Is Knowledge Power?

Francis Bacon’s famous quote“knowledge is power,”has long been misunderstood,for his real intention was precisely to make humankind aware of the limitations of knowledge.His concept of potestas(power)is not about conquest,but about action,aiming to clarify the nature of knowledge,to get rid of the empty and shallow contemplation of antiquity,and thus to bring the spirit of the real world back to the earth,as Socrates did.Bacon emphasized the unity of knowledge and action while valuing action over knowledge.Nature in Bacon’s time was no longer sacred and was degraded to a poor substance that revealed its secrets after being tortured by scientific technology.As a result,natural teleology was completely abandoned.Bacon put man in increasing tension with nature,heralding Kant’s argument that human reason prescribed lawfulness to nature.But Bacon,after all,lived in an era not far from antiquity,so he agreed the limitations of knowledge and action and considered technology to be a labyrinth prone to divest one’s identity.Bacon thought that knowledge could be venom that made humankind swell,and the antidote was charity.Bacon’s quote is not so much an encouragement to take from nature as it is a way to learn from nature and to take a practical approach to happiness.

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.

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.

Indigenous Knowledge of Natural Resource Management and Population Control for Dong Ethnic Group in Guizhou Province

Dong ethnic people have rich indigenous knowledge in terms of their daily life and production, which plays an important role in the sustainable development of their village. This paper aims to understand traditional knowledge of Dong ethnic people in resource management and population control, including traditional resource management, traditional medicinal knowledge and village regulations in Zhanli Village in Southeast Guizhou Province. The research methods include key informant interview, group discussion, participant observation and secondary data collect- ing. The results show that Zhanli villagers try their best to utilize indigenous knowledge to manage the natural resources and keep the stable population to make themselves live in a sustainable way. Indigenous knowledge plays an important role in managing their limited natural resources and keeping the population stable under an excellent condition. Zhanli villagers employ indigenous knowledge to manage natural resources and use local herbs to control the population. Village regulation terms significantly influence villagers’ awareness in resource management and birth control. Women play the chief role in employing indigenous knowledge in weaving as well as medicinal knowledge in birth control, and these kinds of knowledge are passed down through the female line. However, the inheritance style of traditional knowledge is decreasing. Indig- enous knowledge plays an important role in the sustainable development of this village, which gives implications for development practices to involve indigenous knowledge to achieve sustainable development.

Ontology Engineering and Knowledge Services for Agriculture Domain

This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-specialist users, how to provide adequate information to knowledge workers and how to provide the information requiring highly focused and related information. Cyber-Brain has been designed as a platform that combines approaches based on knowledge engineering and language engineering to gather knowledge from various sources and to provide the effective knowledge service. Based on specially designed ontology for practical service scenarios, it can aggregate knowledge from Internet, digital archives, expert, and other resources for providing one-stop-shop knowledge services. The domain specific and task oriented ontology also enables advanced search and allows the system ensures that knowledge service could improve the user benefit. Users are presented with the necessary information closely related to their information need and thus of potential high interest. This paper presents several service scenarios for different end-users and reviews ontology engineering and its life cycle for supporting AOS （Agricultural Ontology Services） Vocbench which is the heart of knowledge services in agriculture domain.

Interpreting and Extracting Open Knowledge for Human-Robot Interaction

A more natural way for non-expert users to express their tasks in an open-ended set is to use natural language. In this case,a human-centered intelligent agent/robot is required to be able to understand and generate plans for these naturally expressed tasks. For this purpose, it is a good way to enhance intelligent robot's abilities by utilizing open knowledge extracted from the web, instead of hand-coded knowledge. A key challenge of utilizing open knowledge lies in the semantic interpretation of the open knowledge organized in multiple modes, which can be unstructured or semi-structured, before one can use it.Previous approaches used a limited lexicon to employ combinatory categorial grammar(CCG) as the underlying formalism for semantic parsing over sentences. Here, we propose a more effective learning method to interpret semi-structured user instructions. Moreover, we present a new heuristic method to recover missing semantic information from the context of an instruction. Experiments showed that the proposed approach renders significant performance improvement compared to the baseline methods and the recovering method is promising.

Extracting and Measuring Uncertain Biomedical Knowledge from Scientific Statements

Purpose:Given the information overload of scientific literature,there is an increasing need for computable biomedical knowledge buried in free text.This study aimed to develop a novel approach to extracting and measuring uncertain biomedical knowledge from scientific statements.Design/methodology/approach:Taking cardiovascular research publications in China as a sample,we extracted subject-predicate-object triples(SPO triples)as knowledge units and unknown/hedging/conflicting uncertainties as the knowledge context.We introduced information entropy(IE)as potential metric to quantify the uncertainty of epistemic status of scientific knowledge represented at subject-object pairs(SO pairs)levels.Findings:The results indicated an extraordinary growth of cardiovascular publications in China while only a modest growth of the novel SPO triples.After evaluating the uncertainty of biomedical knowledge with IE,we identified the Top 10 SO pairs with highest IE,which implied the epistemic status pluralism.Visual presentation of the SO pairs overlaid with uncertainty provided a comprehensive overview of clusters of biomedical knowledge and contending topics in cardiovascular research.Research limitations:The current methods didn’t distinguish the specificity and probabilities of uncertainty cue words.The number of sentences surrounding a given triple may also influence the value of IE.Practical implications:Our approach identified major uncertain knowledge areas such as diagnostic biomarkers,genetic polymorphism and co-existing risk factors related to cardiovascular diseases in China.These areas are suggested to be prioritized;new hypotheses need to be verified,while disputes,conflicts,and contradictions need to be settled.Originality/value:We provided a novel approach by combining natural language processing and computational linguistics with informetric methods to extract and measure uncertain knowledge from scientific statements.

Expanding the Definition and Measurement of Knowledge Economy： Integrating Triple Bottom Line Factors into Knowledge Economy Index Models and Methodologies

The application of knowledge is a primary source of growth in the knowledge economy. The World Bank Group has developed a rigorous assessment methodology for assessing a country＇s ability to access and use knowledge to become more competitive in the knowledge economy of the 21st century. The World Bank＇s annual knowledge economy index is grounded on a four-pillar model： （1） economic incentives and institutional regime; （2） education and skills; （3） information and communication infrastructure; and （4） innovation systems. An argument can be made that the model lacks coverage of some key factors that pertain to intellectual capital and the production and consumption of knowledge. The model＇s heavy focus on economic incentives and open institutional regimes comes at a societal cost. This paper proposes an alternative knowledge economy index which is grounded in a more holistic and balanced view of a knowledge society. Adopting the perspective of triple bottom line shifts the purpose and design of a knowledge economy from one of aggregation and reporting to action and involvement. The World Bank＇s scorecard and indexing methodology are adaptable to this new perspective and a new set of indicators.

Some Massively Parallel Algorithms from Nature

We introduced the work on parallel problem solvers from physics and biology being developed by the research team at the State Key Laboratory of Software Engineering, Wuhan University. Results on parallel solvers include the following areas: Evolutionary algorithms based on imitating the evolution processes of nature for parallel problem solving, especially for parallel optimization and model-building; Asynchronous parallel algorithms based on domain decomposition which are inspired by physical analogies such as elastic relaxation process and annealing process, for scientific computations, especially for solving nonlinear mathematical physics problems. All these algorithms have the following common characteristics: inherent parallelism, self-adaptation and self-organization, because the basic ideas of these solvers are from imitating the natural evolutionary processes.

Time-Aware PolarisX: Auto-Growing Knowledge Graph

A knowledge graph is a structured graph in which data obtained from multiple sources are standardized to acquire and integrate human knowledge.Research is being actively conducted to cover a wide variety of knowledge,as it can be applied to applications that help humans.However,existing researches are constructing knowledge graphs without the time information that knowledge implies.Knowledge stored without time information becomes outdated over time,and in the future,the possibility of knowledge being false or meaningful changes is excluded.As a result,they can’t reect information that changes dynamically,and they can’t accept information that has newly emerged.To solve this problem,this paper proposes Time-Aware PolarisX,an automatically extended knowledge graph including time information.TimeAware PolarisX constructed a BERT model with a relation extractor and an ensemble NER model including a time tag with an entity extractor to extract knowledge consisting of subject,relation,and object from unstructured text.Through two application experiments,it shows that the proposed system overcomes the limitations of existing systems that do not consider time information when applied to an application such as a chatbot.Also,we verify that the accuracy of the extraction model is improved through a comparative experiment with the existing model.

Understanding of Traditional Knowledge and Indigenous Institutions on Sustainable Land Management in Kilimanjaro Region, Tanzania

The paper is based on a study whose objective is to provide an understanding of the extent to which traditional knowledge and indigenous institutions for natural resource governance remain relevant to solving current land degradation issues and how they are integrated in formal policy process in Kilimanjaro Region. Data collection for this study combined qualitative and quantitative methods. A total of 221 individuals from households were interviewed using a structured questionnaire;41 in-depth interviews and 24 focus group discussions were held. Findings indicate that the community acknowledges that there is traditional knowledge and indigenous institutions regarding sustainable land management. However, awareness of the traditional knowledge and practices varied between districts. Rural-based districts were found to be more aware and therefore practiced more of traditional knowledge than urban based districts. Variations in landscape features such as proneness to drought, landslides and soil erosion have also attracted variable responses among the communities regarding traditional knowledge and indigenous practices of sustainable land management. In addition, men were found to have more keen interest in conserving the land than women as well as involvement in other traditional practices of sustainable land management. This is due to the fact that, customarily, it is men who inherit and own land. This, among other factors, could have limited the integration of traditional knowledge and indigenous institutions in village by-laws and overall policy process. The paper concludes by recommending that traditional knowledge and indigenous institutions for sustainable land management should be promoted among the younger generations so as to capture their interest, and ensure that successful practices are effectively integrated into the national policies and strategies.

Traditional Ecological Knowledge and Practices of Ethnic Kyrgyz of the Eastem Pamir in Ethnotoponyms

The author analyzes ethnotoponyms, the local place names of Kyrgyz people living in the Murgab region of Tajikistan＇s Gorno-Badakhshan Autonomous region. The author conducted field research in the region in 2010-2015. The article also builds on data from the works of pre-Soviet Russian and western travelers, who studied the region at middle 19th early 20th centuries. The author concludes that local place names given by Kyrgyz people to the mountains, rivers, lakes, and valleys reflect the unique features of natural landscapes of Eastern Pamir as well as Kyrgyz nomads＇ empirical observations of natural phenomena and processes, livelihoods and nomadic values.

A Motivation Framework to Promote Knowledge Translation in Healthcare

Globally,there is a great gulf between medical knowledge and clinical practice.Translating knowledge into clinical decision support(CDS) application has become the biggest challenge faced by evidence based medicine.This paper proposed a comprehensive motivation framework to facilitate knowledge translation in healthcare.Based on a unified medical knowledge ontology and knowledge base,the framework provides an infrastructure of fundamental services,such as inference service and data acquisition,to support development of knowledge-driven CDS applications and integration into clinical workflow.The framework has been implemented in a 2600-bed Chinese hospital,and is able to reduce the time and cost of developing typical CDS applications.

基于业务流程的认知图谱

针对目前软件项目开发过程中无法充分利用已有业务资源,进而导致开发效率低、能力弱等问题,通过研究业务资源之间的关联,提出一种基于业务流程的认知图谱。首先,通过正式文档抽取业务知识,提出建立知识层级的方法并修正;其次,通过代码特征挖掘与代码实体相似度判断构建代码网络表示模型;最后,利用实际业务数据进行实验验证,并与向量空间模型(VSM)、多样化排序和深度学习等方法进行对比。最终构建的基于业务流程的认知图谱在代码检索方面优于目前基于文本匹配的方法和深度学习算法,分别在前5准确率(precision@5)、平均精度均值(mAP)、归一化折扣增益值(?-NDCG)这3项指标上高过多样化排序的代码检索方法4.30、0.38和2.74个百分点,有效解决了潜在业务词汇识别、业务认知推理表示等多个问题,提升了代码检索效果与业务资源利用率。

基于大语言模型辅助的防洪调度规则标签设计方法

防洪调度规则的信息抽取对于防洪调度自动化具有重要意义,而标签体系设计在信息抽取任务中至关重要。一般的设计方式经常由于理解偏差和疏漏,导致设计出来的标签体系存在过度概括、不全面和不易区分等问题,这些问题被称为标签体系的非完美性。针对这一问题,本研究重点面向防洪调度文本中的规则抽取,提出了一种创新性的非完美标签优化方法,旨在改进文本信息抽取的标签设计方法。方法利用大语言模型进行辅助,通过标签细化、标签生成和标签更名等措施,来提高标签的准确性和表达能力。此外,本文还提出了一种针对数据集标签较多的实体关系三元组分组抽取方法。通过对实体关系三元组进行分组,并按照分组训练模型与识别结果,有效改善了数据集标签较多情况下模型的信息抽取效果。最终,研究利用Neo4j形成了可视化的防洪调度知识图谱。本文研究成果为后续的防洪调度工作以及相关的知识抽取工作提供了基础资源,对防洪调度领域的知识抽取进行了探索。

《沈阳农业大学学报》1993~2022年文献计量分析

基于CNKI数据库,采用文献计量法对《沈阳农业大学学报》1993~2022年共30年发文进行文献计量评价与分析,包括载文量、基金资助文献量、基金资助类别、文献所属栏目、文献所属学科、关键词分布、作者及发文机构分布和2022年期刊影响力指数及影响因子。结果表明,30年时间范围内,《沈阳农业大学学报》总文献量为4313篇,其中园艺、农作物、植物保护和农业基础科学等领域发文量较大,占比分别为16.7%、12.9%、12.0%、11.1%,与沈阳农业大学的优势学科及发展趋势相符合,同时获基金资助论文占比逐年提高,科研论文学术水平、期刊影响力指数及影响因子随之增加,但作者及发文机构过于单一,距国内高水平期刊仍有差距。对此,提出聚焦热点课题、凸显学术创新、提高影响力指数等对策建议。

基于本体和自然语言处理的土石坝险情知识图谱构建方法研究

土石坝在运维阶段可能受施工质量低、极端环境灾害等因素的影响,从而发生滑坡、裂缝、洪水漫顶等一系列险情。现阶段与土石坝险情相关的大量数据存储分散、结构多样,难以直接转化为经验和知识得到有效利用,快速指导险情处置。本研究针对土石坝险情领域提出了基于本体和自然语言处理(NLP)的知识图谱(KG)构建方法,分别采用自顶向下与自底向上法,构建图谱的模式层和数据层。模式层围绕险情类型、险情原因、险情措施三大概念,从土石坝结构、过程、环境、材料4方面建立领域本体库,搭建KG的概念结构。数据层通过数据预处理、知识抽取、语义对齐等操作,运用NLP对文本进行处理并根据语料的特征建立相应的提取规则,获得数据层的具体知识内容。最后以三元组形式存储不同类型的实例和相互关系,运用Neo4j图数据库进行土石坝险情领域KG的可视化表达及查询应用,使领域内分散数据向集成知识转化,为土石坝安全管理和险情处置提供技术和理论支持。

基于自然语言处理(NLP)的医学知识挖掘探索与实践

目的通过对医学健康知识的挖掘,为人工智能等的健康科普知识支撑提供实践经验。方法采用基于自然语言处理(NLP)技术对徐汇区疾病预防控制中心2010年1月—2021年1月积累的科普文章进行结构拆分、阅读理解、实体识别等,处理流程包括文档预处理、特征提取、段落筛选、阅读理解、答案排序、审核和发布。结果通过直接文档结构拆分,得到5395条问答;通过阅读理解,得到857条问答;通过抽取数字问答,得到1668条,初步形成问答形式的医学健康知识库。结论自然语言处理(NLP)技术为人工智能技术需要的大量语料素材提供了有效制作方法。

“纯天然化妆品DIY”课程思政教学研究

大学课堂是用习近平新时代中国特色社会主义思想凝心铸魂的主营地,开展“全校通识课程”社会大课进行思政教学是高校落实立德树人根本任务的重要举措。立足国家强国战略对新时代人才需求,将思政教育融入“纯天然化妆品DIY”实践课堂,以“家国情怀、工匠精神、时代使命、国际传播和孝文化”为切入点,全面推进课程思政建设,挖掘通识课程思政内涵,实现通识教育课程“知识传授”与“思政引领”相统一,培根铸魂,启智润心,培养新时代创新引领性人才,从而推动高校通识课程思政教学高质量发展。