IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

GNN-based temporal knowledge reasoning for UAV mission planning systems

Unmanned aerial vehicles(UAVs)are increasingly applied in various mission scenarios for their versatility,scalability and cost-effectiveness.In UAV mission planning systems(UMPSs),an efficient mission planning strategy is essential to meet the requirements of UAV missions.However,rapidly changing environments and unforeseen threats pose challenges to UMPSs,making efficient mission planning difficult.To address these challenges,knowledge graph technology can be utilized to manage the complex relations and constraints among UAVs,missions,and environments.This paper investigates knowledge graph application in UMPSs,exploring its modeling,representation,and storage concepts and methodologies.Subsequently,the construction of a specialized knowledge graph for UMPS is detailed.Furthermore,the paper delves into knowledge reasoning within UMPSs,emphasizing its significance in timely updates in the dynamic environment.A graph neural network(GNN)-based approach is proposed for knowledge reasoning,leveraging GNNs to capture structural information and accurately predict missing entities or relations in the knowledge graph.For relation reasoning,path information is also incorporated to improve the accuracy of inference.To account for the temporal dynamics of the environment in UMPS,the influence of timestamps is captured through the attention mechanism.The effectiveness and applicability of the proposed knowledge reasoning method are verified via simulations.

A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis cell condition

Root cause analysis （RCA） of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accurate RCA of abnormal aluminum electrolysis cell condition is the precondition of improving current efficiency. RCA of abnormal condition is a complex work of multi-source knowledge fusion, which is difficult to ensure the RCA accuracy of abnormal cell condition because of dwindling and frequent flow of experienced technicians. In view of this, a method based on Fuzzy- Bayesian network to construct multi-source knowledge solidification reasoning model is proposed. The method can effectively fuse and solidify the knowledge, which is used to analyze the cause of abnormal condition by technicians providing a clear and intuitive framework to this complex task, and also achieve the result of root cause automatically. The proposed method was verified under 20 sets of abnormal cell conditions, and implements root cause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian diagnosis reasoning. The accuracy of the test results is up to 95%, which shows that the knowledge reasoning feasibility for RCA of aluminum electrolysis cell.

New Knowledge Network Evaluation Method for Design Rationale Management

Current design rationale （DR） systems have not demonstrated the value of the approach in practice since little attention is put to the evaluation method of DR knowledge. To systematize knowledge management process for future computer-aided DR applications, a prerequisite is to provide the measure for the DR knowledge. In this paper, a new knowledge network evaluation method for DR management is presented. The method characterizes the DR knowledge value from four perspectives, namely, the design rationale structure scale, association knowledge and reasoning ability, degree of design justification support and degree of knowledge representation conciseness. The DR knowledge comprehensive value is also measured by the proposed method. To validate the proposed method, different style of DR knowledge network and the performance of the proposed measure are discussed. The evaluation method has been applied in two realistic design cases and compared with the structural measures. The research proposes the DR knowledge evaluation method which can provide object metric and selection basis for the DR knowledge reuse during the product design process. In addition, the method is proved to be more effective guidance and support for the application and management of DR knowledge.

Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end,we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG development.Furthermore,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for MKG.In addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

Let Some Unforeseen Knowledge Emerge from Heterogeneous Documents

Data production and exchange on the Web grows at a frenetic speed. Such uncontrolled and exponential growth pushes for new researches in the area of information extraction as it is of great interest and can be obtained by processing data gathered from several heterogeneous sources. While some extracted facts can be correct at the origin, it is not possible to verify that correlations among the mare always true (e.g., they can relate to different points of time). We need systems smart enough to separate signal from noise and hence extract real value from this abundance of content accessible on the Web. In order to extract information from heterogeneous sources, we are involved into the entire process of identifying specific facts/events of interest. We propose a gluing architecture, driving the whole knowledge acquisition process, from data acquisition from external heterogeneous resources to their exploitation for RDF trip lification to support reasoning tasks. Once the extraction process is completed, a dedicated reasoner can infer new knowledge as a result of the reasoning process defined by the end user by means of specific inference rules over both extracted information and the background knowledge. The end user is supported in this context with an intelligent interface allowing to visualize either specific data/concepts, or all information inferred by applying deductive reasoning over a collection of data.

AInvR:Adaptive Learning Rewards for Knowledge Graph Reasoning Using Agent Trajectories

Multi-hop reasoning for incomplete Knowledge Graphs(KGs)demonstrates excellent interpretability with decent performance.Reinforcement Learning(RL)based approaches formulate multi-hop reasoning as a typical sequential decision problem.An intractable shortcoming of multi-hop reasoning with RL is that sparse reward signals make performance unstable.Current mainstream methods apply heuristic reward functions to counter this challenge.However,the inaccurate rewards caused by heuristic functions guide the agent to improper inference paths and unrelated object entities.To this end,we propose a novel adaptive Inverse Reinforcement Learning(IRL)framework for multi-hop reasoning,called AInvR.(1)To counter the missing and spurious paths,we replace the heuristic rule rewards with an adaptive rule reward learning mechanism based on agent’s inference trajectories;(2)to alleviate the impact of over-rewarded object entities misled by inaccurate reward shaping and rules,we propose an adaptive negative hit reward learning mechanism based on agent’s sampling strategy;(3)to further explore diverse paths and mitigate the influence of missing facts,we design a reward dropout mechanism to randomly mask and perturb reward parameters for the reward learning process.Experimental results on several benchmark knowledge graphs demonstrate that our method is more effective than existing multi-hop approaches.

DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

New approach to eliminate structural redundancy in case resource pools using α mutual information

Structural redundancy elimination in case resource pools （CRP） is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this paper proposes a novel approach to ensure redundancy elimination of a reasoning system in CRP. By using α entropy and mutual information, functional measures to eliminate redundancy of a system are developed with respect to a set of outputs. These measures help to distinguish both the optimal feature and the relations among the nodes in reasoning networks from the redundant ones with the elimination criterion. Based on the optimal feature and its harmonic weight, a model for knowledge reasoning in CRP （CRPKR） is built to complete the task of query matching, and the missing values are estimated with Bayesian networks. Moreover, the robustness of decisions is verified through parameter analyses. This approach is validated by the simulation with benchmark data sets using cloud SQL. Compared with several state-of-the-art techniques, the results show that the proposed approach has a good performance and boosts the robustness of decisions.

Geological Analogy Expert System of Oil-Generating Depressions

The geologic analogy expert system of oil-generating depressions (GAESOD) constructed on IBM 386 by using GCLISP language is a tool-type expert system for geologic analogy .GAESOD consists of eight parts: (1) illustrating module of system; (2) general controlling module; (3) knowledge base; (4 (reasoning module; (5 )data base; (6)explanation module; (7)gaining and managing module of knowledge; (8) managing module of data base .There are 36 known models of oil-generating depressions of the eastern part and the continental shelf of China in the knowledge base .Three values,such as resemblance coefficient ,certainty factor and fine-poor coefficient ,will be acquired if this system is applied to any two on-generating depressions .Finally,GAESOD are applied to the analysis of some data from Xichang depression ,Hepu basin and the conclusions from this system are consistent with the results from geologic experts.

A Survey of Commonsense Knowledge Acquisition

Collecting massive commonsense knowledge （CSK） for commonsense reasoning has been a long time standing challenge within artificial intelligence research. Numerous methods and systems for acquiring CSK have been developed to overcome the knowledge acquisition bottleneck. Although some specific commonsense reasoning tasks have been presented to allow researchers to measure and compare the performance of their CSK systems, we compare them at a higher level from the following aspects： CSK acquisition task （what CSK is acquired from where）, technique used （how can CSK be acquired）, and CSK evaluation methods （how to evaluate the acquired CSK）. In this survey, we first present a categorization of CSK acquisition systems and the great challenges in the field. Then, we review and compare the CSK acquisition systems in detail. Finally, we conclude the current progress in this field and explore some promising future research issues.

The Intelligent CAI System for Chemistry Based on Automated Reasoning

A new type of intelligent CAI system for chemistry is developed inthis paPer based on automated reaJsoning with chemistry knowledge. The systemhas shown its ability to solve chemistry problems, to assist students and teachersin studies and instruction with the automated reasoning functions. Its open modeof the knowledge base and its unique style of the illterface between the system andhuman provide more opportunities for the users to acquire living knowledge throughactive participation. The automated reasoning based on basic chemistry knowledgealso opened a new approach to the information storage and management of the ICAIsystem for sciences.

Computing a ground appropriateness index for route selection in permafrost regions

The reasonable calculation of ground appropriateness index in permafrost region is the precondition of highway route design in permafrost region. The theory of knowledge base and fuzzy mathematics are applied, and the damage effect of permafrost is considered in the paper. Based on the idea of protecting permafrost the calculation method of ground appro- priateness index is put forward. Firstly, based on the actual environment conditions, the paper determines the factors affecting the road layout in permafrost areas by qualitative and quantitative analysis, including the annual slope, the average annual ground temperature of permafrost, the amount of ice in frozen soil, and the interference engineering. Secondly, based on the knowledge base theory and the use of Delphi method, the paper establishes the knowledge base, the rule base of the permafrost region and inference mechanism. The method of selecting the road in permafrost region is completed and realized by using the software platform. Thirdly, taking the Tuotuo River to Kaixin Mountain section of permafrost region as an example, the application of the method is studied by using an ArcGIS platform. Results show that the route plan determined by the method of selecting the road in perma-frost region can avoid the high temperature and high ice content area, conform the terrain changes and evade the heat disturbance among the existing projects. A reasonable route plan can be achieved, and it can provide the basis for the next engineering construction.

Penalty Formulations and Trap-Avoidance Strategies for Solving Hard Satisfiability Problems

In this paper we study the solution of SAT problems formulated as discretedecision and discrete constrained optimization problems. Constrained formulations are better thantraditional unconstrained formulations because violated constraints may provide additional forces tolead a search towards a satisfiable assignment. We summarize the theory of extended saddle pointsin penalty formulations for solving discrete constrained optimization problems and the associateddiscrete penalty method (DPM). We then examine various formulations of the objective function,choices of neighborhood in DPM, strategies for updating penalties, and heuristics for avoidingtraps. Experimental evaluations on hard benchmark instances pinpoint that traps contributesignificantly to the inefficiency of DPM and force a trajectory to repeatedly visit the same set ofor nearby points in the original variable space. To address this issue, we propose and study twotrap-avoidance strategies. The first strategy adds extra penalties on unsatisfied clauses inside atrap, leading to very large penalties for unsatisfied clauses that are trapped more often and makingthese clauses more likely to be satisfied in the future. The second strategy stores information onpoints visited before, whether inside traps or not, and avoids visiting points that are close topoints visited before. It can be implemented by modifying the penalty function in such a way that,if a trajectory gets close to points visited before, an extra penalty will take effect and force thetrajectory to a new region. It specializes to the first strategy because traps are special cases ofpoints visited before. Finally, we show experimental results on evaluating benchmarks in the DIMACSand SATLIB archives and compare our results with existing results on GSAT, WalkSAT, LSDL, andGrasp. The results demonstrate that DPM with trap avoidance is robust as well as effective forsolving hard SAT problems.