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Semantic computing, a rapidly evolving interdisciplinary field, seeks to structure, design, and manipulate computer content to better satisfy the needs and intentions of users and create a more meaningful user experience. This remarkable contributed work examines the art, engineering, technology, and applications of the field. Moreover, it brings together researchers from such disciplines...
Semantic computing, a rapidly evolving interdisciplinary field, seeks to structure, design, and manipulate computer content to better satisfy the needs and intentions of users and create a more meaningful user experience. This remarkable contributed work examines the art, engineering, technology, and applications of the field. Moreover, it brings together researchers from such disciplines as natural language processing, software engineering, multimedia semantics, semantic Web, signal processing, and pattern recognition in order to provide a single source that presents the state of the technology and points to new breakthroughs on the horizon.
Semantic Computing begins with an introduction that explores the concepts, technology, applications, and future of semantic computing. Next, the book is divided into four parts:
Part One: Semantic Analysis
Part Two: Semantic Languages and Integration
Part Three: Semantic Applications
Part Four: Semantic Programming and Interface
As readers progress through the book, they'll learn not only the underlying science, but also the fundamental technological building blocks of semantic computing. Moreover, they'll discover a variety of cross-disciplinary solutions to current computing and communication problems. Throughout the book, references to the primary literature enable further investigation of each individual topic.
Semantic Computing is ideal for industrial managers, researchers, and engineers seeking to design the next generation of computing systems in order to better meet user needs. It is also recommended as a textbook for senior undergraduate and graduate-level semantic computing courses.
1 Semantic Computing.
PART I: SEMANTIC ANALYSIS.
2 What Computers Need to Know about Verbs.
3 Content-based Retrieval of Videos.
4 Semantic Approaches for Speech Recognition.
5 Link Analysis in Web Mining.
6 Analysis of Online Blogs and Chats.
7 Speaker Identification and Diarization: State-of-the-art and Applications.
8 Machine Learning Methods for Ontology Mining.
9 Visual Ontology Development and Large Scale Semantic Concept Detection.
10 Process Mining and Description.
PART II: SEMANTIC LANGUAGES AND INTEGRATION.
11 Semantic Integration: The Hawkeye Approach.
12 Semantics of Software Modeling.
13 Semantic-Driven Component-Based Automated Code Synthesis.
14 Semantic Web Services.
PART III: SEMANTIC APPLICATIONS.
15 Knowledge Work.
16 Video Search in Visual Semantic Space.
17 Social and Expert Search in Online Communities.
18 Phase-Coherence in Conceptual Spaces for Conversational Agents.
19 The Role of Semantics in Question Answering.
20 Developing and Using a National Cross-domain Semantic Web Infrastructure.
21 Semantic Computing Challenges in MM Security.
PART IV: SEMANTIC PROGRAMMING AND INTERFACE.
22 Semantic Languages for Software Engineering.
23 From Semantic Objects to Structured Natural Language.
Posted July 17, 2010
The book is a lengthy compilation of current research efforts in the adding of more intelligence to computing. Not surprisingly, many of the chapters refer to the Web 2.0 or the Semantic Web. Many challenges remain as the authors make clear.
One of these is described in Chapter 21, "Semantic Analysis for Multimedia Security Applications". The problem is how to programmatically extract meaning from videos, where these might be amassed via security cameras, or more generally from other cameras. A video consists of a sequence of frames. There is a hierarchy of structure, starting with a frame at the lowest level. A shot is a set of frames from 1 camera showing 1 event. While a scene is a series of shots taken at a single location. The entire video is then a collection of scenes. While a human observer can readily discern a shot and a scene, how is this done by computer? Various methods of using a histogram distance metric and a spatial distance metric are given. The chapter goes well beyond work confined to analysing single images.
Subsequent difficulties include how to find shot boundaries and getting a typical frame from a shot to define the shot; ie. how to get a keyframe? Another serious practical issue is the tracking of an object. Imagine following a person as she moves through a region. A higher level related task is to use motion to describe object activity in a video. So perhaps a parametric representation of the object in a 3 dimensional space can be tried. Where a bounding box could be found for an object in each frame [to the extent that this is indeed possible] and the centroid for that box is taken as the object center in establishing a trajectory.
Another common thread in several chapters is the use of ontologies. There have been and are massive efforts to bootstrap by building ontologies like WordNet. Various usages have been tried like extracting and using glosses (the textual description of a term in an ontology) for downstream analysis.
On the subject of ontologies, none of the chapters discussing this mention Ted Nelson's Xanadu, which was an early 1960s attempt at combining this with hypertext. Perhaps a sad reflection that that effort was too early and a failure.
Of the book's editors, Zadeh is clearly the best known, being the founder of fuzzy logic. Be aware that the text only has brief mentions of this topic.