Sentence,Phrase,and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions—A Trial Dataset

Purpose:This work aims to normalize the NLPCONTRIBUTIONS scheme(henceforward,NLPCONTRIBUTIONGRAPH)to structure,directly from article sentences,the contributions information in Natural Language Processing(NLP)scholarly articles via a two-stage annotation methodology:1)pilot stage-to define the scheme(described in prior work);and 2)adjudication stage-to normalize the graphing model(the focus of this paper).Design/methodology/approach:We re-annotate,a second time,the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising:contribution-centered sentences,phrases,and triple statements.To this end,specifically,care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme.Findings:The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences,4,702 contribution-information-centered phrases,and 2,980 surface-structured triples.The intra-annotation agreement between the first and second stages,in terms of F1-score,was 67.92%for sentences,41.82%for phrases,and 22.31%for triple statements indicating that with increased granularity of the information,the annotation decision variance is greater.Research limitations:NLPCONTRIBUTIONGRAPH has limited scope for structuring scholarly contributions compared with STEM(Science,Technology,Engineering,and Medicine)scholarly knowledge at large.Further,the annotation scheme in this work is designed by only an intra-annotator consensus-a single annotator first annotated the data to propose the initial scheme,following which,the same annotator reannotated the data to normalize the annotations in an adjudication stage.However,the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly articles.This would entail a larger initiative of enlisting multiple annotators to accommodate different worldviews into a“single”set of structures and relationships as the final scheme.Given that the initial scheme is first proposed and the complexity of the annotation task in the realistic timeframe,our intraannotation procedure is well-suited.Nevertheless,the model proposed in this work is presently limited since it does not incorporate multiple annotator worldviews.This is planned as future work to produce a robust model.Practical implications:We demonstrate NLPCONTRIBUTIONGRAPH data integrated into the Open Research Knowledge Graph(ORKG),a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge,as a viable aid to assist researchers in their day-to-day tasks.Originality/value:NLPCONTRIBUTIONGRAPH is a novel scheme to annotate research contributions from NLP articles and integrate them in a knowledge graph,which to the best of our knowledge does not exist in the community.Furthermore,our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.

Knowledge Representation and Semantic Inference of Process Based on Ontology and Semantic Web Rule Language

The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.

Equipment selection knowledge base system for industrial styrene process

Equipment selection for industrial process usually requires the extensive participation of industrial experts and technologists, which causes a serious waste of resources. This work presents an equipment selection knowledge base system for industrial styrene process(S-ESKBS) based on the ontology technology. This structure includes a low-level knowledge base and a top-level interactive application. As the core part of the S-ESKBS, the low-level knowledge base consists of the equipment selection ontology library, equipment selection rule set and Pellet inference engine. The top-level interactive application is implemented using S-ESKBS, including the parsing storage layer, inference query layer and client application layer. Case studies for the industrial styrene process equipment selection of an analytical column and an alkylation reactor are demonstrated to show the characteristics and implementability of the S-ESKBS.

A Knowledge Base System for Operation Optimization: Design and Implementation Practice for the Polyethylene Process

Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowl- edge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized.

A quantum‐like approach for text generation from knowledge graphs

Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation,such as knowledge graphs.Since all nodes are connected directly,node global representation encoding enables direct communication between two distant nodes while disregarding graph topology.Node local representation encoding,which captures the graph structure,considers the connections between nearby nodes but misses out onlong‐range relations.A quantum‐like approach to learning bettercontextualised node embeddings is proposed using a fusion model that combines both encoding strategies.Our methods significantly improve on two graph‐to‐text datasets compared to state‐of‐the‐art models in various experiments.

Application of graph neural network and feature information enhancement in relation inference of sparse knowledge graph

At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production environments,there are a large number of KGs with a small number of entities and relations,which are called sparse KGs.Limited by the performance of knowledge extraction methods or some other reasons(some common-sense information does not appear in the natural corpus),the relation between entities is often incomplete.To solve this problem,a method of the graph neural network and information enhancement is proposed.The improved method increases the mean reciprocal rank(MRR)and Hit@3 by 1.6%and 1.7%,respectively,when the sparsity of the FB15K-237 dataset is 10%.When the sparsity is 50%,the evaluation indexes MRR and Hit@10 are increased by 0.8%and 1.8%,respectively.

An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

CONSTRUCTION METHOD OF KNOWLEDGE MAP BASED ON DESIGN PROCESS

Due to the increasing amount and complexity of knowledge in product design, the know-ledge map based on design process is presented as a tool to reuse product design process, promote the product design knowledge sharing. The relationship between design task flow and knowledge flow is discussed; A knowledge organizing method based on design task decomposition and a visualization method to support the knowledge retrieving and sharing in product design are proposed. And a knowledge map system to manage the knowledge in product design process is built with Visual C＋＋ and SVG. Finally, a brief case study is provided to illustrate the construction and application of knowledge map in fuel pump design.

The State of Knowledge on Sustainable Chemical Process Systems

Sustainable engineering becomes a fast growing field of research and education.It aims at designing and operating systems of various scales such that they can use energy and resources in a sustainablemanner.Needless to say,this is one of the most challenging engineering problem types that needs scientists,researchers,engineers,and practitioners to collaboratively work for solutions.

Research on Explicit and Tacit Knowledge Interaction in Software Process Improvement Project

This research develops a knowledge model for Software Process Improvement (SPI) project based on knowledge creation theory and its twenty-four measurement items, and proposes two hypothesizes about the interaction of explicit knowledge and tacit knowledge in SPI. Eleven factors are extracted through statistical analysis. Three knowledge-creation practices for capturing tacit knowledge contribute greatly to SPI, which are communication among members, crossover collaboration in practical work and pair programming. Two knowledge-creation practices for capturing explicit knowledge have significant positive impact on SPI, which are integrating project document and on-the-job training. Ultimately, suggestions for improvement are put forward, that is, encouraging communication among staff and integrating documents in real time, and future research is also illustrated.

Crossing the Growth Threshold： Service-Based Economy, Knowledge Process and Reshaping of Efficiency Model

The transition from middle-income to high-income stage is fraught with risks of growth divergence. Economic transition is clouded by the following possibilities： （1）falling share of industrial seetor through industrial depression and weakening growth momentum caused by the large urbanization costs; （2） the subordination of service sector as a result of nearly irreversibly industrial professional, which falters the process of service sector transition and upgrading,＂ （3） inefficient knowledge production allocation and human capital upgrade due to the absence of incentivized compensation of knowledge consumption. We suggest that a country should reshape its efficiency model by upgrading knowledge factor and human capital as the pre-requisite. Given the dilemmas of transition, China should take the faetorization trend of service sector and reshape efficiency model through institutional reform, ensuring that service sector will develop in tandem with industrial sector.

A Knowledge-reuse Based Intelligent Reasoning Model for Worsted Process Optimization

The textile process planning is a knowledge reuse process in nature, which depends on the expert’s knowledge and experience. It seems to be very difficult to build up an integral mathematical model to optimize hundreds of the processing parameters. In fact, the existing process cases which were recorded to ensure the ability to trace production steps can also be used to optimize the process itself. This paper presents a novel knowledge-reuse based hybrid intelligent reasoning model (HIRM) for worsted process optimization. The model architecture and reasoning mechanism are respectively described. An applied case with HIRM is given to demonstrate that the best process decision can be made, and important processing parameters such as for raw material optimized.

Research on Knowledge Transfer Influencing Factors in Software Process Improvement

Knowledge transfer model of software process improvement (SPI) and the conceptual framework of influencing factors are established. The model includes five elements which are knowledge of transfer, sources of knowledge, recipients of knowledge, relationship of transfer parties, and the environment of transfer. The conceptual framework includes ten key factors which are ambiguity, systematism, transfer willingness, capacity of impartation, capacity of absorption, incen-tive mechanism, culture, technical support, trust and knowledge distance. The research hypothesis is put forward. Em-pirical study concludes that the trust relationship among SPI staffs has the greatest influence on knowledge transfer, and organizational incentive mechanism can produce positive effect to knowledge transfer of SPI. Finally, some sug-gestions are put forward to improve the knowledge transfer of SPI: establishing a rational incentive mechanism, exe-cuting some necessary training to transfer parties and using software benchmarking.

Development of Conceptual Framework for Knowledge Management Process

Knowledge has become one of the most important driving forces for business success. Organizations are becoming more knowledge intensive. Many firms in the global market are aware of this, and they try to explore the field of knowledge management （KM） in order to improve and sustain their competitiveness. Knowledge has always been the central in the functioning of society. However, in today＇s ＂knowledge economy＂, organizations are increasingly aware of the need for a ＂knowledge focus＂ in their organizational strategies as they respond to changes in the environment. The aim of this paper is to describe the theoretical concepts and approaches of KM process that could be implemented in organizations by reviewing KM process theories and present suggestions for what a general process should include based on analysis of various models presented in KM. The main emphasis is laid upon the concept of goal definition review, validation, and knowledge training processes in order to make sure that KM process initiative will deliver competitive advantage to the organization.

Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

Entrepreneurial Orientation,Knowledge creation process and New Product Development Performance

This paper analyzed the relationship between entrepreneurial orientation and new product development perlormance based on the perspective of knowledge creation process. Through a questionnaire survey, we found that entrepreneurial orientation is positively related to new product performance, and knowledge creation process plays a mediating role in this relationship. This article examines the role of entrepreneurial orientation on new product innovation performance in Chinese situations, and it is the first time to check the intermediary functions on each dimension of knowledge test between entrepreneurial orientation and new product development performance.

Integration of Knowledge Processes into Management Systems

Understanding definitions and differences between the processes,knowledge processes and business processes is the first step of the integration of knowledge processes into management systems of an organization.The next step is to understand throughout the company why the processes should be introduced and continuously maintained.The knowledge is one of the most valuable assets of the company,relevant part of the intellectual capital.The management of the knowledge and its lifecycle can give a market advantage for the organization.In the nuclear industry this is the vital requirement to maintain the safe and reliable operation of a nuclear facility,or radiation safety activates.Those companies who already implemented an integrated management system were following international standards,or good practices(like ISO 9001,EFQM,Standard Nuclear Performance Model developed by Nuclear Energy Institute(NEI)and others).This article focuses on nuclear industry organizations,approaches and methods,and how to integrate the knowledge processes into management system.This is the last step of the knowledge management implementation in an organization.When it was done,we can say that the knowledge processes are embedded into organization’s day-to-day life and the knowledge managed in the organization as all other resources.

Process-oriented knowledge-sharing platform for chemical engineering design projects

A process-oriented knowledge-sharing platform is studied to improve knowledge sharing and project management of chemical engineering design enterprises. First, problems and characteristics of knowledge sharing in multi-projects of chemical engineering design are analyzed. Then based on theories of project management, process management, and knowledge management, a process-oriented knowledge-sharing platform is proposed. The platform has three characteristics: knowledge is divided into professional knowledge and project management knowledge; knowledge sharing is integrated with the project process, which makes knowledge sharing a necessary part of the project process and ensures the quantity of knowledge shared; the platform provides quantitative measurements of incentive mechanisms for knowledge providers and users which ensures the quality of knowledge shared. This knowledge-sharing platform uses two knowledge management tools, a knowledge map and a knowledge base, to support the platform.

Neuro-Knowledge-Based Expert System (NKBES) for Optimal Scheming of Die Casting Process

We develop a neuro-knowledge-based expert system (NKBES) frame in this work. The system mainly concerns with decision of gating system and die casting machine based on a neuro-inference engine launched under the MATLAB software environment. For enhancement of reasoning agility, an error back-propagation neural network was applied. A rapidly convergent adaptive learning rate (ALR) and a momentum-based error back-propagation algorithm was used to conduct neuro-reasoning. The working effect of the system was compared to a conventional expert system that is based on a two-way (forward and backward) chaining inference mechanism. As the reference, the present paper provided the neural networks sum-squared error (S5E) and ALR vs iterative epoch curves of process planning case mentioned above. The study suggests that the neuro-modeling optimization application to die casting process design has good feasibility, and based on that a novel and effective intelligent expert system can be launched at low cost.