Latent semantic mapping (LSM) is a generalization of latent semantic analysis (LSA), a paradigm originally developed to capture hidden word patterns in a text document corpus. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. It operates under the assumption that there is some latent semantic structure in the data, which is partially obscured by the randomness of word choice with respect to retrieval. Algebraic and/or statistical techniques are brought to bear to estimate this structure and get rid of the obscuring ""noise."" This results in a parsimonious continuous parameter description of words and documents, which then replaces the original parameterization in indexing and retrieval. This approach exhibits three main characteristics: -Discrete entities (words and documents) are mapped onto a continuous vector space; -This mapping is determined by global correlation patterns; and -Dimensionality reduction is an integral part of the process. Such fairly generic properties are advantageous in a variety of different contexts, which motivates a broader interpretation of the underlying paradigm. The outcome (LSM) is a data-driven framework for modeling meaningful global relationships implicit in large volumes of (not necessarily textual) data. This monograph gives a general overview of the framework, and underscores the multifaceted benefits it can bring to a number of problems in natural language understanding and spoken language processing. It concludes with a discussion of the inherent tradeoffs associated with the approach, and some perspectives on its general applicability to data-driven information extraction. Contents: I. Principles / Introduction / Latent Semantic Mapping / LSM Feature Space / Computational Effort / Probabilistic Extensions / II. Applications / Junk E-mail Filtering / Semantic Classification / Language Modeling / Pronunciation Modeling / Speaker Verification / TTS Unit Selection / III. Perspectives / Discussion / Conclusion / Bibliography

Semantic mapping, a categorical structuring of information in graphic form, has been used successfully in many classrooms. This book provides a comprehensive introduction to this instructional strategy and presents the theory that underlies the proven effectiveness of the semantic mapping procedure. Numerous examples of completed maps are included.

In information retrieval, Latent Semantic Mapping enables retrieval on the basis of conceptual content instead of merely matching words between queries and documents. It operates under the assumption that there is some latent semantic structure in the data, which is partially obscured by the randomness of word choice with respect to retrieval. Algebraic and/or statistical techniques are brought to bear to estimate this structure and get rid of the obscuring "noise." This results in a parsimonious continuous parameter description of words and documents, which then replaces the original parameterization in indexing and retrieval.This monograph gives a general overview of the framework and underscores the multi-faceted benefits it can bring to a number of problems in natural language understanding and spoken language processing. It concludes with a discussion of the inherent trade-offs associated with the approach and some perspectives on its general applicability to unsupervised information extraction.

The book written by Dr. Radu B. Rusu presents a detailed description of 3D Semantic Mapping in the context of mobile robot manipulation. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models that include the objects present in the world, together with their position, form, and other semantic aspects, as well as interpretations of these objects with respect to the robot tasks. The book proposes novel 3D feature representations called Point Feature Histograms (PFH), as well as a frameworks for the acquisition and processing of Semantic 3D Object Maps with contributions to robust registration, fast segmentation into regions, and reliable object detection, categorization, and reconstruction. These contributions have been fully implemented and empirically evaluated on different robotic systems, and have been the original kernel to the widely successful open-source project the Point Cloud Library (PCL) -- see http://pointclouds.org.

This book discusses semantic feature analysis, a strategy that helps teachers focus students' attention on vocabulary and increase their sensitivity to language. The first half of the book deals with the theoretical foundation, reviews the research, and describes the basic teaching strategy of semantic feature analysis. The book's second half is devoted to classroom applications of this knowledge, and includes semantic feature analysis in content area lessons, in reading instruction, in integrated reading/writing lessons, as well as a semantic feature database lesson. Twenty-one figures are included, and 71 references are attached. (SR)

A universal approach to the ontology of geographic space has already been, and is going to be, a comprehensive task for establishing more effective spatial models. The concept of a universal spatial ontology should be independent of location, culture, and time. It should be fundamental and universal in the same way that the number p defines the ratio between the diameter and the circumference of a circle. The term “universal” therefore means all-embracing and for general propose. Universal Ontology of Geographic Space: Semantic Enrichment for Spatial Data aims to escalate the current scope of research to support the development of semantically interoperable systems of geographic space. This reference will aid university lecturers and professors, students, researchers, developers of spatial applications.

This book constitutes the thoroughly refereed post-conference proceedings of the joint International Workshops on Semantic Web, Ontologies and Databases, SWDB-ODBIS 2007, co-located with the 33rd International Conference on Very Large Data Bases, VLDB 2007, held in Vienna, Austria, in September 2007. The 7 revised full papers presented together with 1 invited paper were carefully selected from 11 submissions. Among the topics addressed are semantics-aware data models and query languages; ontology-based views, mapping, transformations, and query reformulation; and storing and indexing semantic Web data and schemas.