Skeleton parsing for complex question answering over knowledge bases

Answering complex questions involving multiple relations over knowledge bases is a challenging task. Many previous works rely on dependency parsing. However, errors in dependency parsing would influence their performance, in particular for long complex questions. In this paper, we propose a novel skeleton grammar to represent the high-level structure of a complex question. This lightweight formalism and its BERT-based parsing algorithm help to improve the downstream dependency parsing. To show the effectiveness of skeleton, we develop two question answering approaches: skeleton-based semantic parsing (called SSP) and skeleton-based information retrieval (called SIR). In SSP, skeleton helps to improve structured query generation. In SIR, skeleton helps to improve path ranking. Experimental results show that, thanks to skeletons, our approaches achieve state-of-the-art results on three datasets: LC-QuAD 1.0, GraphQuestions, and ComplexWebQuestions 1.1.     

基于表示学习的知识库问答研究进展与展望

面向知识库的问答(Question answering over knowledge base, KBQA)是问答系统的重要组成. 近些年, 随着以深度学习为代表的表示学习技术在多个领域的成功应用, 许多研究者开始着手研究基于表示学习的知识库问答技术. 其基本假设是把知识库问答看做是一个语义匹配的过程. 通过表示学习知识库以及用户问题的语义表示, 将知识库中的实体、关系以及问句文本转换为一个低维语义空间中的数值向量, 在此基础上, 利用数值计算, 直接匹配与用户问句语义最相似的答案. 从目前的结果看, 基于表示学习的知识库问答系统在性能上已经超过传统知识库问答方法. 本文将对现有基于表示学习的知识库问答的研究进展进行综述, 包括知识库表示学习和问句(文本)表示学习的代表性工作, 同时对于其中存在难点以及仍存在的研究问题进行分析和讨论.     

Time-evolving rule-based knowledge bases

Human knowledge in any expertise area changes with respect to time. Two types of such knowledge can be identified, time independent and time dependent. It is shown that the maintenance effort of the latter is harder than that of the former. The present paper applies research results in the area of temporal databases, in order to maintain a rule-based knowledge base whose content changes with respect to the real world time. It is shown that the approach simplifies the maintenance of time dependent knowledge. It also enables the study of the evolution of knowledge with respect to time, which is knowledge on its own. Three distinct solutions are actually proposed and evaluated. Their common characteristic is that knowledge is stored in a database; therefore, all the advantages of databases are inherited by knowledge bases. Implementations are also reported.     

Migrant knowledge workers: An empirical study of global Sydney as a knowledge city

Global Sydney is essentially a knowledge city. The global Sydney thesis has focused on Sydney’s performances in the knowledge-based economy, especially the advanced producer services, in an increasingly integrated world economy. Sydney’s emergence as a global city has been inseparable from its migrants from overseas and elsewhere in Australia. This study aims to bridge the theses of global Sydney and migration. The focus is on migrant knowledge workers employed in the knowledge-intensive industries and highly skilled occupations, which are the most reflective of Sydney’s knowledge capacity. Using data from the 2011 Australian Census and a community survey in the Sydney region, this study reveals new insights into the different patterns between international and internal migrant knowledge workers in terms of social–economic backgrounds, drivers for moving to Sydney, and employment before and in Sydney. The findings are useful to better understand migrant knowledge workers in global Sydney, and to contribute to the global city discourse and knowledge city research.     

SINA: Semantic interpretation of user queries for question answering on interlinked data

The architectural choices underlying Linked Data have led to a compendium of data sources which contain both duplicated and fragmented information on a large number of domains. One way to enable non-experts users to access this data compendium is to provide keyword search frameworks that can capitalize on the inherent characteristics of Linked Data. Developing such systems is challenging for three main reasons. First, resources across different datasets or even within the same dataset can be homonyms. Second, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain user query. Finally, constructing a federated formal query from keywords across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present Sina, a scalable keyword search system that can answer user queries by transforming user-supplied keywords or natural-languages queries into conjunctive SPARQL queries over a set of interlinked data sources. Sina uses a hidden Markov model to determine the most suitable resources for a user-supplied query from different datasets. Moreover, our framework is able to construct federated queries by using the disambiguated resources and leveraging the link structure underlying the datasets to query. We evaluate Sina over three different datasets. We can answer 25 queries from the QALD-1 correctly. Moreover, we perform as well as the best question answering system from the QALD-3 competition by answering 32 questions correctly while also being able to answer queries on distributed sources. We study the runtime of SINA in its mono-core and parallel implementations and draw preliminary conclusions on the scalability of keyword search on Linked Data.     

Determinants of firms' knowledge management system implementation: An empirical study

As knowledge becomes an increasingly valuable and important organizational asset, many firms anticipate that implementing the knowledge management systems (KMS) will effectively support and enhance organizational knowledge management activities. Even some firms regard KMS as an emerging and powerful source of competitive advantages.However, the implementation of KMS differs from that of traditional enterprise information systems. The implementation of KMS is difficult and risky since these systems are unstructured and so technologically innovative. Thus, effort is required to identify determinants affecting KMS implementation in businesses.Based on innovation diffusion theory and technology-organization-environment framework, this study develops and tests an integrated model of knowledge management systems implementation for businesses. Survey data were collected from 291 businesses in Taiwan. Confirmatory factor analysis and logistic regression technique were used test the hypothesized relationships. The results show that technological innovation factors (perceived benefits, complexity, and compatibility), organizational factors (top management support, organizational culture), and environmental factors (competitive pressure) are significant influences on KMS implementation in firms. Finally, the implications and future research on KMS implementation are discussed.     

Unsupervised named-entity extraction from the Web: An experimental study

The KnowItAll system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KnowItAll's novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KnowItAll extracted over 50,000 class instances, but suggested a challenge: How can we improve KnowItAll's recall and extraction rate without sacrificing precision?This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall (e.g., “chemist” and “biologist” are identified as sub-classes of “scientist”). List Extraction locates lists of class instances, learns a “wrapper” for each list, and extracts elements of each list. Since each method bootstraps from KnowItAll's domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KnowItAll a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.     

Understanding knowledge sharing activities in free/open source software projects: An empirical study

Free/Open Source Software (F/OSS) projects are people-oriented and knowledge intensive software development environments. Many researchers focused on mailing lists to study coding activities of software developers. How expert software developers interact with each other and with non-developers in the use of community products have received little attention. This paper discusses the altruistic sharing of knowledge between knowledge providers and knowledge seekers in the Developer and User mailing lists of the Debian project. We analyze the posting and replying activities of the participants by counting the number of email messages they posted to the lists and the number of replies they made to questions others posted. We found out that participants interact and share their knowledge a lot, their positing activity is fairly highly correlated with their replying activity, the characteristics of posting and replying activities are different for different kinds of lists, and the knowledge sharing activity of self-organizing Free/Open Source communities could best be explained in terms of what we called “Fractal Cubic Distribution” rather than the power-law distribution mostly reported in the literature. The paper also proposes what could be researched in knowledge sharing activities in F/OSS projects mailing list and for what purpose. The research findings add to our understanding of knowledge sharing activities in F/OSS projects.     

VDEA词典的构建及其在情感倾向性分析中的应用

配价语法主要以谓词为中心研究句子的深层语义结构,重点描述动词和形容词与搭配成分间的依存关系,是解决语义分析处理这个颈瓶问题的利器。以英语形容词为主体,构建了包含相关配价信息的形容词配价词典,词典包含3170个英语形容词的配价关系、格关系、释义、褒贬义、语义分类、语义特征和相关例句等信息。此外,基于该词典设计了词汇情感倾向性分析模型,取得了很好的实验结果。     

Lp, a logic for representing and reasoning with statistical knowledge

This paper presents a logical formalism for representing and reasoning with statistical knowledge. One of the key features of the formalism is its ability to deal with qualitative statistical information. It is argued that statistical knowledge, especially that of a qualitative nature, is an important component of our world knowledge and that such knowledge is used in many different reasoning tasks. The work is further motivated by the observation that previous formalisms for representing probabilistic information are inadequate for representing statistical knowledge. The representation mechanism takes the form of a logic that is capable of representing a wide variety of statistical knowledge, and that possesses an intuitive formal semantics based on the simple notions of sets of objects and probabilities defined over those sets. Furthermore, a proof theory is developed and is shown to be sound and complete. The formalism offers a perspicuous and powerful representational tool for statistical knowledge, and a proof theory which provides a formal specification for a wide class of deductive inferences. The specification provided by the proof theory subsumes most probabilistic inference procedures previously developed in AI. The formalism also subsumes ordinary first-order logic, offering a smooth integration of logical and statistical knowledge.     

Fuzzy linguistic data bases: An application

This paper discusses the empirical development and use of a fuzzy linguistic data base which is relational in character. Unlike conventional fuzzy searches for crisp data, we search a data base for fuzzy information. The data base supports another code which does fuzzy aggregation of data. It has been applied to support decision making connected with the construction of military budgets for command, control and communications (C3).     

R-Calculus for ELP:An Operational Approach to Knowledge Base Maintenance

Sme concepts used in knowledge base maintenace,such as sequence,new law,user‘s rejection and reconstructions of a knowledge base,are first introduced,and then a framework for extended logic programming(ELP)is given,where an extended logic program is equivalent to a knowledge base.A transition system called R-calculus for ELP is provided.For a given knowledge base and a user‘s rejection,the R-calculus for ELP will deduce best revisions of the base.The soundness and the completeness of the R-calculus for ELP are proved,and the R-calculus for ELP is implemented in Prolog.In addition,the research is compared with other relevant work.     

The training of knowledge structures for manufacturing tasks: an empirical study

Abstract

Knowledge structure refers to the manner in which a human organizes knowledge with a given domain. Research has identified knowledge structure as a determinant of the human ability to perform cognitive-oriented tasks. Yet uncertainty still exists about how to improve an individual's cognitive task performance through the controlled utilization of the individual's knowledge structure. The purpose of this study is to investigate whether the development of individual's knowledge structure in a particular domain can be manipulated through training. The experiment utilized the manufacturing domain of plastic extrusion machine operation. Sixteen subjects, having no previous knowledge of the domain, were randomly assigned to one of two experimental groups. Each of the experimental groups corresponded to a distinct training condition. Over a three-day period, both training groups received the same instructional content; however, the sequence in which the training material was presented differed. One group initially received the abstract, conceptual relationships between domain concepts, followed by more detailed relationships associated with the lower level aspects of the domain. The other group received the training material in the reverse order; i.e. the lower level information followed by the abstract. Prior to and concluding the training sessions, each individual's knowledge structure was assessed along two dimensions, hierarchical levels and multiple relations, through a computer-based measurement technique.entitled KSAT. The group which received the abstract relationships first showed significant improvement following training along both dimensions of knowledge structure. No significant changes in the knowledge structure dimensions were found for the group which received the lower level relationships first. This study suggests that an individual's knowledge structure can be manipulated through training, with a significant effect being attributed to the training sequence of abstract material followed by the more detailed material.     

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.     

Knowledge acquisition and knowledge representation with class: the object-oriented paradigm

Knowledge acquisition and knowledge representation are the fundamental building blocks of knowledge-based systems (KBSs). How to efficiently elicit knowledge from experts and transform this elicited knowledge into a machine usable format is a significant and time consuming problem for KBS developers. Object-orientation provides several solutions to persistent knowledge acquisition and knowledge representation problems including transportability, knowledge reuse, and knowledge growth. An automated graphical knowledge acquisition tool is presented, based upon object-oriented principles. The object-oriented graphical interface provides a modeling platform that is easily understood by experts and knowledge engineers. The object-oriented base for the automated KA tool provides a representation independent methodology that can easily be mapped into any other object-oriented expert system or other object-oriented intelligent tools.     

An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models

An important objective of data mining is the development of predictive models. Based on a number of observations, a model is constructed that allows the analysts to provide classifications or predictions for new observations. Currently, most research focuses on improving the accuracy or precision of these models and comparatively little research has been undertaken to increase their comprehensibility to the analyst or end-user. This is mainly due to the subjective nature of ‘comprehensibility’, which depends on many factors outside the model, such as the user's experience and his/her prior knowledge. Despite this influence of the observer, some representation formats are generally considered to be more easily interpretable than others. In this paper, an empirical study is presented which investigates the suitability of a number of alternative representation formats for classification when interpretability is a key requirement. The formats under consideration are decision tables, (binary) decision trees, propositional rules, and oblique rules. An end-user experiment was designed to test the accuracy, response time, and answer confidence for a set of problem-solving tasks involving the former representations. Analysis of the results reveals that decision tables perform significantly better on all three criteria, while post-test voting also reveals a clear preference of users for decision tables in terms of ease of use.     

基于多知识源的语义搭配知识库的构建及应用

针对汉语文本语义搭配错误,在对《现代汉语语义信息词典》(以下简称《语义词典》)及《知网》相关属性的研究基础上,提出了基于多知识源的语义搭配知识库的构建方法,并设计出了面向汉语文本语义查错的三层语义搭配知识库结构.在此基础上,设计并实现了一个汉语文本自动查错算法.实验结果表明,基于上述方法所设计的语义搭配知识库,可以大大提高汉语文本语义查错算法错误查找的召回率,对于语义查错的研究具有一定的借鉴作用.     

An application of knowledge based modelling using scripts

Knowledge management (KM) takes an increasingly significant place in the companies. The field of the KM aims to answer the problems of memory within companies by proposing methodologies to formalise know-how during the different steps of production. The KM is a domain with many ramifications and applications. One of them, so called Knowledge Based Engineering, search how to record knowledge from experts to put them in CAD software. Our study relates to the possibility of using the knowledge of an expert in modelling and, more particularly, on the automatic modelling of filling systems in foundry.     

An empirical study of the effects of knowledge sharing and learning behaviors on firm performance

Knowledge sharing and learning behaviors in the workplace are believed to be very important to the success of firms. In this study, the relationships between knowledge sharing and learning behaviors, business process improvement, product and service offerings, and organizational performance are examined based on a sample of 134 firms engaged in manufacturing, and wholesale or retailing operations. Data analyses using the partial least squares statistical technique revealed that knowledge sharing and learning behaviors are positively associated with business process improvement, and product and service offerings. Business process improvement and product and service offerings are positively associated, and they in turn are positively related to organizational performance. The findings reinforce the importance of knowledge sharing and learning to companies. Executives should encourage knowledge management and organizational learning activities within their firms, and give proper considerations to the strategies and implementation of programs supporting these activities in order to enhance firm performance.