In order to meet the rapid positioning of 12315 complaints, aiming at the natural language expression of telephone complaints, a semantic retrieval framework is proposed which is based on natural language parsing and geographical names ontology reasoning. Among them, a search result ranking and recommended algorithms is proposed which is regarding both geo-name conceptual similarity and spatial geometry relation similarity. The experiments show that this method can assist the operator to quickly find location of 12,315 complaints, increased industry and commerce customer satisfaction.
Location-related data are playing an increasingly irreplaceable role in business, government and scientific research. At the same time, the amount and types of data are rapidly increasing. It is a challenge how to quickly find required information from this rapidly growing volume of data, as well as how to efficiently provide different levels of geospatial data to users. This paper puts forward a data-oriented access model for geographic information science data. First, we analyze the features of GIS data including traditional types such as vector and raster data and new types such as Volunteered Geographic Information (VGI). Taking into account these analyses, a classification scheme for geographic data is proposed and TRAFIE is introduced to describe the establishment of a multi-level model for geographic data. Based on this model, a multi-level, scalable access system for geospatial information is put forward. Users can select different levels of data according to their concrete application needs. Pull-based and push-based data access mechanisms based on this model are presented. A Service Oriented Architecture (SOA) was chosen for the data processing. The model of this study has been described by providing decision-making process of government departments with a simulation of fire disaster data collection. The use case shows this data model and the data provision system is flexible and has good adaptability.
In this paper a web information extraction method is presented which identifies a variety of thematic events utilizing the event knowledge framework derived from text training, and then further uses the syntactic analysis to extract the event key information. The method which combines the text semantic information and domain knowledge of the event makes the extraction of information people interested more accurate. In this paper, web based earthquake news extraction is taken as an example. The paper firstly briefs the overall approaches, and then details the key algorithm and experiments of seismic events extraction. Finally, this paper conducts accuracy analysis and evaluation experiments which demonstrate that the proposed method is a promising way of hot events mining.
This article explores Automatic generalization of Map Resident in digital environment that include creating neighboring network based on Delaunay triangulation, constructing minimum spanning tree from neighboring network, to determine the polygon grouping strategy based on minimum spanning tree, then explore polygon merging and removing algorithm. This method can retain important features of geographic features while maintaining a good capacity and readability, finally obtaining multi-scale maps.
With the development of Web services technology, the number of service increases rapidly, and it becomes a challenge
task that how to efficiently discovery the services that exactly match the user's requirements from the large scale of services
library. Many semantic Web services discovery technologies proposed by the recent literatures only focus on the
keyword-based or primary semantic based service's matching. This paper studies the rules and rule reasoning based service
matching algorithm in the background of large scale services library. Firstly, the formal descriptions of semantic
web services and service matching is presented. The services' matching are divided into four levels: Exact, Plugin, Subsume
and Fail and their formal descriptions are also presented. Then, the service matching is regarded as rule-based reasoning
issues. A set of match rules are firstly given and the related services set is retrieved from services ontology base
through rule-based reasoning, and their matching levels are determined by distinguishing the relationships between service's
I/O and user's request I/O. Finally, the experiment based on two services sets show that the proposed services
matching strategy can easily implement the smart service discovery and obtains the high service discovery efficiency in
comparison with the traditional global traversal strategy.