KEYWORDS: Internet, Data mining, Data modeling, Data processing, Profiling, Mining, Analytical research, Target recognition, Social networks, Standards development
Due to the emergence of highly intelligent new cybercriminals in the Internet, traditional case investigation methods cannot analyze the massive amount of data efficiently and accurately. Therefore, we can use relevant technology of big data to conduct correlation analysis on the network identities of the people involved, describe the relationship between the characters, and study the overlapping communities based on the separation of multiple roles. Then, according to the features that the ontology of the users involved and the associated targets contains multiple roles, the high-value targets hidden in the case can be mined. By mining, analyzing, and modeling the big data involved in the case, and constructing a portrait of the relationship between personal identities, it is possible to give an early warning and judgment on potential criminal tendencies, and to carry out demonstration applications in the public security system to combat complex new cybercrimes.
The heterogeneity of geospatial information will persist for a long time. As the key to overcome the semantic heterogeneity, categories mapping has gained considerable attention. In previous studies, the existing geographic ontologies cannot support enough multi-semantic extension to conquer semantic heterogeneity effectively. When introduced to explore categories mapping, many semantic similarity measures encountered the problem of subjective weight setting and failed to make full use of the organizational structure information of categories. The rapid development of Artificial Intelligence (AI) and Natural Language Processing (NLP) bring new enlightenment to the semantic analysis and understanding in the geographic information field. Therefore, this paper proposes a new geographic categories mapping method based on ontology attribute characteristics learning, which utilizes ontology attributes and the classification hierarchy of geographic categories. Firstly, a basic semantic framework based on ontology attributes is defined to realize the semantic vectorization descriptions of geographic categories, by extracting semantic knowledge from definitions. Then, a new hierarchical coding method is proposed to describe the classification hierarchy of categories and identify the classification status of each category. After that, a self-learning mapping mechanism based on BP neural network is used to establish the non-linear relationship between ontology attribute eigenvectors and classification states, which can support categories mapping. Finally, some categories mappings are formed by this method to evaluate transition effects, and introduces the category differentiation degree to analyze the influence of classification structure on prediction accuracy. The preliminary results show the feasibility and reliability of the proposed model for automatic semantic mapping.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.