kinds of multimedia data available on the Web today. To our best knowl-edge, there has been no work done on automatic multimedia representation for the Semantic Web using the semantics of XML. The proposed system LVWKH¿UVWZRUNLQWKLVGLUHFWLRQ BACKGROUND The term e-business in general refers to online transactions conducted on the Internet. These are PDLQO\FODVVL¿HGLQWRWZRFDWHJRULHVbusiness-to-consumer (B2C) and business-to-business (B2B). One of the main differences between these two kinds of e-businesses is that B2C, as the name suggests, applies to companies that sell their products or offer services to consumers over the Internet. B2B on the other hand are online transactions conducted between two companies. From its initial introduction in late 1990s, e-busi-ness has grown to include services such as car rentals, health services, movie rentals, and online banking. The Web site CIO.com (2006) reports that North American consumers have spent $172 billion shopping online in 2005, up from $38.8 billion in 2000. Moreover, e-business is expected to grow even more in the coming years. By 2010, consumers are expected to spend $329 billion each year online. We expect the evolving Semantic Web WRSOD\DVLJQL¿FDQWUROHLQHQKDQFLQJWKHZD\H business is done today. However, as mentioned in the earlier section, there is a need to represent the multimedia data on the Semantic Web in an HI¿FLHQWZD\,QWKHIROORZLQJVHFWLRQZHUHYLHZ some of the related work done on the topic. Ontology/Schema-Based Approaches Ontology-based approaches have been frequently of graduation ceremony images by creating hi-erarchical annotation. They used Protégé (n.d.) DVWKHRQWRORJ\HGLWRUIRUGH¿QLQJWKHRQWRORJ\ and annotating images. Schreiber, Dubbeldam, Wielemaker, and Wielinga (2001) also performed ontology-based annotation of ape photographs, LQZKLFKWKH\XVHWKHVDPHRQWRORJ\GH¿QLQJ DQGDQQRWDWLRQWRRODQGXVH5HVRXUFH`H¿QL-tion Framework (RDF) Schema as the output language. Nagao, Shirai, and Squire (2001) have developed a method for associating external an-notations to multimedia data appearing over the Web. Particularly, they discuss video annotation by performing automatic segmentation of video, semiautomatic linking of video segments, and interactive naming of people and objects in video frames. More recently, Rege, Dong, Fotouhi, Sia-dat, and Zamorano (2005) proposed to annotate human brain images using XML by following the MPEG-7 (Manjunath, 2002) multimedia standard. The advantages of using XML to store meta-information (such as patient name, surgery location, etc.), as well as brain anatomical infor-mation, has been demonstrated in a neurosurgical domain. The major drawback of the approaches, mentioned previously, is that the image annotation is performed manually. There is an extra effort needed from the user’s side in creating the ontol-ogy and performing the detailed annotation. It is highly desirable to have a system that performs automatic semantic annotation of multimedia data on the Internet. Keyword-Based Annotations Automatic image annotation using keywords has recently received extensive attention in the research community. Mori, Takahashi, and Oka (1999) developed a co-occurrence model, in which they looked at the co-occurrence of keywords with image regions. Duygulu, Barnard, Freitas, and used for multimedia annotation and retrieval. Forsyth (2002) proposed a method to describe Hyvonen, Styrman, and Saarela (2002) proposed ontology-based image retrieval and annotation images using a vocabulary of blobs. First, regions are created using a segmentation algorithm. For 2457 Enhancing E-Business on the Semantic Web through Automatic Multimedia Representation each region, features are computed and then blobs are generated by clustering the image features for these regions across images. Finally, a translation model translates the set of blobs of an image to a set of keywords. Jeon, Lavrenko, and Man-matha (2003) introduced a cross-media relevance model that learns the joint distribution of a set of regions and a set of keywords rather than the cor-respondence between a single region and a single keyword. Feng, Manmatha, and Lavrenko (2004) proposed a method of automatic annotation by partitioning each image into a set of rectangular regions. The joint distribution of the keyword annotations and low-level features is computed from the training set and used to annotate test-ing images. High annotation accuracy has been reported. The readers are referred to Barnard, Duygulu, Freitas, and Forsyth (2003) for a com-prehensive review on this topic. As we point out in WKHVHFWLRQ³;0/%DVHG$QQRWDWLRQ´NH\ZRUG annotations do not fully express the semantic meaning embedded in the multimedia data. In this paper, we propose an Automatic Multimedia Representation System for the Semantic Web using the semantics of XML, which enables ef-¿FLHQWPXOWLPHGLDDQQRWDWLRQDQGUHWULHYDOEDVHG on the domain knowledge. The proposed work is WKH¿UVWDWWHPSWLQWKLVGLUHFWLRQ PROPOSED FRAMEWORK In order to represent multimedia data for the Semantic Web, we propose to perform automatic multimedia annotation using XML techniques. Though the proposed framework is applicable to multimedia data in general, we provide details about the framework using image annotations as a case study. XML-Based Annotation $QQRWDWLRQVDUHGRPDLQVSHFL¿FVHPDQWLFLQIRU-mation assigned with the help of a domain expert to semantically enrich the data. The traditional approach practiced by image repository librarians is to annotate each image manually with keywords or captions and then search on those captions or keywords using a conventional text search engine. The rationale here is that the keywords capture the semantic content of the image and help in retrieving the images. This technique is also used by television news organizations to retrieve ¿OHIRRWDJHIURPWKHLUYLGHRV6XFKWHFKQLTXHV DOORZWH[WTXHULHVDQGDUHVXFFHVVIXOLQ¿QGLQJ the relevant pictures. The main disadvantage with PDQXDODQQRWDWLRQVLVWKHFRVWDQGGLI¿FXOW\RI scaling it to large numbers of images. MPEG-7 (Manjunath, 2002, p. 8) describes WKHFRQWHQW²³WKHELWVDERXWWKHELWV´²RIDPXO-WLPHGLD¿OHVXFKDVDQLPDJHRUDYLGHRFOLS7KH MPEG-7 standard has been developed after many rounds of careful discussion. It is expected that this standard would be used in searching and retrieving for all types of media objects. It proposes to store low-level image features, annotations, and other PHWDLQIRUPDWLRQLQRQH;0/¿OHWKDWFRQWDLQV a reference to the location of the corresponding LPDJH¿OH;0/KDVEURXJKWJUHDWIHDWXUHVDQG promising prospects to the future of the Semantic Web and will continue to play an important role in its development. XML keeps content, structure, and representation apart and is a much more adequate means for knowledge representation. It can represent semantic properties through its syntactic structure, that is, by the nesting or se-quentially ordering relationship among elements (XML tags). The advantage of annotating mul-timedia using XML can best be explained with the help of an example. Suppose we have a New York image (shown in Figure 2) with keywords annotation of Statue of Liberty, Sea, Clouds, Sky. Instead of simply using keywords as annotation for this image, consider now that the same image is represented in an XML format. Note that the XML representation of the im-DJHFDQFRQIRUPWRDQ\GRPDLQVSHFL¿F;0/ schema. For the sake of illustration, consider 2458 ... - tailieumienphi.vn