Representing Software Engineering Knowledge

We argue that one important role that Artificial Intelligence can play in Software Engineering is to act as a source of ideas about representing knowledge that can improve the state-of-the-art in software information management, rather than just building intelligent computer assistants. Among others, such techniques can lead to new approaches for capturing, recording, organizing, and retrieving knowledge about a software system. Moreover, this knowledge can be stored in a software knowledge base, which serves as corporate memory, facilitating the work of developers, maintainers and users alike. We pursue this central theme by focusing on requirements engineering knowledge, illustrating it with ideas originally reported in (Greenspan et al., 1982; Borgida et al., 1993; Yu, 1993) and (Chung, 1993b). The first example concerns the language RML, designed on a foundation of ideas from frame- and logic-based knowledge representation schemes, to offer a novel (at least for its time) formal requirements modeling language. The second contribution adapts solutions of the frame problem originally proposed in the context of AI planning in order to offer a better formulation of the notion of state change caused by an activity, which appears in most formal requirements modeling languages. The final contribution imports ideas from multi-agent planning systems to propose a novel ontology for capturing organizational intentions in requirements modeling. In each case we examine alterations that have been made to knowledge representation ideas in order to adapt them for Software Engineering use.     

METABANK: A KNOWLEDGE-BASE OF METAPHORIC LANGUAGE CONVENTIONS

The frequent and conventional use of nonliteral language has been a major stumbling block for natural language processing systems since the early machine translation efforts. Metaphor, metonymy, and indirect speech acts are among the most troublesome phenomena. Recent computational efforts addressing these problems have taken an approach that emphasizes the use of systematic knowledge about nonliteral language conventions. We are currently engaged in an effort to supply this knowledge in the case of conventional metaphor. We are constructing MetaBank: an empirically derived and theoretically motivated knowledge-base of English metaphorical conventions. This article describes our three-part approach to the construction of MetaBank: the collection of on-line textual resources and databases of linguistic generalizations, the development of a methodology for analyzing these resources, and the construction of a knowledge-base based on the preceding analyses.     

Dynamic interpretation and hoare deduction

In this paper we present a dynamic assignment language which extends the dynamic predicate logic of Groenendijk and Stokhof [1991: 39–100] with assignment and with generalized quantifiers. The use of this dynamic assignment language for natural language analysis, along the lines of o.c. and [Barwise, 1987: 1–29], is demonstrated by examples. We show that our representation language permits us to treat a wide variety of donkey sentences: conditionals with a donkey pronoun in their consequent and quantified sentences with donkey pronouns anywhere in the scope of the quantifier. It is also demonstrated that our account does not suffer from the so-called proportion problem.Discussions about the correctness or incorrectness of proposals for dynamic interpretation of language have been hampered in the past by the difficulty of seeing through the ramifications of the dynamic semantic clauses (phrased in terms of input-output behaviour) in non-trivial cases. To remedy this, we supplement the dynamic semantics of our representation language with an axiom system in the style of Hoare. While the representation languages of barwise and Groenendijk and Stokhof were not axiomatized, the rules we propose form a deduction system for the dynamic assignment language which is proved correct and complete with respect to the semantics.Finally, we define the static meaning of a program of the dynamic assignment language as the weakest condition such that terminates successfully on all states satisfying , and we show that our calculus gives a straightforward method for finding static meanings of the programs of the representation language.     

How well does a vanilla loop fit into a frame?

The frame language FRL is compared with some newer object-oriented languages, namely LOOPS, the Flavor System and the Poor Man's Flavor System. The kernel features are investigated in detail and a simulation of FRL in the Flavor System is performed. The conclusion is that in general the object-oriented languages cannot be easily utilized as a substitute for FRL frames because of the different view these languages have of objects, but there are important special cases where object-oriented languages are very similar to FRL.     

Conversation with and through computers

People design what they say specifically for their conversational partners, and they adapt to their partners over the course of a conversation. A comparison of keyboard conversations involving a simulated computer partner (as in a natural language interface) with those involving a human partner (as in teleconferencing) yielded striking differences and some equally striking similarities. For instance, there were significantly fewer acknowledgments in human/computer dialogue than in human/human. However, regardless of the conversational partner, people expected connectedness across conversational turns. In addition, the style of a partner's response shaped what people subsequently typed. These results suggest some issues that need to be addressed before a natural language computer interface will be able to hold up its end of a conversation.     

Event detection in online social network: Methodologies,state-of-art,and evolution

Online social network such as Twitter, Facebook and Instagram are increasingly becoming the go-to medium for users to acquire information and discuss what is happening globally. Understanding real-time conversations with masses on social media platforms can provide rich insights into events, provided that there is a way to detect and characterise events. To this end, in the past twenty years, many researchers have been developing event detection methods based on the data collected from various social media platforms. The developed methods for discovering events are generally modular in design and novel in scale and speed. To review the research in this field, we line up existing works for event detection in online social networks and organise them to provide a comprehensive and in-depth survey. This survey comprises three major parts: research methodologies, the review of state-of-the-art literature and the evolution of significant challenges. Each part is supposed to attract readers with different motivations and expectations on the ‘things’ delivered in this survey. For example, the methodologies provide the life-cycle to design new event detection models, from data collection to model evaluations. A timeline and a taxonomy of existing methods are also introduced to elaborate the development of various technologies under the umbrella of event detection. These two parts benefit those with a background in event detection and want to commit a deep exploration of existing models such as discussing their pros and cons alike. The third part shows the development of the major open issues in this field. It also indicates the milestones of each challenge in terms of typical models. Our survey can contribute to the community by highlighting possible new problem statements and opening new research directions.     

Querying Documents using Content, Structure and Properties

Much information is nowadays stored electronically in document bases. Users retrieve information from these document bases by browsing and querying. While a large number of tools are available nowadays, not much work has been done on tools that support queries involving all the characteristics of documents as well as the use of domain knowledge during the search for information. In this paper we propose a query language that allows for querying documents using content information, information about the logical structure of the documents as well as information about properties of the documents. Domain knowledge is taken into account during the search as well. We also present an architecture for a system supporting such a language and we describe a prototype implementation together with test results.     

Optimizing Nested Loops Using Local CPS Conversion

Local CPS conversion is a compiler transformation for improving the code generated for nested loops by a direct-style compiler that uses recursive functions to represent loops. The transformation selectively applies CPS conversion at non-tail call sites, which allows the compiler to use a single machine procedure and stack frame for both the caller and callee. In this paper, we describe LCPS conversion, as well as a supporting analysis. We have implemented Local CPS conversion in the MOBY compiler and describe our implementation. In addition to improving the performance of loops, Local CPS conversion is also used to aid the optimization of non-local control flow by the MOBY compiler. We present results from preliminary experiments with our compiler that show significant reductions in loop overhead as a result of Local CPS conversion.     

基于领域词典的文本特征表示

为提高文本分类性能,提出一种结合机器学习和领域词典的文本特征表示方法．基于领域词典的文本特征表示方法可以增强文本特征表示能力。并降低文本特征空间维数,但是领域词典存在覆盖度不足的问题．为此,提出一种学习模型——自划分模型——来解决这个覆盖度不足的问题．实验结果表明,采用基于自划分模型的领域特征属性作为文本特征。可以提高文本分类性能,特别是特征数目少的情况下,该方法表现出很好的分类效果．相对于传统词文本特征方法。在特征数为500时分类的F1值提高6．58％．     

通用型CAPP专家系统中知识表示和推理机制的研究

ＣＡＰＰ作为ＣＡＤ与ＣＡＭ的中间桥梁，是ＣＩＭＳ必不可少的重要环节．本文针对开发通用性ＣＡＰＰ专家系统过程中，产品零件表示和工艺规程设计这两个关键问题，根据事物之间的相似性原理，提出了面向对象的产品零件和工艺规程的知识表示方法，以满足ＣＡＰＰ系统对产品零件信息表示的完整性、通用性、逻辑可分解性、动态性和相容性等要求，并在此基础上提出了ＣＡＰＰ专家系统的混合推理机制，包括单继承、多继承、非单调继承、类比推理等．     

计算机视觉模型中的知识及其表示

计算机视觉模型是计算机完成视觉任务时所要具备的知识.根据任务的需要,确定模型中应当具有哪些知识并选择合适的表示方法,是建立一个计算机视觉系统首先要解决的问题,并将从根本上影响系统的性能.该文将这些知识分为几何知识、物理知识和关系三种类型,概括论述了各类知识的作用和现有的各种表示方法,并特别指出了物理知识在视觉模型中的重要性.     

Declarative representation of strategic control knowledge

Strategic (control) knowledge typically specifies how a target task is solved. Representing such knowledge declaratively remains a difficult and practical knowledge engineering challenge. The key to addressing this challenge rests on two observations. One, strategic knowledge comprises two finer types of knowledge: subgoaling knowledge used to construct the goal structure for each problem situation pertaining to a target task, and goal-sequencing knowledge used to choose which subgoal in this goal structure is to be pursued at any given moment. Second, when subgoaling knowledge is explicit and expressed in declarative ontological terms, it is possible to fully express goal-sequencing knowledge in the same declarative terms. Building on these observations, we achieve three things. First, we analyse several conventional knowledge-based applications whose subgoaling and goal-sequencing knowledge is implicit, showing that making their subgoaling knowledge explicit permits (re)representing their goal-sequencing knowledge declaratively. Among the applications analysed are MORE and NEOMYCIN. Second, upon studying the roles of goal-sequencing knowledge vis-á-vis subgoaling knowledge, we develop a declarative formalism for representing goal-sequencing knowledge. Finally, we discuss and illustrate key benefits from using our declarative formalism, including an enhanced ability to validate and reuse goal-sequencing knowledge.     

A metalogic programming approach: language,semantics and applications

Abstract

This paper presents a logic programming language of novel conception, called Reflective Prolog, which allows declarative metaknowledge representation and metareasoning. The language is defined by augmenting pure Prolog (Horn clauses) with capabilities of self-reference and logical reflection. Self-reference is designed as a quotation device (a carefully defined naming relation) which allows the construction of metalevel terms that refer to object-level terms and atoms. Logical reflection is designed as an unquotation mechanism (a distinguished truth predicate) which relates names to what is named, thus extending the meaning of domain predicates. The reflection mechanism is embodied in an extended resolution procedure which automatically switches the context between levels. This implicit reflection relieves the programmer from having to explicitly deal with control aspects of the inference process. The declarative semantics of a Reflective Prolog definite program P is provided in terms of the least reflective Herbrand model of P, characterized by means of a suitable mapping defined over the Herbrand interpretations of P. The extended resolution is proved sound and complete with respect to the least reflective Herbrand model. By illustrating Reflective Prolog solutions to an organic set of problems, and by discussing the main differences with respect to other approaches to logic metaprogramming, we show that the proposed language deploys, within its field of action, greater expressive and inferential power than those available till now. The interpreter of the language has been fully implemented. Because of its enhanced power, logic semantics and working interpreter, Reflective Prolog is offered as a contribution toward making the declarative approach of logic programming applicable to the development of increasingly sophisticated knowledge-based systems.     

Representing UNIX Domain Metaphors

The language used to describe technical domains like UNIX is filled with metaphor. An approach to metaphor, based on theexplicit representation of knowledge about metaphors, has beendeveloped. MIDAS (Metaphor Interpretation, Denotation, andAcquisition System) is a computer program that that has been developedbased upon this approach. MIDAS can be used to representknowledge about conventional metaphors, interpret metaphoric languageby applying this knowledge, and dynamically learn new metaphors asthey are encountered during normal processing.     

REASONING WITH BACKGROUND KNOWLEDGE–A THREE-LEVEL THEORY

We address the problem of reasoning in cases when knowledge bases containing background knowledge are understood not as sets of formulas (rules and facts) but as collections of partially ordered theories. In our system, the usual, two-part logical structures, consisting of a metalevel and an object level, are augmented by a third level–a referential level. The referential level is a partially ordered collection of theories; it encodes background knowledge. As usual, current situations are described on the object level, and the metalevel is a place for rules that can eliminate some of the models permitted by the object level and the referential level. As a logic of reasoning the system generalizes the standard model of a rational agent: deducing actions and deriving new information about the world from a logical theory–its knowledge base. It is a natural logical system in which priorities on the possible readings of predicates, not special rules of inference, are the main source of nonmonotonicity. We introduce a theory forming operator PT(x) to exploit the priorities, and we investigate its basic logical properties. Then we show how such a system can be augmented by metarules. Although this paper concentrates on basic logical properties of the new theory, this formalism has already been applied to model a number of natural language phenomena such as the notion of text coherence, Gricean maxims, vagueness, and a few others. The paper also briefly compares it with the model of background knowledge of CYC, as proposed by Lenat and Guha.     

关于知识工程的发展综述

人工智能与计算机技术的结合产生了所谓"知识工程",本文首先从知识工程的基本概念入手,依次对其包含的知识的获取、知识的表示和知识的运用三大要素进行详细的总结概述,同时提出将知识管理加入知识工程对于其实戢应用的巨大作用.     

Shape Analysis Using the Auto Diffusion Function

Scalar functions defined on manifold triangle meshes is a starting point for many geometry processing algorithms such as mesh parametrization, skeletonization, and segmentation. In this paper, we propose the Auto Diffusion Function (ADF) which is a linear combination of the eigenfunctions of the Laplace-Beltrami operator in a way that has a simple physical interpretation. The ADF of a given 3D object has a number of further desirable properties: Its extrema are generally at the tips of features of a given object, its gradients and level sets follow or encircle features, respectively, it is controlled by a single parameter which can be interpreted as feature scale, and, finally, the ADF is invariant to rigid and isometric deformations.
We describe the ADF and its properties in detail and compare it to other choices of scalar functions on manifolds. As an example of an application, we present a pose invariant, hierarchical skeletonization and segmentation algorithm which makes direct use of the ADF.     

知识表示及企业本体建模研究

在企业建模领域引入本体思想,介绍了企业本体的概念、作用和意义,然后阐述了企业知识表示和组织策略。最后通过一个实例给出了本体建模的详细步骤和方法体系。基于本体的企业建模从根本上解决了企业的知识共享和重用问题。     

GSIES-TOOL中的知识表示及知识库的构造

在开发岩土工程安全监测专家系统开发工具(GSIES-TOOL)的过程中,针对岩土工程安全监测领域的具体特点,将传统的产生式规则和推理树、方法、实例、解释机制封装在一起,设计了一种新的知识表示方法OORL。在用OORL来描述该领域的知识后,根据对该领域知识的分类,进一步采用树形结构对该系统的知识库进行了分级构造,并通过双向链条机制对知识库中的相邻层次进行约束,以确保对知识库维护的安全性。由此而建立起来的知识库层次性好、推理效率高、易于管理。