The Research On Web-based Spatial Ontology Construction Method

Although ontology engineering tools have matured over the last decade, manual spatial ontology acquisition remains a time-consuming, expensive, highly skilled, and sometimes cumbersome task that can easily result in geography knowledge acquisition bottleneck. These problems resemble those that knowledge engineers have dealt with over the last two decades as they worked on knowledge acquisition methodologies or workbenches for defining knowledge bases. The integration of knowledge acquisition with machine learning techniques proved beneficial for geography knowledge acquisition. How to use knowledge acquisition techniques to reduce the overhead in spatial ontology construction is called spatial ontology learning techniques. Spatial ontology learning from existing knowledge sources to obtain geography knowledge in order to (semi) automatic construction or renovation of spatial ontology. Extraction of spatial ontology from existing data on the Web can be supported by ontology learning techniques.As the Internet, one of the most important applications, Web provides a convenient mechanism for document publishing and access, and gradually became a gathering place for all kinds of information resources. As the text are the Web's most abundant resources, Web-based ontology learning technology research focused on the ontology acquisition from free text. Free text based on certain Sentences methods to express special meaning, so the knowledge engineer can understand the meaning based on some background knowledge. However, the lack of a certain structure, for machines that can automatically understand the text and extracted from the knowledge required, you must use natural language process technology to its pretreatment, then, statistics, machine learning and other means can be used to acquire knowledge. Web-based ontology learning methods typically include terminology extraction, semantic interpretation, and create domain ontology, as well as Web-based spatial ontology learning includes those three aspects. In the past spatial ontology construction was from scratch by hand, such as the construction of spatial ontology according to different application needs,the formalized definition for ontology conception, relationship and axiom, the construction of ontology using all kinds of ontology tools and the deduction for those constructions, and the research on ontology application (for example:the ontology-based spatial search engine). Whereas little research has been conducted in constructing spatial ontology from extract term on the web and disambiguating its semantic statement in spatial term learning. This paper emphasis on the theory and technique of semantic interpretation which bases on natural language understanding and do further research on spatial concept and spatial ontology construction. To make a deep understanding about spatial concept, some theories are provided and methodology is discussed. Three innovation points of this paper list here:1. Systematic research and analysis on semantic of vocabulary is provided through comparing natural language and spatial information and a meticulous analysis on spatial concept instance included in WordNet is experimented also.2. Detailed measurements of disambiguation are provided. The base of disambiguation is semantic explanation. Based on the introduction of thematic role system theory, selective constraints and disambiguation of statistic method are used to get the aim.3. A learning model of spatial ontology is provided which consists of web document arrangement, extracting of spatial term, spatial concept learning, spatial relationship learning, to extract spatial ontology from web pages automatically.