Formalisation method for the text expressed knowledge

This paper describes a method of knowledge representation as a set of text expressed statements. The method is based on the identification of word-categories/phrases and their semantic relationships within the observed statement. Furthermore, the identification of semantic relationships between words/phrases using wh-questions that clarify the role of the word/phrase in the relationship is described. A conceptual model of the computer system based on the formalization method of text-expressed knowledge is proposed. The subsystem text formalization is described in detail, especially its parts: syntactic analysis of the sentence, sentence formalization, phrase structure grammar and lexicon. The phrase structure grammar is formed by induction and it is used to generate the language of the formalized notation of a sentence. The derivation of grammar is based on the simple phrase structure grammar which was used for the syntactical analysis of informal language notation. In its base, the suggested method translates sentences of the informal language into formal language sentences which are generated by the derivated phrase structure grammar. Current limitations of the method that also set the path of its further development are shown. Next concrete steps in the development of the method are also described.     

一种融合语义知识和BiLSTM-CNN的短文本分类方法

为快速准确地从海量新闻中挖掘用户需求,解决短文本语义关系单薄、篇幅较短、特征稀疏问题,提出一种融合语义知识和BiLSTM-CNN的短文本分类方法.该分类模型将新闻短文本预处理成Word2Vec词向量,通过卷积神经网络提取代表性的局部特征,利用双向长短时记忆网络捕获上下文语义特征,再由Softmax分类器实现短文本分类.文章对体育、财经、教育、文化和游戏五大主题的新闻语料进行了实验性的分析.结果表明,融合语义知识和BiLSTM-CNN的短文本分类方法在准确率、召回率和F1值上均有所提升,该方法可以为短文本分类和推荐系统提供有效支撑.     

一种融合语义知识和BiLSTM-CNN的短文本分类方法

为快速准确地从海量新闻中挖掘用户需求,解决短文本语义关系单薄、篇幅较短、特征稀疏问题,提出一种融合语义知识和BiLSTM-CNN的短文本分类方法.该分类模型将新闻短文本预处理成Word2Vec词向量,通过卷积神经网络提取代表性的局部特征,利用双向长短时记忆网络捕获上下文语义特征,再由Softmax分类器实现短文本分类.文章对体育、财经、教育、文化和游戏五大主题的新闻语料进行了实验性的分析.结果表明,融合语义知识和BiLSTM-CNN的短文本分类方法在准确率、召回率和F1值上均有所提升,该方法可以为短文本分类和推荐系统提供有效支撑.     

A semantic term weighting scheme for text categorization

Traditional term weighting schemes in text categorization, such as TF-IDF, only exploit the statistical information of terms in documents. Instead, in this paper, we propose a novel term weighting scheme by exploiting the semantics of categories and indexing terms. Specifically, the semantics of categories are represented by senses of terms appearing in the category labels as well as the interpretation of them by WordNet. Also, the weight of a term is correlated to its semantic similarity with a category. Experimental results on three commonly used data sets show that the proposed approach outperforms TF-IDF in the cases that the amount of training data is small or the content of documents is focused on well-defined categories. In addition, the proposed approach compares favorably with two previous studies.     

Combining automatic acquisition of knowledge with machine learning approaches for multilingual temporal recognition and normalization

This paper presents an improvement in the temporal expression (TE) recognition phase of a knowledge based system at a multilingual level. For this purpose, the combination of different approaches applied to the recognition of temporal expressions are studied. In this work, for the recognition task, a knowledge based system that recognizes temporal expressions and had been automatically extended to other languages (TERSEO system) was combined with a system that recognizes temporal expressions using machine learning techniques. In particular, two different techniques were applied: maximum entropy model (ME) and hidden Markov model (HMM), using two different types of tagging of the training corpus: (1) BIO model tagging of literal temporal expressions and (2) BIO model tagging of simple patterns of temporal expressions. Each system was first evaluated independently and then combined in order to: (a) analyze if the combination gives better results without increasing the number of erroneous expressions in the same percentage and (b) decide which machine learning approach performs this task better. When the TERSEO system is combined with the maximum entropy approach the best results for F-measure (89%) are obtained, improving TERSEO recognition by 4.5 points and ME recognition by 7.     

Demand-driven knowledge acquisition method for enhancing domain ontology integrity

Knowledge, as the most important resource for the knowledge economy in the 21st century, is fundamental to enterprise competitive strength. Therefore, how to effectively integrate internal and external knowledge and provide correct knowledge to the right users in a timely fashion have become key success factors in business operation.     

A novel self-optimizing approach for knowledge acquisition

The attribute reduction and rule generation (the attribute value reduction) are two main processes for knowledge acquisition. A self-optimizing approach based on a difference comparison table for knowledge acquisition aimed at the above processes was proposed. In the attribute reduction process, the conventional logic computation was transferred to a matrix computation along with some added thoughts on the evolution computation used to construct the self-adaptive optimizing algorithm. In addition, some sub-algorithms and proofs were presented in detail. In the rule generation process, the orderly attribute value reduction algorithm (OAVRA), which simplified the complexity of rule knowledge, was presented. The approach provided an effective and efficient method for knowledge acquisition that was supported by the experimentation.     

A data representation code for text processing systems

A brief analysis of character strings and string processing is given. Text processing is defined as the processing of character strings to control not only the sequential relations among the characters, but also spatial and form relations among the symbols used to produce a physical display of the character string. Requirements are given for a data structure by which character strings may be represented to facilitate text processing, including independent manipulation of sequential, spatial, and form relations. A Text Processing Code (TPC) meeting these requirements is presented in detail. Several other coding schemes are examined and shown to be inadequate for text processing as defined here.     

Strategies for knowledge acquisition

This paper describes knowledge acquisition strategies developed in the course of handcrafting a diagnostic system and reports on their consequent implementation in MORE, an automated knowledge acquisition system. We describe MORE in some detail, focusing on its representation of domain knowledge, rule generation capabilities, and interviewing techniques. MORE's approach is shown to embody methods which may prove fruitful to the development of knowledge acquisition systems in other domains.     

A tool for task-based knowledge and specification acquisition

Knowledge acquisition has been identified as the bottleneck for knowledge engineering. One of the reasons is the lack of an integrated methodology that is able to provide tools and guidelines for the elicitation of knowledge as well as the verification and validation of the system developed. Even though methods that address this issue have been proposed, they only loosely relate knowledge acquisition to the remaining part of the software development life cycle. to alleviate this problem, we have developed a framework in which knowledge acquisition is integrated with system specifications to facilitate the verification, validation, and testing of the prototypes as well as the final implementation. to support the framework, we have developed a knowledge acquisition tool, TAME. It provides an integrated environment to acquire and generate specifications about the functionality and behavior of the target system, and the representation of the domain knowledge and domain heuristics. the tool and the framework, together, can thus enhance the verification, validation, and the maintenance of expert systems through their life cycles. © 1994 John Wiley & Sons, Inc.     

A neural network-based methodology for machining knowledge acquisition

The paper presents results of a study on collecting machining strategies for machining assistants and process planning. These efforts are being conducted at the NMSU-Integrated Manufacturing Systems Laboratory (IMSL). Goals of the project aim at improving and advancing the solicitation, documentation, and automation of machining knowledge/data acquisition, and integration with CAD/CAM/CAE systems. This paper emphasizes the knowledge acquisition phase of the study utilizing artificial neural networks.     

A vague-rough set approach for uncertain knowledge acquisition

By combining both vague sets and rough sets in fuzzy data processing, we propose a vague-rough set approach for extracting knowledge under uncertain environments. We compute all attribute reductions using the vague-rough lower approximation distribution, concepts of attribute reduction and the discernibility matrix in a vague decision information system (VDIS). Research results for extracting decision rules from the VDIS show the proposed approaches extend the corresponding method in classical rough set theory and provide a new avenue to uncertain vague knowledge acquisition.     

利用onto1ogy实现的多库知识获取方法

在ontology研究的基础上,提出了一种基于ontology的多库知识获取(OBMDKA)方法.考虑到不同用户有不同的表述习惯,引入自然语言理解(NLU)子系统和用户ontology,在正确理解用户语义的前提下方便用户查询.同一知识的表示形式是多种多样的,按不同的表示形式将其分别存放在不同的库中.利用领域ontology对待查找知识进行分类,使查找更加准确全面.     

A simple and efficient method for diagnosing equipment faults based on quantitative physical knowledge

This paper proposes a method for identifying and locating the origin of a fault of a piece of equipment using approximate linear quantitative equations of variable increments which are derived from equations representing the steady state of the equipment and measured values of the variables. The proposed method differs fundamentally from strict numerical simulation methods and qualitative methods. Although the solution which is obtained by the proposed method is approximate, it is devised so that errors which are contained in them can be reduced. The proposed method, because only linear equations are dealt with, has the advantage of being easy to implement and process and of taking much less computation time.     

Design considerations for knowledge acquisition

Expert systems are commercially becoming more prominent and available as the breadth of applications continues to expand, eventually they could very well become the most integral part of an organization's normal operations. Knowledge acquisition is often found to be the major problem in the development of expert systems. This paper places in order the framework for proper knowledge acquisition to ensure that knowledge which is the key for any expert system is acquired effectively from an expert.