Multi-Modal Military Event Extraction Based on Knowledge Fusion

Event extraction stands as a significant endeavor within the realm of information extraction,aspiring to automatically extract structured event information from vast volumes of unstructured text.Extracting event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the data.Although researchers have proposed various methods to accomplish this task,most existing event extraction models cannot address these challenges because they are only applicable to text scenarios.To solve the above issues,this paper proposes a multi-modal event extraction method based on knowledge fusion.Specifically,for event-type recognition,we use a meticulous pipeline approach that integrates multiple pre-trained models.This approach enables a more comprehensive capture of the multidimensional event semantic features present in military texts,thereby enhancing the interconnectedness of information between trigger words and events.For event element extraction,we propose a method for constructing a priori templates that combine event types with corresponding trigger words.This approach facilitates the acquisition of fine-grained input samples containing event trigger words,thus enabling the model to understand the semantic relationships between elements in greater depth.Furthermore,a fusion method for spatial mapping of textual event elements and image elements is proposed to reduce the category number overload and effectively achieve multi-modal knowledge fusion.The experimental results based on the CCKS 2022 dataset show that our method has achieved competitive results,with a comprehensive evaluation value F1-score of 53.4%for the model.These results validate the effectiveness of our method in extracting event elements from multi-modal data.

Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

Cooperative planning ofmulti-agent systems based on task-oriented knowledge fusion with graph neural networks

Cooperative planning is one of the critical problems in the field of multi-agent system gaming.This work focuses on cooperative planning when each agent has only a local observation range and local communication.We propose a novel cooperative planning architecture that combines a graph neural network with a task-oriented knowledge fusion sampling method.Two main contributions of this paper are based on the comparisons with previous work:(1)we realize feasible and dynamic adjacent information fusion using GraphSAGE(i.e.,Graph SAmple and aggreGatE),which is the first time this method has been used to deal with the cooperative planning problem,and(2)a task-oriented sampling method is proposed to aggregate the available knowledge from a particular orientation,to obtain an effective and stable training process in our model.Experimental results demonstrate the good performance of our proposed method.

Knowledge-enriched joint-learning model for implicit emotion cause extraction

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention recently.However,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application scenarios.The lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local context.Moreover,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event information.To address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different clauses.Based on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is constructed.The authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause events.Experiments on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

Physics-data coupling-driven method to predict the penetration depth into concrete targets

The projectile penetration process into concrete target is a nonlinear complex problem.With the increase ofexperiment data,the data-driven paradigm has exhibited a new feasible method to solve such complex prob-lem.However,due to poor quality of experimental data,the traditional machine learning(ML)methods,whichare driven only by experimental data,have poor generalization capabilities and limited prediction accuracy.Therefore,this study intends to exhibit a ML method fusing the prior knowledge with experiment data.The newML method can constrain the fitting to experimental data,improve the generalization ability and the predic-tion accuracy.Experimental results show that integrating domain prior knowledge can effectively improve theperformance of the prediction model for penetration depth into concrete targets.

Knowledge Graph Extension Based on Crowdsourcing in Textile and Clothing Field

Generally,knowledge extraction technology is used to obtain nodes and relationships of unstructured data and structured data,and then the data fuse with the original knowledge graph to achieve the extension of the knowledge graph.Because the concepts and knowledge structures expressed on the Internet have problems of multi-source heterogeneity and low accuracy,it is usually difficult to achieve a good effect simply by using knowledge extraction technology.Considering that domain knowledge is highly dependent on the relevant expert knowledge,the method of this paper try to expand the domain knowledge through the crowdsourcing method.The method split the domain knowledge system into subgraph of knowledge according to corresponding concept,form subtasks with moderate granularity,and use the crowdsourcing technology for the acquisition and integration of knowledge subgraph to improve the knowledge system.

Relation-Aware Entity Matching Using Sentence-BERT

A key aspect of Knowledge fusion is Entity Matching.The objective of this study was to investigate how to identify heterogeneous expressions of the same real-world entity.In recent years,some representative works have used deep learning methods for entity matching,and these methods have achieved good results.However,the common limitation of these methods is that they assume that different attribute columns of the same entity are independent,and inputting the model in the form of paired entity records will cause repeated calculations.In fact,there are often potential relations between different attribute columns of different entities.These relations can help us improve the effect of entity matching,and can perform feature extraction on a single entity record to avoid repeated calculations.To use attribute relations to assist entity matching,this paper proposes the Relation-aware Entity Matching method,which embeds attribute relations into the original entity description to form sentences,so that entity matching is transformed into a sentence-level similarity determination task,based on Sentence-BERT completes sentence similarity calculation.We have conducted experiments on structured,dirty,and textual data,and compared them with baselines in recent years.Experimental results show that the use of relational embedding is helpful for entity matching on structured and dirty data.Our method has good results on most data sets for entity matching and reduces repeated calculations.

BOM-BASED DESIGN KNOWLEDGE REPRESENTATION AND REASONING FOR COLLABORATIVE PRODUCT DEVELOPMENT

Modem product development becomes increasingly collaborative and integrated, which raises the need for effectively and efficiently sharing and re-using design knowledge in a distributed and collaborative environment. To address this need, a framework is developed in this research to support design knowledge representation, retrieval, reasoning and fusion, which takes account of structural, functional and behavioral data, various design attributes and knowledge reasoning cases. Specifically, a multi-level knowledge representation based on the Base Object Model （BOM） is proposed to enable knowledge sharing using Web services technologies. On this basis, a multi-level knowledge reuse method is developed to support the retrieval, matching and assembly of knowledge records. Due to the tree structure of BOM, both depth-first and breadth-first searching strategies are employed in the retrieval algorithm while a novel measure is proposed to evaluate similarity. Moreover, a method based on the D-S evidence theory is developed to enable knowledge fusion and thus support effective decision-making. The framework has been implemented and integrated into an HLA-based simulation platform on which the development of a missile simulation example is conducted. It is demonstrated in the case study that the proposed framework and methods are useful and effective for design knowledge representation and reuse.

辅助工程师设计的知识融合设计方法:卫星舱布局设计(英文)

As a complex engineering problem,the satellite module layout design (SMLD) is difficult to resolve by using conventional computation-based approaches. The challenges stem from three aspects:computational complexity,engineering complexity,and engineering practicability. Engineers often finish successful satellite designs by way of their plenty of experience and wisdom,lessons learnt from the past practices,as well as the assistance of the advanced computational techniques. Enlightened by the ripe patterns,th...

Identifying the Core Competitive Intelligence Based on Enterprise Strategic Factors

Competitive intelligence(CI)is a key factor in helping business leaders gain and maintain competitive advantages.The emergence of big data and Web 2.0 has created new opportunities and more challenges for enterprises to effectively obtain CI.This paper attempts to explore a CI identification method based on strategic factors(SF).By filtering process before CI collection,the core CI,closely related to critical success factors and crisis inducement factors,are identified reliably and efficiently.Based on knowledge element model and multiattribute fusion method,emphasis is placed on the construction of a criterion function by which the SF thesaurus in achieving CI objectives is established.The advantages of this method lie not only in the capability of mining the core CI from massive data,but also in the foundation of efficient CI storage and analysis.This paper is of significance to make a thorough inquiry on CI obtaining and fusing methods of CI system in era of big data.Experiment results verified the feasibility and validity of this study.