A programmable approach to revising knowledge bases

This paper presents a programmable approach to revising knowledge bases consisting of clauses. Some theorems and lemmas are shown in order to give procedures for generating maximally consistent subsets. Then a complete procedure and an incomplete procedure for generating the maximal consistent subsets are presented, and the correctness of the procedures is also shown. Furthermore, a way to implement knowledge base revision is presented, and a prototype system is introduced. Compared with related works, the main characteristic of our approach is that the approach can be implemented by a computer program.

KB4Rec:A Data Set for Linking Knowledge Bases with Recommender Systems

To develop a knowledge-aware recommender system,a key issue is how to obtain rich and structured knowledge base(KB)information for recommender system(RS)items.Existing data sets or methods either use side information from original RSs(containing very few kinds of useful information)or utilize a private KB.In this paper,we present KB4Rec v1.0,a data set linking KB information for RSs.It has linked three widely used RS data sets with two popular KBs,namely Freebase and YAGO.Based on our linked data set,we first preform qualitative analysis experiments,and then we discuss the effect of two important factors(i.e.,popularity and recency)on whether a RS item can be linked to a KB entity.Finally,we compare several knowledge-aware recommendation algorithms on our linked data set.

Sememe knowledge computation:a review of recent advances in application and expansion of sememe knowledge bases

A sememe is defined as the minimum semantic unit of languages in linguistics.Sememe knowledge bases are built by manually annotating sememes for words and phrases.HowNet is the most well-known sememe knowledge base.It has been extensively utilized in many natural language processing tasks in the era of statistical natural language processing and proven to be effective and helpful to understanding and using languages.In the era of deep learning,although data are thought to be of vital importance,there are some studies working on incorporating sememe knowledge bases like HowNet into neural network models to enhance system performance.Some successful attempts have been made in the tasks including word representation learning,language modeling,semantic composition,etc.In addition,considering the high cost of manual annotation and update for sememe knowledge bases,some work has tried to use machine learning methods to automatically predict sememes for words and phrases to expand sememe knowledge bases.Besides,some studies try to extend HowNet to other languages by automatically predicting sememes for words and phrases in a new language.In this paper,we summarize recent studies on application and expansion of sememe knowledge bases and point out some future directions of research on sememes.

An algebraic approach to revising propositional rule-based knowledge bases

One of the important topics in knowledge base revision is to introduce an efficient implementation algorithm. Algebraic approaches have good characteristics and implementation method; they may be a choice to solve the problem. An algebraic approach is presented to revise propositional rule-based knowledge bases in this paper. A way is firstly introduced to transform a propositional rule-based knowledge base into a Petri net. A knowledge base is represented by a Petri net, and facts are represented by the initial marking. Thus, the consistency check of a knowledge base is equivalent to the reachability problem of Petri nets. The reachability of Petri nets can be decided by whether the state equation has a solution; hence the consistency check can also be implemented by algebraic approach. Furthermore, algorithms are introduced to revise a propositional rule-based knowledge base, as well as extended logic programming. Compared with related works, the algorithms presented in the paper are efficient, and the time complexities of these algorithms are polynomial.

Keyword Query over Error-Tolerant Knowledge Bases

With more and more knowledge provided by WWW, querying and mining the knowledge bases have attracted much research attention. Among all the queries over knowledge bases, which are usually modelled as graphs, a keyword query is the most widely used one. Although the problem of keyword query over graphs has been deeply studied for years, knowledge bases, as special error-tolerant graphs, lead to the results of the traditional defined keyword queries out of users＇ satisfaction. Thus, in this paper, we define a new keyword query, called confident r-clique, specific for knowledge bases based on the r-clique definition for keyword query on general graphs, which has been proved to be the best one. However, as we prove in the paper, finding the confident r-cliques is #P-hard. We propose a filtering-and-verification framework to improve the search efficiency. In the filtering phase, we develop the tightest upper bound of the confident r-clique, and design an index together with its search algorithm, which suits the large scale of knowledge bases well. In the verification phase, we develop an efficient sampling method to verify the final answers from the candidates remaining in the filtering phase. Extensive experiments demonstrate that the results derived from our new definition satisfy the users＇ requirement better compared with the traditional r-clique definition, and our algorithms are efficient.

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.

Multi-Domain Malicious Behavior Knowledge Base Framework for Multi-Type DDoS Behavior Detection

Due to the many types of distributed denial-of-service attacks(DDoS)attacks and the large amount of data generated,it becomes a chal-lenge to manage and apply the malicious behavior knowledge generated by DDoS attacks.We propose a malicious behavior knowledge base framework for DDoS attacks,which completes the construction and application of a multi-domain malicious behavior knowledge base.First,we collected mali-cious behavior traffic generated by five mainstream DDoS attacks.At the same time,we completed the knowledge collection mechanism through data pre-processing and dataset design.Then,we designed a malicious behavior category graph and malicious behavior structure graph for the characteristic information and spatial structure of DDoS attacks and completed the knowl-edge learning mechanism using a graph neural network model.To protect the data privacy of multiple multi-domain malicious behavior knowledge bases,we implement the knowledge-sharing mechanism based on federated learning.Finally,we store the constructed knowledge graphs,graph neural network model,and Federated model into the malicious behavior knowledge base to complete the knowledge management mechanism.The experimental results show that our proposed system architecture can effectively construct and apply the malicious behavior knowledge base,and the detection capability of multiple DDoS attacks occurring in the network reaches above 0.95,while there exists a certain anti-interference capability for data poisoning cases.

Capacity Set Optimization of Knowledge-Based Enterprise and Its Organizational IQ

In this paper, the knowledge based enterprise is considered as an organism, which possesses a set of capabilities. The organizational structure model of knowledge based enterprise organism is described in order to possess the essential capacity set. A dynamic capacity set is defined and analyzed based on the definition of the growth and development for knowledge based enterprise organism. The structure of the capacity base, a subset of the capacity set, is optimized for different periods of the organism ...

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

Construction and realization of the knowledge base and inference engine of an IDSS model for air-conditioning cooling/heating sources selection

The knowledge representation mode and inference control strategy were analyzed according to the specialties of air-conditioning cooling/heating sources selection. The constructing idea and working procedure for knowledge base and inference engine were proposed while the realization technique of the C language was discussed. An intelligent decision support system (IDSS) model based on such knowledge representation and inference mechanism was developed by domain engineers. The model was verified to have a small kernel and powerful capability in list processing and data driving, which was successfully used in the design of a cooling/heating sources system for a large-sized office building.

Criteria Selecting Knowledge Base in the FuzzyController of the Electrohydraulic Position Control System

Aim To analyse the influence of knowledge base on the performance of the fuzzy controller of the electrohydraulic position control system,and to determine their selection cri- teria. Methods Experiments based on different membership functions,scaling factors and con-trol rules were done separately.The experiment results and the influence of different know- ledge base on the control performance were analysed in theory so that criteria of selcting knowledge base can be summarized correctly.Results Knowledge base,including membershipfunctions, scaling factors and control rules,has a crucial effect on the fuzzy control system.Suitably selected knowledge base can lead to good control performance of fuzzy control sys-tem. Conclusion Being symmetric,having an intersection ratio of 1 and satisfying width con- dition are three necessities for selecting membership functions.Selecting scaling factors dependson both the system requirement and a comprehensive analysis in the overshoot,oscillation, rising time and stability. Integrity and continuity must be guaranteed when determining control rules.

Intelligent search on integrated knowledge base of traditional Chinese medicine

To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machineunderstandable resources so that intelligent search can be supported. TCMSearch, a deployed intelligent search engine for traditional Chinese medicine （TCM）, is presented. The core of the system is an integrated knowledge base that uses a TCM domain ontology to represent the instances and relationships in TCM. Machine-learning techniques are used to generate semantic annotations for texts and semantic mappings for relational databases, and then a semantic index is constructed for these resources. The major benefit of representing the semantic index in RDF/OWL is to support some powerful reasoning functions, such as class hierarchies and relation inferences. By combining resource integration with reasoning, the knowledge base can support some intelligent search paradigms besides keyword search, such as correlated search, semantic graph navigation and concept recommendation.

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.

The Development of a Framework for CFD Validation and Best Practice: The QNET-CFD Knowledge Base

QNET-CFD is a thematic network on quality and trust for the industrial applications of Computational Fluid Dynamics （CFD）, developed under the European Union R＆D program. The main objectives of QNET-CFD were to collect CFD and experimental data in a systematic and quality controlled way and to set the basis for a consistent Knowledge Base in support of CFD guidance and validation. The QNET-CFD activity was organized around six Thematic Areas （TAs） covering the following industry sectors： external aerodynamics; combustion ＆ heat transfer; chemical process, thermal hydraulics and nuclear safety; civil construction ＆ HVAC; environment; turbomachinery internal flows. The main outcome of the QNET-CFD actions is the Knowledge Base （KB） with contains in a user oriented interface, extensive experimental and CFD data for a large number of test cases subdivided into 53 Application Challenges （AC） and 43 Underlying Flow Regimes （UFR）. The KB contains, in addition to state-of-the-art reviews for each of the six thematic areas, Best Practice Advice （BPA） in the use of CFD for most of AC. This is considered as a significant contribution form the QNET-CFD activities and it is expected that the level of the thrust and quality in CFD will hereby be improved.

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.

Construction of carbonate reservoir knowledge base and its application in fracture-cavity reservoir geological modeling

To improve the efficiency and accuracy of carbonate reservoir research,a unified reservoir knowledge base linking geological knowledge management with reservoir research is proposed.The reservoir knowledge base serves high-quality analysis,evaluation,description and geological modeling of reservoirs.The knowledge framework is divided into three categories:technical service standard,technical research method and professional knowledge and cases related to geological objects.In order to build a knowledge base,first of all,it is necessary to form a knowledge classification system and knowledge description standards;secondly,to sort out theoretical understandings and various technical methods for different geologic objects and work out a technical service standard package according to the technical standard;thirdly,to collect typical outcrop and reservoir cases,constantly expand the content of the knowledge base through systematic extraction,sorting and saving,and construct professional knowledge about geological objects.Through the use of encyclopedia based collaborative editing architecture,knowledge construction and sharing can be realized.Geological objects and related attribute parameters can be automatically extracted by using natural language processing(NLP)technology,and outcrop data can be collected by using modern fine measurement technology,to enhance the efficiency of knowledge acquisition,extraction and sorting.In this paper,the geological modeling of fracture-cavity reservoir in the Tarim Basin is taken as an example to illustrate the construction of knowledge base of carbonate reservoir and its application in geological modeling of fracture-cavity carbonate reservoir.

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.

KNOWLEDGE AND XML BASED CAPP SYSTEM

In order to enhance the intelligent level of system and improve the interaetivity with other systems, a knowledge and XML based computer aided process planning （CAPP） system is implemented. It includes user management, bill of materials（BOM） management, knowledge based process planning, knowledge management and database maintaining sub-systems. This kind of nesting knowledge representation method the system provided can represent complicated arithmetic and logical relationship to deal with process planning tasks. With the representation and manipulation of XML based technological file, the system solves some important problems in web environment such as information interactive efficiency and refreshing of web page. The CAPP system is written in ASP VBScript, JavaScript, Visual C＋＋ languages and Oracle database. At present, the CAPP system is running in Shenyang Machine Tools. The functions of it meet the requirements of enterprise production.

Knowledge expression and reasoning process in an expert system for welding procedure qualification

After analyzing the welding procedure knowledge in Chinese national standards for welding procedure qualification of steel pressure vessel from the point of establishing expert system, it can be divided into five types of knowledge, i. e. practice, definition, regularity, process and description knowledge. The knowledge expression methods are established according to the different type of welding procedure knowledge. The reasoning process based on rule is adopted. And the reasoning engine is embedded among objects integrated with the knowledge base.

Design of Multi-attribute Knowledge Base Based on Hybrid Knowledge Representation

Based on the knowledge representation and knowledge reasoning, this paper addresses the creation of the multi-attribute knowledge base on the basis of hybrid knowledge representation, with the help of object-oriented programming language and relational database. Compared with general knowledge base, multi-attribute knowledge base can enhance the ability of knowledge processing and application; integrate the heterogeneous knowledge, such as model, symbol, case-based sample knowledge; and support the whole decision process by integrated reasoning.