A linguistic ontology

This paper defends the choice of a linguistically-based content ontology for natural language processing and demonstrates that a single common-sense ontology produces plausible interpretations at all levels from parsing through reasoning. The paper explores some of the problems and tradeoffs for a method which has just one content ontology. A linguistically-based content ontology represents the "world view" encoded in natural language. The content ontology (as opposed to the formal semantic ontology which distinguishes events from propositions, and so on) is best grounded in the culture, rather than in the world itself, or in the mind. By "world view" we mean naive assumptions about "what there is" in the world, and how it should be classified. These assumptions are time-worn and reflected in language at several levels: morphology, syntax and lexical semantics. The content ontology presented in the paper is part of a Naive Semantic lexicon, Naive Semantics is a lexical theory in which associated with each word sense is a naive theory (or set of beliefs) about the objects or events of reference. While naive semantic representations are not combinations of a closed set of primitives, they are also limited by a shallowness assumption. Included is just the information required to form a semantic interpretation incrementally, not all of the information known about objects. The Naive Semantic ontology is based upon a particular language, its syntax and its word senses. To the extent that other languages codify similar world views, we predict that their ontologies are similar. Applied in a computational natural language understanding system, this linguistically-motivated ontology (along with other native semantic information) is sufficient to disambiguate words, disambiguate syntactic structure, disambiguate formal semantic representations, resolve anaphoric expressions and perform reasoning tasks with text.     

Natural language plurals in logic programming queries

This article presents a representation for natural language plurals in knowledge base queries, implementing collective, distributive, cumulative, and multiply distributive senses of the plural by means of higher predicates. The underlying semantics is based on Franconi's theory of collections. The collective reading of a plural applies the predicate to the whole collection; the distributive reading applies the predicate to all of the elements; and the cumulative reading says that each element either satisfies the predicate or belongs to some collection that does so. Implicit in the cumulative reading is the notion of element-property, the property of belonging to some collection that satisfies a given predicate. Another reading of the plural, here termed the multiply distributive, requires a straightforward extension of the system to allow simultaneous distribution over more than one variable at a time, with none of the distributions having scope over any other. Simultaneous distribution is implemented as a metalogical predicate that transforms queries before executing them.     

国家知识基础设施中的数学知识表示

数学知识表示是知识表示中的一个重要方面,是数学知识检索、自动定理机器证明、智能教学系统等的基础.根据在设计NKI(national knowledge infrastructure)的数学知识表示语言中遇到的问题,并在讨论了数学对象的本体论假设的基础上提出了两种数学知识的表示方法:一种是以一个逻辑语言上的公式为属性值域的描述逻辑;另一种是以描述逻辑描述的本体为逻辑语言的一部分的一阶逻辑.在前者的表示中,如果对公式不作任何限制,那么得到的知识库中的推理不是可算法化的;在后者的表示中,以描述逻辑描述的本体中的推理是可算法化的,而以本体为逻辑语言的一部分的一阶逻辑所表示的数学知识中的推理一般是不可算法化的.因此,在表示数学知识时,需要区分概念性的知识(本体中的知识)和非概念性的知识(用本体作为语言表示的知识).框架或者描述逻辑可以表示和有效地推理概念性知识,但如果将非概念性知识加入到框架或知识库中,就可能使得原来可以有效推理的框架所表示的知识库不存在有效的推理算法,甚至不存在推理算法.为此,建议在表示数学知识时,用框架或描述逻辑来表示概念性知识;然后,用这样表示的知识库作为逻辑语言的一部分,以表示非概念性知识.     

Visual knowledge specification for conceptual design: Definition and tool support

Current CAD tools are not able to support the conceptual design phase, and none of them provides a consistency analysis for sketches produced by architects. This phase is fundamental and crucial for the whole design and construction process of a building. To give architects a better support, we developed a CAD tool for conceptual design and a knowledge specification tool. The knowledge is specific to one class of buildings and it can be reused. Based on a dynamic and domain-specific knowledge ontology, different types of design rules formalize this knowledge in a graph-based form. An expressive visual language provides a user-friendly, human readable representation. Finally, a consistency analysis tool enables conceptual designs to be checked against this formal conceptual knowledge.In this article, we concentrate on the knowledge specification part. For that, we introduce the concepts and usage of a novel visual language and describe its semantics. To demonstrate the usability of our approach, two graph-based visual tools for knowledge specification and conceptual design are explained.     

概念知识表示和推理

Nilsson教授首先提出了代数格应用于概念知识表示的思想．其优点在于知识表示的代数特性和图示特性，在此基础上．表文把代数格与PROLOG相结合．定义了一种基于概念的逻辑编程语言．其语言具有比PROLOG更抽象、更方便的编程风格．此外．给出了该逻辑编程语言的匹配算法．     

A treatment of plurals and plural quantifications based on a theory of collections

Collective entities and collective relations play an important role in natural language. In order to capture the full meaning of sentences like The Beatles sing Yesterday, a knowledge representation language should be able to express and reason about plural entities — like the Beatles — and their relationships — like sing — with any possible reading (cumulative, distributive or collective).In this paper a way of including collections and collective relations within a concept language, chosen as the formalism for representing the semantics of sentences, is presented. A twofold extension of theA–C concept language is investigated: (1) special relations introduce collective entities either out of their components or out of other collective entities, (2) plural quantifiers on collective relations specify their possible reading. The formal syntax and semantics of the concept language is given, together with a sound and complete algorithm to compute satisfiability and subsumption of concepts, and to compute recognition of individuals.An advantage of this formalism is the possibility of reasoning and stepwise refining in the presence of scoping ambiguities. Moreover, many phenomena covered by the Generalized Quantifiers Theory are easily captured within this framework. In the final part a way to include a theory of parts (mereology) is suggested, allowing for a lattice-theoretical approach to the treatment of plurals.     

Question answering based on pervasive agent ontology and Semantic Web

Semantic Web technologies bring new benefits to knowledge-based question answering system. Especially, ontology is becoming the pivotal methodology to represent domain-specific conceptual knowledge in order to promote the semantic capability of a QA system. In this paper we present a QA system in which the domain knowledge is represented by means of ontology. In addition, personalized services are enabled through modeling users’ profiles in the form of pervasive agent ontology, and a Chinese Natural Language human–machine interface is implemented mainly through a NL parser in this system. An initial evaluation result shows the feasibility to build such a semantic QA system based on pervasive agent ontology, the effectivity of personalized semantic QA, the extensibility of pervasive agent ontology and knowledge base, and the possibility of self-produced knowledge-based on semantic relations in the pervasive agent ontology.     

A linguistic ontology of space for natural language processing

We present a detailed semantics for linguistic spatial expressions supportive of computational processing that draws substantially on the principles and tools of ontological engineering and formal ontology. We cover language concerned with space, actions in space and spatial relationships and develop an ontological organization that relates such expressions to general classes of fixed semantic import. The result is given as an extension of a linguistic ontology, the Generalized Upper Model, an organization which has been used for over a decade in natural language processing applications. We describe the general nature and features of this ontology and show how we have extended it for working particularly with space. Treaitng the semantics of natural language expressions concerning space in this way offers a substantial simplification of the general problem of relating natural spatial language to its contextualized interpretation. Example specifications based on natural language examples are presented, as well as an evaluation of the ontology's coverage, consistency, predictive power, and applicability.     

An integrated, dual learner for grammars and ontologies

We introduce a dual-use methodology for automating the maintenance and growth of two types of knowledge sources, which are crucial for natural language text understanding—background knowledge of the underlying domain and linguistic knowledge about the lexicon and the grammar of the underlying natural language. A particularity of this approach is that learning occurs simultaneously with the on-going text understanding process. The knowledge assimilation process is centered around the linguistic and conceptual ‘quality' of various forms of evidence underlying the generation, assessment and on-going refinement of lexical and concept hypotheses. On the basis of the strength of evidence, hypotheses are ranked according to qualitative plausibility criteria, and the most reasonable ones are selected for assimilation into the already given lexical class hierarchy and domain ontology.     

Large-Scale Dictionary Construction for Foreign Language Tutoring and Interlingual Machine Translation

This paper describes techniques for automatic construction of dictionaries for use in large-scale foreign language tutoring (FLT) and interlingual machine translation (MT) systems. The dictionaries are based on a language-independent representation called lexical conceptual structure (LCS). A primary goal of the LCS research is to demonstrate that synonymous verb senses share distributional patterns. We show how the syntax–semantics relation can be used to develop a lexical acquisition approach that contributes both toward the enrichment of existing online resources and toward the development of lexicons containing more complete information than is provided in any of these resources alone. We start by describing the structure of the LCS and showing how this representation is used in FLT and MT. We then focus on the problem of building LCS dictionaries for large-scale FLT and MT. First, we describe authoring tools for manual and semi-automatic construction of LCS dictionaries; we then present a more sophisticated approach that uses linguistic techniques for building word definitions automatically. These techniques have been implemented as part of a set of lexicon-development tools used in the milt FLT project.     

Principles for organizing semantic relations in large knowledgebases

Defines principles for organizing semantic relations represented by slots in frame-structured knowledge bases. We organize slots based on the knowledge-level semantics of relations and the symbol-level function of slots that implement the representation language. The symbol-level organization of slots depends on the inferencing and expressive capabilities of the knowledge representation system. At the knowledge level, two entirely different organizational schemes are identified: one based on linguistic similarities and differences, and another based on the types of concepts being related     

A Chinese time ontology for the Semantic Web

Representation of and reasoning with temporal knowledge are fundamental in information systems that involve changes and actions. To build such systems, a time ontology is demanded. The development of a time ontology is also an indispensable part of effort to realize the Semantic Web. Nevertheless, our practice shows that any practical time ontology is closely related with a specific calendar, culture or history. To this end, this paper presents a Chinese time ontology for knowledge systems and web services which involve temporal entities or temporal properties. First, we define a base time ontology. As a core component, it consists of a time system, a timing system, a Gregorian timing system, and a timing ontology. Upon this base ontology, other parts of the Chinese time ontology are finally constructed, including the traditional Chinese timing system, temporal representation in Chinese idiosyncratic ways, and transformation between temporal entities in the Gregorian timing system and temporal entities in the traditional Chinese timing system. We will argue that the base time ontology is not only a basic and integral part of the Chinese time ontology, but also a base for constructing other time ontologies.     

Integrating discourse pragmatics and propositional knowledge for multilingual natural language processing

We present an integrated knowledge representation system for natural language processing (NLP) whose main distinguishing feature is its emphasis on encoding not only the usual propositional structure of the utterances in the input text, but also capturing an entire complex of nonpropositional — discourse, attitudinal, and other pragmatic — meanings that NL texts always carry. The need for discourse pragmatics, together with generic semantic information, is demonstrated in the context of anaphoric and definite noun phrase resolution for accurate machine translation. The major types of requisite pragmatic knowledge are presented, and an extension of a frame-based formalism developed in the context of the TRANSLATOR system is proposed as a first-pass codification of the integrated knowledge base.     

Kumbang: A domain ontology for modelling variability in software product families

Variability is the ability of a system to be efficiently extended, changed, customised or configured for use in a particular context. There is an ever-growing demand for variability of software. Software product families are an important means for implementing software variability. We present a domain ontology called Kumbang for modelling the variability in software product families. Kumbang synthesises previous approaches to modelling variability in software product families. In addition, it incorporates modelling constructs developed in the product configuration domain for modelling the variability in non-software products. The modelling concepts include components and features with compositional structure and attributes, the interfaces of components and connections between them, and constraints. The semantics of Kumbang is rigorously described using natural language and a UML profile. We provide preliminary proof of concept for Kumbang: the domain ontology has been provided with a formal semantics by implementing a translation into a general-purpose knowledge representation language with formal semantics and inference support. A prototype tool for resolving variability has been implemented.     

An Ontology-Based Approach to Metaphor Cognitive Computation

Language understanding is one of the most important characteristics for human beings. As a pervasive phenomenon in natural language, metaphor is not only an essential thinking approach, but also an ingredient in human conceptual system. Many of our ways of thinking and experiences are virtually represented metaphorically. With the development of the cognitive research on metaphor, it is urgent to formulate a computational model for metaphor understanding based on the cognitive mechanism, especially with the view to promoting natural language understanding. Many works have been done in pragmatics and cognitive linguistics, especially the discussions on metaphor understanding process in pragmatics and metaphor mapping representation in cognitive linguistics. In this paper, a theoretical framework for metaphor understanding based on the embodied mechanism of concept inquiry is proposed. Based on this framework, ontology is introduced as the knowledge representation method in metaphor understanding, and metaphor mapping is formulated as ontology mapping. In line with the conceptual blending theory, a revised conceptual blending framework is presented by adding a lexical ontology and context as the fifth mental space, and a metaphor mapping algorithm is proposed.     

The elements of an open KBS infrastructure

This paper describes the elements of an open knowledge based system infrastructure. These are: standard knowledge representation systems; a knowledge interchange format; a knowledge manipulation and query language; common shared ontology and an agent based software engineering model. These elements provide the means to achieve Knowledge Sharing and Reuse between heterogeneous knowledge based systems. The elements have been adopted as the basis of the conceptual design for the Advanced Technology Operations System ATOS being designed by the European Space Agency ESA.