Knowledge-oriented modeling for influencing factors of battle damage in military industrial logistics:An integrated method

Modeling influencing factors of battle damage is one of essential works in implementing military industrial logistics simulation to explore battle damage laws knowledge.However,one of key challenges in designing the simulation system could be how to reasonably determine simulation model input and build a bridge to link battle damage model and battle damage laws knowledge.In this paper,we propose a novel knowledge-oriented modeling method for influencing factors of battle damage in military industrial logistics,integrating conceptual analysis,conceptual modeling,quantitative modeling and simulation implementation.We conceptualize influencing factors of battle damage by using the principle of hierarchical decomposition,thus classifying the related battle damage knowledge logically.Then,we construct the conceptual model of influencing factors of battle damage by using Entity-Relations hip approach,thus describing their interactions reasonably.Subsequently,we extract the important influencing factors by using social network analysis,thus evaluating their importance quantitatively and further clarifying the elements of simulation.Finally,we develop an agent-based military industry logistics simulation system by taking the modeling results on influencing factors of battle damage as simulation model input,and obtain simulation model output,i.e.,new battle damage laws knowledge,thus verifying feasibility and effectiveness of the proposed method.The results show that this method can be used to support human decision-making and action.

Entity Relationship Explanation via Conceptualization

Finding an attribute to explain the relationships between a given pair of entities is valuable in many applications.However,many direct solutions fail,owing to its low precision caused by heavy dependence on text and low recall by evidence scarcity.Thus,we propose a generalization-and-inference framework and implement it to build a system:entity-relationship finder(ERF).Our main idea is conceptualizing entity pairs into proper concept pairs,as intermediate random variables to form the explanation.Although entity conceptualization has been studied,it has new challenges of collective optimization for multiple relationship instances,joint optimization for both entities,and aggregation of diluted observations into the head concepts defining the relationship.We propose conceptualization solutions and validate them as well as the framework with extensive experiments.

KnowID:An Architecture for Efficient Knowledge-Driven Information and Data Access

Modern information systems require the orchestration of ontologies,conceptual data modeling techniques,and efficient data management so as to provide a means for better informed decision-making and to keep up with new requirements in organizational needs.A major question in delivering such systems,is which components to design and put together to make up the required“knowledge to data”pipeline,as each component and process has trade-offs.In this paper,we introduce a new knowledge-to-data architecture,KnowID.It pulls together both recently proposed components and we add novel transformation rules between Enhanced Entity-Relationship(EER)and the Abstract Relational Model to complete the pipeline.KnowID’s main distinctive architectural features,compared to other ontology-based data access approaches,are that runtime use can avail of the closed world assumption commonly used in information systems and of full SQL augmented with path queries.