A natural language front-end for knowledge acquisition

Acquisition and validation of software requirements

Knowledge acquisition through international outsourcing alliances often plays a key in increasing a firm’s technology capability. However, knowledge acquisition is not an easy process, as it relates to the firm’s learning ability, the quality of the partnership, cross-national communication, and knowledge ambiguity, etc. Few studies discuss knowledge acquisition from the perspective of suppliers in newly industrialized economies, and often overlook the usefulness of information technology in the learning process. As a result, there are still some research gaps on this topic. Therefore, this study adopts the supplier point of view to explore the differences between suppliers with more or less effective knowledge acquisition. Using a sample of 204 cases of international outsourcing alliances, this study shows that learner characteristics, partnerships, and the use of information technology are the primary factors affecting supplier knowledge acquisition. This study also investigates the different learning patterns of tacit knowledge acquisition and explicit knowledge acquisition. Key words: suppliers, international outsourcing alliance, knowledge acquisition, learner characteristics, partnership, informational technology.

Knowledge acquisition begins with identifying knowledge in an organization's external environment and concludes with transforming that knowledge into a representation that can be used by the organization. This can manifest as any of 10 kinds of activities belonging to the knowledge acquisition class. This paper identifies and tests each kind of knowledge acquisition activity and finds empirical support for the propositions that the kinds of activities in the knowledge acquisition class can be performed in ways contributing to an organization's competitiveness. Additionally, for 9 of the 10 knowledge acquisition activities, the study offers empirical support that when technology is more important in the way activities are performed, those activities yield more strongly positive competitive impacts. Implications for practitioners and researchers are discussed. Copyright © 2015 John Wiley & Sons, Ltd.

Requirements engineering is a critical phase in software development. Errors committed in the requirements, if not found and corrected early in the engineering process, become costly problems later on. Artifacts described in natural language are the most suitable for both parties that participate in the process: experts of the domain and the software development team. Nevertheless, natural language can have some issues because the diversity of participants writing and reading specification. Thus, it is necessary to guide and assist the production of these artifacts. Kernel sentences is a linguistic definition about small sentences (with only one verb) written in active voice. We argue that kernel sentences are adequate to use in the acquisition of knowledge. Then, conceptual model are a good technique to summarize a large and complex specification to provide an overview to the whole group of participants. This paper proposes an approach to acquire specification collaboratively checking the writing style to conform kernel sentences, and synthetizing a conceptual model to summarize the specification to help participants to have an overview of the knowledge gathered in order to provide a consistent and coherent model. The process proposed has two main activities: acquisition of the knowledge checking its style and synthesis of the conceptual model. This paper also describes a prototype that uses natural language processing and artificial intelligence tools to support the process. Finally, the paper shows the result of a preliminary evaluation with promising results about the applicability of the process.

Based on organizational learning theory and cooperative relationship theory, the influencing mechanism of relational mechanisms on firms' innovation performance is explored. The results show that relational mechanisms and knowledge acquisition have significantly positive correlation. Knowledge acquisition positively affects knowledge dissemination. Absorptive capacity moderates the effect of knowledge acquisition on knowledge dissemination. Knowledge acquisition and knowledge dissemination promote firm's innovation performance.

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

One of the greatest challenges in building an expert system is obtaining, representing, and programming the knowledge base. As the size and scope of the problem domain increases, knowledge acquisition and knowledge engineering become more challenging. Methods for knowledge acquisition and engineering for large-scale projects are investigated in this paper. The objective is to provide new insights as to how knowledge engineers play a role in defining the scope and purpose of expert systems and how traditional knowledge acquisition and engineering methods might be recast in cases where the expert system is a component within a larger scale client-server application targeting multiple users.

Knowledge acquisition and natural language processing are two fields of Artificial Intelligence that have much to offer each other. Natural language requires such large amounts of knowledge that it will probably be necessary to automate the acquisition process for this field to achieve its goals. Machine learning has focused on incremental improvements of performance; but the acquisition of knowledge is probably more of a key bottleneck for building intelligent systems. Huge volumes of knowledge are available now, in machine readable form, if only we could understand how to use it. Natural language processing technology holds the key to this storehouse.

A firm's collaborative context can represent an important source of knowledge for sustaining its entrepreneurial outcomes. Drawing on entrepreneurship and resource-based view literature, we study how the acquisition of knowledge from inter-firm ties affects the relationship between entrepreneurial orientation and performance, distinguishing between two types of knowledge, market and technological, and between two types of entrepreneurial orientation, radical and incremental. The results of the study on a sample of 203 Spanish small businesses from the Information and Communication Technology (ICT) sector suggest that knowledge acquisition plays different roles, depending on the type of entrepreneurial orientation developed and also on the type of knowledge exchanged. In particular, market and technological knowledge acquisition positively moderate only the relationship between radical entrepreneurial orientation and performance, while in the case of incremental entrepreneurial orientation we find only a direct positive influence of market knowledge acquisition on performance.

Mothers' beliefs about the role of parents and of teachers in the child's acquisition of knowledge in five domains were investigated in a sample of families from Tanzania and from the United States. Tanzanian and US mothers differed in their views of the importance of parents and teachers in the child's acquisition of language, sociopolitical, and historical knowledge, but not in the domains of science or mathematics knowledge. These differences were linked in predictable ways to the cultural traditions of the two countries. Teacher ratings of children's classroom behaviours were also examined across cultures and in relation to mothers' beliefs. Tanzanian children were rated as more dependent than the US children were, and as less independent, creative, and intelligent. United States and Tanzanian mothers' beliefs about the teachers' role in children's historical knowledge showed the most consistent relationship to children's classroom behaviours. For Tanzanian children's classroom behaviour, several other maternal beliefs were related to classroom ratings in addition to beliefs relative to the acquisition of historical knowledge.

A central problem for the design of knowledge-based systems is the so-called knowledge acquisition bottleneck. Knowledge acquisition is crucial for the quality and efficiency of a planned knowledge-based system. These systems are always based on one or several knowledge bases, whose contents are an influential quality factor for the system's result. For that reason, it is evident that a consistent approach to knowledge acquisition is of great importance. In relation to the problem domain the knowledge-based system is intended for, the knowledge acquisition can be highly problematic. While, for example, in technical problem domains, the access to expert knowledge can usually be achieved in an acceptable manner, the acquisition of medical knowledge for formalisation and usage in an information system is not possible without difficulty.

Answer set Prolog, or AnsProlog in short, is one of the leading knowledge representation (KR) languages with a large body of theoretical and building block results, several implementations and reasoning and declarative problem solving applications. But it shares the problem associated with knowledge acquisition with all other KR languages; most knowledge is entered manually by people and that is a bottleneck. Recent advances in natural language processing have led to some systems that convert natural language sentences to a logical form. Although these systems are in their infancy, they suggest a direction to overcome the above mentioned knowledge acquisition bottleneck. In this paper we discuss some recent work by us on developing applications that process logical forms of natural language text and use the processed result together with AnsProlog rules to do reasoning and problem solving.

Much of today’s organizational knowledge still exists outside of formal information repositories and often only in people’s heads. While organizations are eager to capture this knowledge, existing acquisition methods are not up to the task. Neither traditional artificial intelligence-based approaches nor more recent, less-structured knowledge management techniques have overcome the knowledge acquisition challenges. This article investigates knowledge acquisition bottlenecks and proposes the use of collaborative, conversational knowledge management to remove them. The article demonstrates the opportunity for more effective knowledge acquisition through the application of the principles of Bazaar style, open-source development. The article introduces wikis as software that enables this type of knowledge acquisition. It empirically analyzes the Wikipedia to produce evidence for the feasibility and effectiveness of the proposed approach.

Much of today’s organizational knowledge still exists outside of formal information repositories and often only in people’s heads. While organizations are eager to capture this knowledge, existing acquisition methods are not up to the task. Neither traditional artificial intelligence-based approaches nor more recent, less-structured knowledge management techniques have overcome the knowledge acquisition challenges. This article investigates knowledge acquisition bottlenecks and proposes the use of collaborative, conversational knowledge management to remove them. The article demonstrates the opportunity for more effective knowledge acquisition through the application of the principles of Bazaar style, open-source development. The article introduces wikis as software that enables this type of knowledge acquisition. It empirically analyzes the Wikipedia to produce evidence for the feasibility and effectiveness of the proposed approach.Request access from your librarian to read this chapter's full text.

Evaluating the success of a knowledge acquisition (KA) task is difficult and expensive. Most evaluation approaches rely on the expert themselves, either directly, or indirectly by relying on data previously prepared with the help of experts. In incremental KA, knowledge base (KB) errors are monitored and corrected by an expert. Thus, during its evolution a record of the knowledge based system (KBS) performance is usually easy to keep. We propose to integrate with the incremental KA process, an evaluation process based on a statistical analysis to estimate the effectiveness of the KBS, as the KBS is actually evolved. We tailor such an analysis for Ripple Down Rules (RDR), which is an effective incremental KA methodology where a record of the KBS performance can be easily derived and updated as new cases are processed by the system. An RDR KB is a collection of rules with hierarchical exceptions, which are entered and validated by the expert in the context of their use. This greatly facilitates the knowledge maintenance task which, characteristically in RDR, overlaps with the incremental KA process. The work in this paper aims to overlap evaluation with maintenance and development of the knowledge base. It also minimises the major expense in deploying the RDR KBS, that of keeping a domain expert on-line during maintenance and the initial period of deployment. The expert is not kept on-line longer than it is absolutely necessary. We use the structure and semantics of an evolving RDR KB, combined with proven machine learning statistical methods, to estimate the added value in every KB update, as the KB evolves. Using these values, the decisionmakers in the organisation employing the KBS can apply a cost-benefit analysis of the continuation of the incremental KA process. They can then determine when this process, involving keeping an expert on-line, should be terminated.