Lixu Shao

Bidirectional value driven design between economical planning and technical implementation based on data graph, information graph and knowledge graph

Value-Driven Design enables rational decisions to be made in terms of the optimum business and technical solution at every level of engineering design by employing economics in decision making. In order to maximize the business profitability, we propose to bridge bidirectional value driven design between economic planning and technology implementation on the basis of the data graph, information graph and knowledge graph. We use data graph, information graph and knowledge graph to analyze problems that have negative impact on activities of software development including requirement analysis, summary design and detail design. We propose to improve system reliability and robustness by managing data and information reuse, redundancy as well as structure.

Specifying architecture of knowledge graph with data graph, information graph, knowledge graph and wisdom graph

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. Knowledge graph has become a powerful tool to represent knowledge in the form of a labelled directed graph and to give semantics to textual information. A knowledge graph is a graph constructed by representing each item, entity and user as nodes, and linking those nodes that interact with each other via edges. Knowledge graph has abundant natural semantics and can contain various and more complete information. Its expression mechanism is close to natural language. However, we still lack a unified definition and standard expression form of knowledge graph. We propose to clarify the expression of knowledge graph as a whole. We clarify the architecture of knowledge graph from data, information, knowledge, and wisdom aspects respectively. We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

Answering Who/When, What, How, Why through Constructing Data Graph, Information Graph, Knowledge Graph and Wisdom Graph.

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. Natural language questions are the most intuitive way of formulating an information need. People can formulate questions to express their information needs. Natural language questions as a query language present an ideal compromise between keyword and structured querying. Questions can be used to express complex information needs that cannot be expressed as keywords without a significant loss in structure and semantics. Knowledge graph has abundant natural semantics and can contain various and more complete information. Its expression mechanism is closer to natural language. We propose to clarify the expression of knowledge graph as a whole. We use knowledge graph to solve the Five Ws problems respectively which are guided by interrogative words such as who/when, what, how and why. We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph. 1

Learning Planning and Recommendation Based on an Adaptive Architecture on Data Graph, Information Graph and Knowledge Graph

With massive learning resources that contain data, information and knowledge on Internet, users are easy to get lost and confused in processing of learning. Automatic processing, automatic synthesis, and automatic analysis of natural language, such as the original representation of the resources of these data, information and knowledge, have become a huge challenge. We propose a three-layer architecture composing Data Graph, Information Graph and Knowledge Graph which can automatically abstract and adjust resources. This architecture recursively supports integration of empirical knowledge and efficient automatic semantic analysis of resource elements through frequency focused profiling on Data Graph and optimal search through abstraction on Information Graph and Knowledge Graph. Our proposed architecture is supported by the 5W (Who/When/Where, What and How) to interface users’ learning needs, learning processes, and learning objectives which can provide users with personalized learning service recommendation.

Towards Collaborative Typed Resources Manipulation in Health-Care Environments

Web service is a popular solution to integrate components when building a software system, or to allow communication between a system and third-party users, providing a flexible and reusable mechanism to access its functionalities. Due to differences in medical level and extremely uneven distribution of medical resources, medical information technology lacks unified planning and is not supported by digital health care system. Thus diagnosis resources of patients cannot be shared by each medical institution. Inspections usually repeat which not only increases the burden of hospitalizing and physical injuries, but also leads to the waste of medical resources. We propose a framework towards constructing and searching typed health resources in terms of data, information and knowledge through a hierarchy composing Data Graph, Information Graph and Knowledge Graph in order to improve performance in accessing and processing resources. We use cases to illustrate the mechanism of the framework.

Constructing Search as a Service Towards Non-deterministic and Not Validated Resource Environment with a Positive-Negative Strategy

Internet resources are non-deterministic, non-guaranteed and ultra-complex. We provide a progressive search approach towards problems with positive and negative tendencies aiming at improving the credibility of resources through multi times progressive searching. Meanwhile, we introduce Knowledge Graph as a resource process architecture to organize resources on the network and analyze the tendency of searchers for retrieving information by semantic analysis. We calculate entropy of resources according to searching times and amount of items of each search to represent the reliability of resources with positive and negative tendencies. Resources with ambiguous tendency and false information will be eliminated during the process of progressive search and quality of searching results will be improved while avoiding dead loop of searching towards infinite and complex problems. We apply the searching strategy to a medical resource processing system that provides high precision medical resource retrieval service for medical workers to verify the feasibility of our approach.

A Pay as You Use Resource Security Provision Approach Based on Data Graph, Information Graph and Knowledge Graph.

With the development of data mining technology, lack of private resource protection has become a serious challenge. We propose to clarify the expression of Knowledge Graph in three layers including Data Graph, Information Graph and Knowledge Graph and illustrate the representation of Data Graph, Information Graph and Knowledge Graph respectively. We elaborate a pay as you use resource security provision approach based on Data Graph, Information Graph and Knowledge Graph in order to ensure that resources will not be used, tampered with, lost and destroyed in unauthorized situations.

An Investment Defined Transaction Processing Towards Temporal and Spatial Optimization with Collaborative Storage and Computation Adaptation

Transaction processing technology is the key technology of reporting information consistency and reliability, and determines whether web services can be applied to e-commerce. We propose an investment defined transaction processing approach towards temporal and spatial optimization with collaborative storage and computation adaptation approach aiming at satisfying requirements of different users according to their investment. We studies resource modelling, resource processing, processing optimization and resource management and puts forward a three-tier architecture consisting of Data Graph, Information Graph and Knowledge Graph.

Data, information, and knowledge-driven manipulation between strategical planning and technical implementation for wireless sensor network construction:

To provide guidance to and integrate directly with the information technology side implementation to achieve coherent data, information, and knowledge coordination and robust value-oriented adaptability for maximization of business profitability, we propose to leverage the ideology of service economics to achieve bidirectional computational bridging between business planning and technical implementation of ubiquitous services/XaaS in the application program interface economy for promoting the macro-service market, especially in global value chains. Enlightened by value-driven design, we propose a systemic formalization from value calculation to design quality measurement which binds the technical modification and variation on design artifacts with business value strategy through a framework of managed quality properties in a wireless sensor network–based service system creation process. In the process of building a service system, we use data, information, and knowledge flow to abstract various data, information, and knowledge manipulation and usage scenarios. We propose to improve system reliability and robustness by managing data and information reuse, redundancy as well as structure.

Enhancing UML Class Diagram Abstraction with Page Rank Algorithm and Relationship Abstraction Rules

Model-Driven Engineering (MDE) alleviates the cognitive complexity and effort through the refinement and abstraction of consecutive models. In MDE, models should accurately and completely accommodate the expected data, information and knowledge in requirement specification following a series of refinement and abstraction. Proper abstraction starting from Class Diagrams lays the foundation for effective reuse and efficient manipulation of contained data, information and knowledge. Most current model abstraction approaches assume the scenarios with interaction of stakeholders for providing the key entities and thereafter focus on the relationship abstraction. However few work is done on unguided abstraction where stakeholders don’t know the key entities. Towards resolving the abstraction covering both automatic locating of representative entities and abstracting of link among these entities in Class Diagrams, we proposed a combination of class rank algorithm which prioritizes classes and relationship abstraction rules which heuristically determine the representative semantics of relationships towards improving the efficiency and effectiveness of class abstraction.