Since the search engine was first released in 1993, the development has never been slow down and various search engines emerged to vied for popularity. However, current traditional search engines like Google and Yahoo! are based on key words which lead to results impreciseness and information redundancy. A new search engine with semantic analysis can be the alternate solution in the future. It is more intelligent and informative, and provides better interaction with users.        This thesis discusses the detail on semantic search, explains advantages of semantic search over other key-word-based search and introduces how to integrate semantic analysis with common search engines. At the end of this thesis, there is an example of implementation of a simple semantic search engine.

The objective of this project is to improve the performance of the Gnutella peer-to-peer protocol (version 0.4) by introducing a semantic-social routing model and several categories of interest. The Gnutella protocol requires peers to broadcast messages to their neighbours when they search files. The message passing generates a lot of traffic in the network, which degrades the quality of service. We propose using social networks to optimize the speed of search and to improve the quality of service in a Gnutella based peer-to-peer environment. Each peer creates and updates a friends list from its past experience, for each category of interest. Once peers generate their friends lists, they use these lists to semantically route queries in the network. Search messages in a given category are mainly sent to friends who have been useful in the past in finding files in the same category. This helps to reduce the search time and to decrease the network traffic by minimizing the number of messages circulating in the system as compared to standard Gnutella. This project will demonstrate by simulating a peer-to-peer type of environment with the JADE multi-agent system platform that by learning other peers interests, building and exploiting their social networks (friends lists) to route queries semantically, peers can get more relevant resources faster and with less traffic generated, i.e. that the performance of the Gnutella system can be improved.

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This thesis proposes a link-based approach to identify supporting and opposing groups in a Weblog community. We formulate the interaction behavior as a graph. Bloggers involved in the discussion of one specific issue are formulated as vertices. Semantic orientation is used to construct possible opposite opinion links. Bloggers with opposite stances will form an opposite link. A max-cut algorithm is used latter to obtain the optimal approximation of supporting and opposing groups. The categorization results are compared between semantic orientation classifier and simple link-based categorization. The simple link-based categorization compares then with the enhancement of link-based categorization using hypergraph.

Text categorization to automatically assign documents into the appropriate pre-defined category or categories is essential to facilitating the retrieval of desired documents efficiently and effectively from a huge text depository, e.g., the world-wide web. Most techniques, however, suffer from the feature selection problem and the vocabulary mismatch problem. A few research works have addressed on text categorization via text summarization to reduce the size of documents, and consequently the number of features to consider, while some proposed using latent semantic indexing (LSI) to reveal the true meaning of a term via its association with other terms. Few works, however, have studied the joint effect of text summarization and the semantic dimension reduction technique in the literature. The objective of this research is thus to propose a practical approach, SBDR to deal with the above difficulties in text categorization tasks. Two experiments are conducted to validate our proposed approach. In the first experiment, the results show that text summarization does improve the performance in categorization. In addition, to construct important sentences, the association terms of both noun-noun and noun-verb pairs should be considered. Results of the second experiment indicate slight better performance with the approach of adopting LSI exclusively (i.e. no summarization) than that with SBDR (i.e. with summarization). Nonetheless, the minor accuracy reduction can be largely compensated for the computational time saved using LSI with text summarized. The feasibility of the SBDR approach is thus justified.

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Recently, sport video analysis has attracted lots of researcher¡¦s attention because of its entertainment applications and potential commercial benefits. Sport video analysis aims to identify what trigged the excitement of audiences. Previous methods rely mainly on video decomposition using domain specific knowledge. The study and development of suitable and efficient techniques for sport video analysis have been conducted extensively over the last decade. However, several longstanding challenges, such as semantic gap and commercial detection are still waiting to be solved. In this work, we consider using semantic analysis to adjacent pitch scenes which we called ¡§gap length.¡¨ Difference kinds of baseball games show its specific distribution for gap length, which depicts the potential significance of each baseball game.

Thesis (M.S.)--California Polytechnic State University, 2009. / Mode of access: Internet. Title from PDF title page; viewed on Jan. 6, 2010. Major professor: Dr. Michael Haungs. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Computer Science." "Aug 2009." Includes bibliographical references (p. 76-78).

Thesis (Ph. D.)--College of Computing, Georgia Institute of Technology, 2004. Directed by Ling Liu. / Vita. Includes bibliographical references (leaves 147-155).

Unstructured text contains a wealth of knowledge, however, it is in a form unsuitable for reasoning. Semantic interpretation is the task of processing natural language text to create or extend a coherent, formal knowledgebase able to reason and support question answering. This task involves entity, event and relation extraction, co-reference resolution, and inference. Many domains, from intelligence data to bioinformatics, would benefit by semantic interpretation. But traditional approaches to the subtasks typically require a large annotated corpus specific to a single domain and ontology. This dissertation describes an approach to rapidly train a semantic interpreter using a set of seed annotations and a large, unlabeled corpus. Our approach adapts methods from paraphrase acquisition and automatic thesaurus construction to extend seed syntactic to semantic mappings using an automatically gathered, domain specific, parallel corpus. During interpretation, the system uses joint probabilistic inference to select the most probable interpretation consistent with the background knowledge. We evaluate both the quality of the extended mappings as well as the performance of the semantic interpreter. / text