Privacy-Preserving Content-Aware Search Based on Two-Level Index

Nowadays,cloud computing is used more and more widely,more and more people prefer to using cloud server to store data.So,how to encrypt the data efficiently is an important problem.The search efficiency of existed search schemes decreases as the index increases.For solving this problem,we build the two-level index.Simultaneously,for improving the semantic information,the central word expansion is combined.The purpose of privacy-preserving content-aware search by using the two-level index(CKESS)is that the first matching is performed by using the extended central words,then calculate the similarity between the trapdoor and the secondary index,finally return the results in turn.Through experiments and analysis,it is proved that our proposed schemes can resist multiple threat models and the schemes are secure and efficient.

Survey of Knowledge Graph Approaches and Applications

With the advent of the era of big data,knowledge engineering has received extensive attention.How to extract useful knowledge from massive data is the key to big data analysis.Knowledge graph technology is an important part of artificial intelligence,which provides a method to extract structured knowledge from massive texts and images,and has broad application prospects.The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search,intelligent question answering and personalized recommendation.Although knowledge graph has been applied to various systems,the basic theory and application technology still need further research.On the basis of comprehensively expounding the definition and architecture of knowledge graph,this paper reviews the key technologies of knowledge graph construction,including the research progress of four core technologies such as knowledge extraction technology,knowledge representation technology,knowledge fusion technology and knowledge reasoning technology,as well as some typical applications.Finally,the future development direction and challenges of the knowledge graph are prospected.

Semantic and secure search over encrypted outsourcing cloud based on BERT

Searchable encryption provides an effective way for data security and privacy in cloud storage.Users can retrieve encrypted data in the cloud under the premise of protecting their own data security and privacy.However,most of the current content-based retrieval schemes do not contain enough semantic information of the article and cannot fully reflect the semantic information of the text.In this paper,we propose two secure and semantic retrieval schemes based on BERT(bidirectional encoder representations from transformers)named SSRB-1,SSRB-2.By training the documents with BERT,the keyword vector is generated to contain more semantic information of the documents,which improves the accuracy of retrieval and makes the retrieval result more consistent with the user’s intention.Finally,through testing on real data sets,it is shown that both of our solutions are feasible and effective.

Query Intent Disambiguation of Keyword-Based Semantic Entity Search in Dataspaces

Keyword query has attracted much research attention due to its simplicity and wide applications. The inherent ambiguity of keyword query is prone to unsatisfied query results. Moreover some existing techniques on Web query, keyword query in relational databases and XML databases cannot be completely applied to keyword query in dataspaces. So we propose KeymanticES, a novel keyword-based semantic entity search mechanism in dataspaces which combines both keyword query and semantic query features. And we focus on query intent disambiguation problem and propose a novel three-step approach to resolve it. Extensive experimental results show the effectiveness and correctness of our proposed approach.

Towards intelligent geospatial data discovery:a machine learning framework for search ranking

Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension(e.g.popularity and release date).This approach largely fails to take account of users’multidimensional preferences for geospatial data,and hence may likely result in a less than optimal user experience in discovering the most applicable dataset.This study reports a machine learning framework to address the ranking challenge,the fundamental obstacle in geospatial data discovery,by(1)identifying a number of ranking features of geospatial data to represent users’multidimensional preferences by considering semantics,user behavior,spatial similarity,and static dataset metadata attributes;(2)applying a machine learning method to automatically learn a ranking function;and(3)proposing a system architecture to combine existing search-oriented open source software,semantic knowledge base,ranking feature extraction,and machine learning algorithm.Results show that the machine learning approach outperforms other methods,in terms of both precision at K and normalized discounted cumulative gain.As an early attempt of utilizing machine learning to improve the search ranking in the geospatial domain,we expect this work to set an example for further research and open the door towards intelligent geospatial data discovery.

Intelligent Development Environment and Software Knowledge Graph

Software intelligent development has become one of the most important research trends in software engineering. In this paper, we put forward two key concepts -- intelligent development environment （IntelliDE） and software knowledge graph -- for the first time. IntelliDE is an ecosystem in which software big data are aggregated, mined and analyzed to provide intelligent assistance in the life cycle of software development. We present its architecture and discuss its key research issues and challenges. Software knowledge graph is a software knowledge representation and management framework, which plays an important role in IntelliDE. We study its concept and introduce some concrete details and examples to show how it could be constructed and leveraged.

Distributed geospatial information processing: sharing distributed geospatial resources to support Digital Earth

This paper introduces a new concept,distributed geospatial information processing(DGIP),which refers to the process of geospatial information residing on computers geographically dispersed and connected through computer networks,and the contribution of DGIP to Digital Earth(DE).The DGIP plays a critical role in integrating the widely distributed geospatial resources to support the DE envisioned to utilise a wide variety of information.This paper addresses this role from three different aspects:1)sharing Earth data,information,and services through geospatial interoperability supported by standardisation of contents and interfaces;2)sharing computing and software resources through a GeoCyberinfrastructure supported by DGIP middleware;and 3)sharing knowledge within and across domains through ontology and semantic searches.Observing the long-term process for the research and development of an operational DE,we discuss and expect some practical contributions of the DGIP to the DE.

Type-2 fuzzy description logic

Description logics （DLs） are widely employed in recent semantic web application systems. However, classical description logics are limited when dealing with imprecise concepts and roles, thus providing the motivation for this work. In this paper, we present a type-2 fuzzy attributive concept language with complements （ALC） and provide its knowledge representation and reasoning algorithms. We also propose type-2 fuzzy web ontology language （OWL） to build a fuzzy ontology based on type- 2 fuzzy ALC and analyze the soundness, completeness, and complexity of the reasoning algorithms. Compared to type-1 fuzzy ALC, type-2 fuzzy ALC can describe imprecise knowledge more meticulously by using the membership degree interval. We implement a semantic search engine based on type-2 fuzzy ALC and carry out experiments on real data to test its performance. The results show that the type-2 fuzzy ALC can improve the precision and increase the number of relevant hits for imprecise information searches.

TDRM:A Peer-to-Peer-Based Taxonomy Data Ring Model

Peer-to-peer(P2P) networks are widely used due to their good scalability and robustness.This paper considers the characteristics of data sources which use some taxonomy hierarchies to classify and organize their data objects,combines P2P techniques,and proposes a P2P based taxonomy data ring model(TDRM).The model makes full use of the semantic information contained in taxonomy hierarchies,places the data objects having similar semantics together,and organizes them into one dimensional ring structure.Super-peers dynamically join the ring according to the requirement.The routing connections among super-peers are created,which are similar to Chord ring,thus a semantics based structured super-peer network is formed.Experiments show that the model has good scalability and search efficiency.