Construction a Knowledge Base of a CAD System for Neutralizing Emissions from Energy Source Enterprises and Production Enterprises

The conventional approach to build a chatbot system uses the sequence of complex algorithms and productivity of these systems depends on order and coherence of algorithms. This research work introduces and showcases a deep learning-based conversation system approach. The proposed approach is an intelligent conversation model approach which conceptually uses graph model and neural conversational model. The proposed deep learning-based conversation system uses neural conversational model over knowledge graph model in a hybrid manner. Graph-based model answers questions written in natural language using its intent in the knowledge graph and neural conversational model converses answer based on conversation content and conversation sequence order. NLP is used in graph model and neural conversational model uses natural language understanding and machine intelligence. The neural conversational model uses seq2seq framework as it requires less feature engineering and lacks domain knowledge. The results achieved through the authors' approach are competitive with solely used graph model results.

Knowledge and Intelligence have a much closed relationship. Knowledge is both the crystallization and source of intelligence. Knowledge embodies intelligence, and intelligence emerges from knowledge. Every ICAX system (Intelligent Computer Aided X, where X may mean any domain, such as education, design or manufacturing, etc.), such as ICAI (I = Instruction), ICAD (D = Design), ICAM (M = Manufacturing), etc., has its intelligence based on a content rich knowledge base. In this sense, we may have the formula: ICAX = CAX + X knowledge base. Using this formula, we have developed a methodology of generating knowledge based system automatically. The core idea is to develop a domain-oriented pseudo-natural language (PNL for short), where PNL means a normalized subset of some natural language, which can be easily parsed by computer. Each domain expert may use this language to write down his knowledge and experience. A PNL compiler then compiles 'program's written in this PNL to form a domain knowledge base. Combined with a preexisting system shell, a prototype of the knowledge based system is automatically generated. We have applied this idea to automatic generation of ICAI and ICASE (SE = Software Engineering) systems. The following problem is how to generalize this idea. Can the development of knowledge base and system shell be done by different people or groups? Can the knowledge base be easily renewed or even become an independent commodity? Finally, we have got an answer to this problem. The commodity form of such knowledge base is knowware. In general, knowware is a commodity form of knowledge. More precisely, knowware is a commercialized knowledge module with documentation and intellectual property, which is computer operable, but free of any built-in control mechanism, meeting some industrial standards and embeddable in software/hardware. The process of development, application and management of knowware is called knowware engineering. Three different knowware life cycle models are discussed: the furnace model, the crystallization model and the spiral model. Software/ knowware co-engineering is a mixed process involving both software engineering and knowware engineering issues. It involves three parallel lines of developing system components of different types. The key issues of this process are how to guarantee the correctness and appropriateness of system composition and decomposition. The ladder principle, which is a modification of the waterfall model, and the tower principle, which is a modification of the fountain model, are proposed.

Big data represents a new technology for managing data with high velocity, volume, variety and contributes to creating value for companies. As quoted in1, capturing all queries made on the company website or from customer support calls, emails or chat lines, regardless of their outcome, may have significant value in identifying emerging trends. The Big Data Era has largely contributed in accelerating the development strategic plans issued from governments and research organisms, coving the management, exploitation and analysis of these data by taking into account the different V's of Big Data. Among these plans, we can cite for instance the development of: (a) large-scale platforms (ex. data-clusters, distributed data clusters), (b) Software Defined Environments (SDE) (ex. IBM SDE), (c) advanced programming paradigms (ex. map-reduce, Spark, etc.), Data Analytics Tools (Rapid Miner, Google Fusion Tables, Solver), (d) Visualization tools (Google Chart, Tableau, Oracle Visual Analyzer), and (h) high quality and valuable Knowledge Bases (KB), constructed either by academicians (e.g., Cvc, DBpedia, Freebase, and YAGO) and industrials (e.g., Google Knowledge Graph, Facebook Knowledge Graph, Amazon Knowledge Graph, Credit Rating Agencies, Enterprise Knowledge Base, etc.). In this talk, we would like to foster the creation of a think tank dedicated to getting the best from Big Data V's and the efforts related to it to revisit our research activities without compromising them. In this talk, we would like to share the experience conducted with our Model and Data Engineering Team of the LIAS Laboratory at ISAE-ENSMA, which aims at the design of data warehousing applications. Based on the literature, this design is based on two main approaches: (i) a supply-driven approach (also called data-driven) that starts with an analysis of operational data sources in order to identify all the available data and (ii) a user-driven approach (also known as requirement-driven or goal-orientated) which stems from the determination of the information requirements of different business users. Several studies and experiments show that resorting to these two approaches entails a high risk for companies, since some functional requirements cannot be satisfied. This is due to the lack of relevant data in sources. In parallel, reference studies have identified the crucial role of knowledge bases (KB) for analytical tasks, by offering analysts more entities (people, places, products, etc.). The availability of a huge, high quality valuable KB is an asset for data warehousing designers and decision-makers to construct/exploit a valuable data warehouse. So, faced with this situation, we here present a value-driven approach that revisits the traditional life cycle of the design of data warehouses, by considering KB as an external resource. These different phases are illustrated via the YAGO KB.

Evolution of Knowledge Bases (KBs) consists of incorporating new information in an existing KB. Previous studies assume that the new information should be fully trusted and thus completely incorporated in the old knowledge. We suggest a setting where the new knowledge can be partially trusted and develop model-based approaches (MBAs) to KB evolution that rely on this assumption. Under MBAs the result of evolution is a set of interpretations and thus two core problems for MBAs are closure, i.e., whether evolution result can be axiomatised with a KB, and approximation, i.e., whether it can be (maximally) approximated with a KB. We show that DL-Lite is not closed under a wide range of trust-sensitive MBAs. We introduce a notion of s-approximation that improves the previously proposed approximations and show how to compute it for various trust-sensitive MBAs.

Dynamic scheduling is a complicated problem with high research value in the production field, because it requires a fast response to the dynamic disturbance event. In this paper, knowledge graph is combined with reinforcement learning to solve this problem. In the proposed method, knowledge graph model is constructed by a series of steps, including ontology-based knowledge modeling, knowledge extraction, and knowledge fusion. In the knowledge graph model, the state information of the shop floor is extracted from the production data, including the information about machine, job, and process. On this basis, reinforcement learning model is designed, which is composed of machine assignment agent and job sequence agent. Thus, the real-time scheduling decisions can be made rapidly under the dynamic production environment. The effectiveness of the proposed method is verified on a case study of aviation component production workshop. Compared with other methods, the proposed method obtains the best results.

Requirements necessary to integrate intelligent computer aided design systems in future KAE/CAM systems aimed at supporting concurrent engineering and the handling of product data, propounding extensions and new developments of system architecture, data structures and functionality. Indispensable among these are an integrated and flexible formalization of the knowledge base and a modular system architecture which can be easily modified to suit current demands. Within our developed framework we propose a homogeneous representation of declarative, procedural, and control knowledge based on a conditional attributed rewriting system (CARW). Design, reasoning and computation are carried out in domains of typed attributed entities, which are replenished with reflective capabilities. Due to the rule-based approach, a high degree of flexibility and modularity can be achieved, while perpetuating the system’s overall control in the form of a CARW manager. The objective of this study is to investigate how far our approach satisfies the requirements under consideration. Of major interest are — within the given scope — consistency aspects among attributed entities of different domains and the use of reflective techniques to support status evaluation of design features, their operators as well as design strategies.

Modern knowledge bases contain and organize knowledge from many different topic areas. Apart from specific entity information, they also store information about their relationships amongst each other. Combining this information results in a knowledge graph that can be particularly helpful in cases where relationships are of central importance. Among other applications, modern risk assessment in the financial sector can benefit from the inherent network structure of such knowledge graphs by assessing the consequences and risks of certain events, such as corporate insolvencies or fraudulent behavior, based on the underlying network structure. As public knowledge bases often do not contain the necessary information for the analysis of such scenarios, the need arises to create and maintain dedicated domain-specific knowledge bases. This thesis investigates the process of creating domain-specific knowledge bases from structured and unstructured data sources. In particular, it addresses the topics of named entity recognition (NER), duplicate detection, and knowledge validation, which represent essential steps in the construction of knowledge bases. As such, we present a novel method for duplicate detection based on a Siamese neural network that is able to learn a dataset-specific similarity measure which is used to identify duplicates. Using the specialized network architecture, we design and implement a knowledge transfer between two deduplication networks, which leads to significant performance improvements and a reduction of required training data. Furthermore, we propose a named entity recognition approach that is able to identify company names by integrating external knowledge in the form of dictionaries into the training process of a conditional random field classifier. In this context, we study the effects of different dictionaries on the performance of the NER classifier. We show that both the inclusion of domain knowledge as well as the generation and use of alias names results in significant performance improvements. For the validation of knowledge represented in a knowledge base, we introduce Colt, a framework for knowledge validation based on the interactive quality assessment of logical rules. In its most expressive implementation, we combine Gaussian processes with neural networks to create Colt-GP, an interactive algorithm for learning rule models. Unlike other approaches, Colt-GP uses knowledge graph embeddings and user feedback to cope with data quality issues of knowledge bases. The learned rule model can be used to conditionally apply a rule and assess its quality. Finally, we present CurEx, a prototypical system for building domain-specific knowledge bases from structured and unstructured data sources. Its modular design is based on scalable technologies, which, in addition to processing large datasets, ensures that the modules can be easily exchanged or extended. CurEx offers multiple user interfaces, each tailored to the individual needs of a specific user group and is fully compatible with the Colt framework, which can be used as part of the system. We conduct a wide range of experiments with different datasets to determine the strengths and weaknesses of the proposed methods. To ensure the validity of our results, we compare the proposed methods with competing approaches.

Knowledge graph technology is a critical part of artificial intelligence research. It establishes a knowledge base with the capacity of semantic processing and open interconnection in order to provide intelligent information service, such as search, question-answering, personalized recommendation, and so on. This article first presents a comprehensive study on definitions and architectures of knowledge graphs. Then we summarizes recent advances in knowledge graphs, including knowledge extraction, knowledge representation, knowledge fusion, and knowledge reasoning, with typical applications. Finally, this article concludes with future challenges of knowledge graphs.

Millions of HTML tables containing structured data can be found on the Web. With their wide coverage, these tables are potentially very useful for filling missing values and extending cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph. As a prerequisite for being able to use table data for knowledge base extension, the HTML tables need to be matched with the knowledge base, meaning that correspondences between table rows/columns and entities/schema elements of the knowledge base need to be found. This paper presents the T2D gold standard for measuring and comparing the performance of HTML table to knowledge base matching systems. T2D consists of 8 700 schema-level and 26 100 entity-level correspondences between the WebDataCommons Web Tables Corpus and the DBpedia knowledge base. In contrast related work on HTML table to knowledge base matching, the Web Tables Corpus (147 million tables), the knowledge base, as well as the gold standard are publicly available. The gold standard is used afterward to evaluate the performance of T2K Match, an iterative matching method which combines schema and instance matching. T2K Match is designed for the use case of matching large quantities of mostly small and narrow HTML tables against large cross-domain knowledge bases. The evaluation using the T2D gold standard shows that T2K Match discovers table-to-class correspondences with a precision of 94%, row-to-entity correspondences with a precision of 90%, and column-to-property correspondences with a precision of 77%.

In recent years, several large-scale knowledge bases (KBs) have been constructed, such as YAGO, DBpedia, and Google Knowledge Graph. Although automatic extractio techniques that extract facts and rules from the Web is necessary for constructing such large-scale KBs, incorporation of noisy, unreliable knowledge cannot be unavoidable. Thus, Google Knowledge Vault assigns extracted knowledge with confidence scores based on consistency with the existing KBs. In this paper, we propose a new approach for associating confidence scores with knowledge based on a large amount of raw data for domains, where there is no existing KB. We first construct knowledge in a specific domain as a semantic network, and then design a probabilistic network, that corresponds to the semantic network. To associate the confidence scores with the semantic network, we train the probabilistic network with a large amount of open data, provided by the Osaka central wholesale market in Japan. We also confirm the validity of the confidence scores with the accuracy of reasoning on the probabilistic network. A semantic network associated with confidence scores, that is, a weighted labeled graph is advantageous not only for reducing the noisy, unreliable knowledge with low confidence, but also for making retrieval results ranking on the KB. In the future, probabilistic reasoning on semantic networks may also be possible.

Nowadays, computer numerical control (CNC) machine tool undertakes more processing tasks than other common machine tools because of its highly automated machining ability and high performance. However, due to the lack of intelligence in machining process planning, machining procedure of products mostly depends on process planners rather than CNC machine tools. To make product quality less dependable on process planner’s ability and improve the efficiency of process planning in order to fulfill changeable market, this paper presents an approach to design and develop CNC machining process knowledge base using cloud technology. The general standard STEP-NC is mapped to web ontology language (OWL) to describe machining process-related knowledge in a readable and comprehensible way. This mapping relation also makes knowledge suitable for storage in HBase. Through this ontology model, descriptive and logical knowledge can be collected. Hadoop platform is used in this approach to provide the NoSQL database HBase for large-scale knowledge storage and MapReduce programming model for large-scale knowledge processing. Taking advantage of MapReduce, knowledge query engine and reasoning engine can be developed. Users can submit task and resource descriptive files to the cloud through CNC controller and get machining process solutions from knowledge base. Evaluation mechanism is also adopted to filter low-quality knoweldge.

Open knowledge platform can provide a purified knowledge base. Thus, we build a platform for several application areas in a cloud computing that supports APIs for various data based on a knowledge utilization model. The goal of this platform is to maximize the utilization of the knowledge base. In order to achieve this goal, we designed the structure of this platform as an open knowledge platform. The targets of the design are to maximize the utilization of data linkage, to expand it to national common knowledge and to increase its usability by providing services with knowledge graphs. In order to design the platform we identified users, information sources, and infrastructures. In the process, we found it is crucial to specify roles and services to the users of the platform. The requirements are induced from a utilization model and scenario of the service based on the knowledge graph. With the service scenario, stakeholders of the platform started narrow down function modules needed to support the service. One of the modules is a national common knowledge in the knowledge base, which provide essential connected knowledge to support solving regional problems of government such as earthquake, flooding. To increase the usability of data scattered by departments and agencies, data linkage, and knowledge between fragmented data sets is included in this platform. Subsequently, we designed modules to support the effective utilization of this knowledge information. Also, we found that a cloud infrastructure instead of in-house hardware and software could provide flexible and compatible services for the platform. Moreover, the cloud system has advantages on big data analysis and distributed system interconnection. Utilization model and scenario-based process modeling provide a systematic approach to design an open knowledge platform that supports many required components enabling interoperability, compatibility, and connectivity among other knowledge bases..

Knowledge graphs (KGs) remain an underutilized tool in the field of psychiatric research. In the broader biomedical field KGs are already a significant tool mainly used as knowledge database or for novel relation detection between biomedical entities. This review aims to outline how KGs would further research in the field of psychiatry in the age of Artificial Intelligence (AI) and Large Language Models (LLMs). We conducted a thorough literature review across a spectrum of scientific fields ranging from computer science and knowledge engineering to bioinformatics. The literature reviewed was taken from PubMed, Semantic Scholar and Google Scholar searches including terms such as "Psychiatric Knowledge Graphs", "Biomedical Knowledge Graphs", "Knowledge Graph Machine Learning Applications", "Knowledge Graph Applications for Biomedical Sciences". The resulting publications were then assessed and accumulated in this review regarding their possible relevance to future psychiatric applications. A multitude of papers and applications of KGs in associated research fields that are yet to be utilized in psychiatric research was found and outlined in this review. We create a thorough recommendation for other computational researchers regarding use-cases of these KG applications in psychiatry. This review illustrates use-cases of KG-based research applications in biomedicine and beyond that may aid in elucidating the complex biology of psychiatric illness and open new routes for developing innovative interventions. We conclude that there is a wealth of opportunities for KG utilization in psychiatric research across a variety of application areas including biomarker discovery, patient stratification and personalized medicine approaches.

Nowadays knowledge base KB has been viewed as one of the important infrastructures for many web search applications and NLP tasks. However, in practice the availability of KB data varies from language to language, which greatly limits potential usage of knowledge base. In this paper, we propose a novel method to construct or enrich a knowledge base by entity translation with help of another KB but compiled in a different language. In our work, we concentrate on two key tasks: 1 collecting translation candidates with as good coverage as possible from various sources such as web or lexicon; 2 building an effective disambiguation algorithm based on collective inference approach over knowledge graph to find correct translation for entities in the source knowledge base. We conduct experiments on movie domain of our in-house knowledge base from English to Chinese, and the results show the proposed method can achieve very high translation precision compared with classical translation methods, and significantly increase the volume of Chinese knowledge base in this domain.

As organizations have become increasingly effective at reducing costs and improving efficiency in their operations, their attention has shifted to revenue growth. Tapping the collective creativity and know‐how of employees is emerging as one of the best ways to anticipate customer needs and grow new markets. Typically, when companies set out to create knowledge bases, the first issue that they tackle is technology. In many cases, resources and patience are exhausted by the time they have resolved technology issues. This paper reviews Arthur Andersen’s experiences in developing knowledge bases ‐ focusing on content; knowledge sharing; and culture change.