Measures of semantic distance have received a great deal of attention recently in the field of computational lexical semantics. Although techniques for approximating the semantic distance of two concepts have existed for several decades, the introduction of the WordNet lexical database and improvements in corpus analysis have enabled significant improvements in semantic distance measures. <br /><br /> In this study we investigate a special kind of semantic distance, called <em>semantic relatedness</em>. Lexical semantic relatedness measures have proved to be useful for a number of applications, such as word sense disambiguation and real-word spelling error correction. Most relatedness measures rely on the observation that the shortest path between nodes in a semantic network provides a representation of the relationship between two concepts. The strength of relatedness is computed in terms of this path. <br /><br /> This dissertation makes several significant contributions to the study of semantic relatedness. We describe a new measure that calculates semantic relatedness as a function of the shortest path in a semantic network. The proposed measure achieves better results than other standard measures and yet is much simpler than previous models. The proposed measure is shown to achieve a correlation of <em>r</em> = 0. 897 with the judgments of human test subjects using a standard benchmark data set, representing the best performance reported in the literature. We also provide a general formal description for a class of semantic distance measures &mdash; namely, those measures that compute semantic distance from the shortest path in a semantic network. Lastly, we suggest a new methodology for developing path-based semantic distance measures that would limit the possibility of unnecessary complexity in future measures.

The tradition of semantic externalism that follows Kripke (1972) and Putnam (1975) is built on the assumption that the folk have essentialist commitments about natural kinds. Externalists commonly take the body of empirical data concerning psychological essentialism as support for this claim. However, recent empirical findings (Malt, 1994; Kalish, 2002) call the psychological theory of essentialism into question. This thesis examines the relevance of these findings to both essentialism and semantic externalism. I argue that these findings suggest that these theories fail to reflect folk beliefs about natural kinds and folk natural kind term usage. This leads me to propose an alternative thesis-- the Ambiguity Thesis-- that is better able to accommodate the existing body of empirical data.

Language production depends upon the context in which words are named. Renaming previous items results in facilitation while naming pictures semantically related to previous items causes interference. A computational model (Oppenheim, Dell, & Schwartz, 2010) proposes that both facilitation and interference are the result of using naming events as “learning experiences” to ensure future accuracy. The model successfully simulates naming data from different semantic interference paradigms by implementing a learning mechanism that creates interference and a boosting mechanism that resolves interference. This study tested this model’s assumptions that semantic interference effects in naming are created by learning and resolved by boosting. Findings revealed no relationship between individual performance across semantic interference tasks, and measured learning and boosting abilities did not predict performance. These results suggest that learning and boosting mechanisms do not fully characterize the processes underlying semantic interference when naming.

In this thesis, we plan to use a group-based semantic-expansion approach to design a new personalised system framework. Semantic web and group preference offer solution to the above problem. In this thesis, ontologies and semantic techniques are applied in different components of the framework. Information has been gathered from different resources and each of the resource might be using various types of identifiers for the same concept, therefore semantic web technologies are used to find out if the concept is the same or not. On the other hand, we create group preference in our personalization system. If the system fails to obtain personal preference from new user, group preference supports the system providing recommendation to the new user according to group classification.

During the practical use of Design Automation (DA) System in a company, the lack of assistance from either documentation work about the whole system or management of knowledge could bring out some obstacles when engineers reuse existing knowledge and information. The purpose of this project is to explore an approach of documentation and knowledge management in DA System. The study is mainly based on the actual case of seat heater DA system developed by JTH. Based on preset functional requirement for the potential solution, several principles and methods of documentation and knowledge management are introduced such as MOKA, CommonKADS, SysML and PVM. A number of useful applications such as DRed (Design Rationale Editor), PC PACK, Sementic MediaWiki and Product Model Manager became candidates solutions for this project. The selection of final approach was Sementic MediaWiki, and this is based on the comparison of the result from evaluation of functionality of each application. Due to specificity of documentation on the DA system, the “process based” approa­ch­ had been used for structuring system included knowledge instead of using a systematical method like either MOKA or CommonKADS completely. Setting up interconnection between different knowledge objects was one of the most important tasks in this project because it enables capturing and retrieving of knowledge. Sementic MediaWiki, a powerful text representative and web-based tool has been used as a platform of representing the whole knowledge and information. With its implementation, the performance of Sementic MediaWiki had been tested accor­ding to the preset functional requirement. After a slight refine process to the solution, the satisfactory result had been achieved, and also proved the applicability of Sementic Wiki in such kind of project.

Measures of semantic distance have received a great deal of attention recently in the field of computational lexical semantics. Although techniques for approximating the semantic distance of two concepts have existed for several decades, the introduction of the WordNet lexical database and improvements in corpus analysis have enabled significant improvements in semantic distance measures. <br /><br /> In this study we investigate a special kind of semantic distance, called <em>semantic relatedness</em>. Lexical semantic relatedness measures have proved to be useful for a number of applications, such as word sense disambiguation and real-word spelling error correction. Most relatedness measures rely on the observation that the shortest path between nodes in a semantic network provides a representation of the relationship between two concepts. The strength of relatedness is computed in terms of this path. <br /><br /> This dissertation makes several significant contributions to the study of semantic relatedness. We describe a new measure that calculates semantic relatedness as a function of the shortest path in a semantic network. The proposed measure achieves better results than other standard measures and yet is much simpler than previous models. The proposed measure is shown to achieve a correlation of <em>r</em> = 0. 897 with the judgments of human test subjects using a standard benchmark data set, representing the best performance reported in the literature. We also provide a general formal description for a class of semantic distance measures &mdash; namely, those measures that compute semantic distance from the shortest path in a semantic network. Lastly, we suggest a new methodology for developing path-based semantic distance measures that would limit the possibility of unnecessary complexity in future measures.

Owing to the development of Internet, excessive online data drive users to apply tools to assist them in obtaining desired and useful information. Information retrieval techniques serve as one of the major assistance tools that ease users¡¦ information processing loads. However, most current IR models do not consider processing streaming information which essentially characterizes today¡¦s Web environment. The approach to re-building models based on the full knowledge of data at hand triggered by the new incoming information every time is impractical, inefficient, and costly. Instead, IR models that can be adapted to streaming information incrementally should be considered under the dynamic environment. Therefore, this research is to propose an IR related technique, the incremental aspect model (ISM), which not only uncovers latent aspects from the collected documents but also adapts the aspect model on streaming documents chronologically. There are two stages in ISM: in Stage I, we employ probabilistic latent semantic indexing (PLSI) technique to build a primary aspect model; and in Stage II, with out-of-date data removing and new data folding-in, the aspect model can be expanded using the derived spectral method if new aspects significantly exist. Three experiments are conducted accordingly to verify ISM. Results from the first two experiments show the robust performance of ISM in incremental text clustering tasks. In Experiment III, ISM performs the task of storylines tracking on the 2010 Soccer World Cup event. It illustrates ISM¡¦s incremental learning ability to discover different themes around the event at any time. The feasibility of our proposed approach in real applications is thus justified.

Service-oriented Architecture is now an important issue when it comes to program development. However, there is not yet an efficient and effective way for developer to obtain appropriate component. Current researches mostly focus on either textual meaning or ontology relation of the services. In this research we propose a hybrid approach that integrates both types of information. It starts by defining important attributes and their weights for web service discovery using Multiple Criteria Decision Making. Then a method of similarity calculation based on both textual and ontological information is applied. In the experiment, we collect 103 real-world Web services, and the experimental results show that our approach generally performs better than the existing ones.

This dissertation investigates the extraction of knowledge from a known environment. Virtual ontology – the extracted knowledge – is defined as a structure of a virtual environment with semantics. While many existing 3D reconstruction approaches can generate virtual environments without structure and related knowledge, the use of Metaearth architecture is proposed as a more descriptive data structure for virtual ontology. Its architecture consists of four layers: interactions and relationships between virtual components can be represented in the virtual space layer; and the library layers contribute to the design of large-scale virtual environments with less redundancy; and the mapping layer links the library layer to the virtual space layer; and the ontology layer functions as a context for the extracted knowledge. The dissertation suggests two construction methodologies. The first method generates a scene structure from a 2D image. Unlike other scene understanding techniques, the suggested method generates scene ontology without prior knowledge and human intervention. As an intermediate process, a new and effective fuzzy color-based over-segmentation method is suggested. The second method generates virtual ontology with 3D information using multi-view scenes. The many ambiguities in extracting 3D information are resolved by employing a new fuzzy dynamic programming method (FDP). The hybrid approach of FDP and 3D reconstruction method generates more accurate virtual ontology with 3D information. A virtual model is equipped with virtual ontology whereby contextual knowledge can be mapped into the Metaearth architecture via the proposed isomorphic matching method. The suggested procedure guarantees the automatic and autonomous processing demanded in virtual interaction analysis with far less effort and computational time.

A typical online knowledge-sharing environment would generate vast amount of formal knowledge elements or interactions that generally available as textual documents. Thus, an effective management of the ever-increasing volume of online knowledge documents is essential to organizational knowledge sharing. Reply-semantic relationships between knowledge documents may exist either explicitly or implicitly. Such reply-semantic relationships between knowledge documents, once discovered or identified, would facilitate subsequent knowledge access by providing a novel and more semantic retrieval mechanism. In this study, we propose a preliminary taxonomy of reply-semantic relationships for documents organized in reply-replied structures and develop a SEmantic Enrichment between Knowledge documents (SEEK) technique for automatically annotating reply-semantic relationships between reply-pair documents. Based on the content-based text categorization techniques and genre classification techniques, we propose and evaluate different feature-set models, combinations of keyword features, POS statistics features, and/or given/new information (GI/NI) features. Our empirical evaluation results show that the proposed SEEK technique can achieve a satisfactory classification accuracy. Furthermore, use of keyword and GI/NI features by the proposed SEEK technique resulted in the best classification accuracy for the Answer/Comment classification task. On the other hand, the use of keyword features only can best differentiate Explanation and Instruction relationships.