Knowledge enhanced graph inference network based entity-relation extraction and knowledge graph construction for industrial domain

With the escalating complexity in production scenarios, vast amounts of production information are retained within enterprises in the industrial domain. Probing questions of how to meticulously excavate value from complex document information and establish coherent information links arise. In this work, we present a framework for knowledge graph construction in the industrial domain, predicated on knowledge-enhanced document-level entity and relation extraction. This approach alleviates the shortage of annotated data in the industrial domain and models the interplay of industrial documents. To augment the accuracy of named entity recognition, domain-specific knowledge is incorporated into the initialization of the word embedding matrix within the bidirectional long short-term memory conditional random field (BiLSTM-CRF) framework. For relation extraction, this paper introduces the knowledge-enhanced graph inference (KEGI) network, a pioneering method designed for long paragraphs in the industrial domain. This method discerns intricate interactions among entities by constructing a document graph and innovatively integrates knowledge representation into both node construction and path inference through TransR. On the application stratum, BiLSTM-CRF and KEGI are utilized to craft a knowledge graph from a knowledge representation model and Chinese fault reports for a steel production line, specifically SPOnto and SPFRDoc. The F1 value for entity and relation extraction has been enhanced by 2% to 6%. The quality of the extracted knowledge graph complies with the requirements of real-world production environment applications. The results demonstrate that KEGI can profoundly delve into production reports, extracting a wealth of knowledge and patterns, thereby providing a comprehensive solution for production management.

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.

A New Machine-Learning Extracting Approach to Construct a Knowledge Base: A Case Study on Global Stromatolites over Geological Time

Within any scientific disciplines, a large amount of data are buried within various literature depositories and archives, making it difficult to manually extract useful information from the datum swamps. The machine-learning extraction of data therefore is necessary for the big-data-based studies. Here, we develop a new text-mining technique to reconstruct the global database of the Precambrian to Recent stromatolites, providing better understanding of secular changes of stromatolites though geological time. The step-by-step data extraction process is described as below. First, the PDF documents of stromatolite-containing literatures were collected, and converted into text formation. Second, a glossary and tag-labeling system using NLP(Natural Language Processing) software was employed to search for all possible candidate pairs from each sentence within the papers collected here. Third, each candidate pair and features were represented as a factor graph model using a series of heuristic procedures to score the weights of each pair feature. Occurrence data of stromatolites versus stratigraphical units(abbreviated as Strata), facies types, locations, and age worldwide were extracted from literatures, respectively, and their extraction accuracies are 92%/464, 87%/778, 92%/846, and 93%/405 from 3 750 scientific abstracts, respectively, and are 90%/1 734, 86%/2 869, 90%/2 055 and 91%/857 from 11 932 papers, respectively. A total of 10 072 unique datum items were identified. The newly obtained stromatolite dataset demonstrates that their stratigraphical occurrences reached a pronounced peak during the Proterozoic(2 500 – 541 Ma), followed by a distinct fall during the Early Phanerozoic, and overall fluctuations through the Phanerozoic(541–0 Ma). Globally, seven stromatolite hotspots were identified from the new dataset, including western United States, eastern United States, western Europe, India, South Africa, northern China, and southern China. The proportional occurrences of inland aquatic stromatolites remain rather low(~20%) in comparison to marine stromatolites from the Precambrian to Jurassic, and then display a significant increase(30%–70%) from the Cretaceous to the present.

Fusing Systems Thinking with Knowledge Management

This paper introduces the background and purpose of the International Society for Knowledge and Systems Sciences and considers new developments in systems science in the knowledge society.First,in connection with the reason why the name of the society includes knowledge and systems,this paper argues that it is important to support each other for the development of both systems science and knowledge science.Next,this paper introduces three approaches that have tried to combine systems thinking and knowledge management in this academic society.They are Knowledge Systems Engineering,Informed Systems Approach,and Knowledge Construction Systems Methodology.This paper suggests new developments in systems science and engineering that incorporate the concept of knowledge management through explanations of these significances.

KNOWLEDGE SYNTHESIS IN TECHNOLOGY DEVELOPMENT

This paper introduces a knowledge construction model called the i-System for knowledge integration and creation and its relation to the new concept of the Creative Space. The five ontological elements of the i-System are Intelligence, Involvement, Imagination, Intervention, and Integration corresponding to five diverse dimensions of the Creative Space. The paper discusses the meanings and functions of these dimensions in knowledge integration and creation, and presents applications of the i-System to technology roadmapping and archiving.

A Study on Human-Computer Interaction Mechanism in an Intelligent Tutoring System

Discussion is a common and important learning process.Involvement of a virtual agent can provide adaptive support for the discussion process.Argumen-tative knowledge construction is beneficial to learners’acquisition of knowledge,but the effectiveness of argumentative scaffolding in existing studies is not consistent.Based on an intelligent discussion system,a total of 47 undergraduate students took part in the experiment and they were assigned to three different conditions:content-related plus content-independent scaffolding condition,content-related scaffolding condition,and the control condition.Under the content-related and content-independent scaffolding condition,the computer agent provided an idea from semantically different categories(content-related scaffolding)according to the automatic categorization of the current contributions,and further inquired the participants about their attitudes and reasons(content-independent scaffolding).Under the condition of content-related scaffolding condition,the virtual agent only provided semantically different viewpoints.Under the control condition,the subjects expressed their opinion independently without the participation of the virtual agent.Findings revealed that compared with the control group,when the virtual agent provided semantically different ideas(content-related scaffolding),the discussion breadth(number of categories)was improved and the subjects felt that they had a more comprehensive understanding of the problem.Compared with the content-related scaffolding condition,when the virtual agent provided semantically different ideas and further asked about the attitudes and reasons,the subjects expressed more agreement with these views,but mentioned fewer categories during the discussion.This study suggests that the content-related scaffolding can facilitate the cognitive processing effect relevant to the topic of discussion.When the content independent scaffolding is added,it can promote the argumentative processing,but may have a negative effect on the cognitive processing related to the topic discussed.