Ambiguity Of Natural Language Research Articles

From Existential Graphs to Conceptual Graphs

Existential graphs (EGs) are a simple, readable, and expressive graphic notation for logic. Conceptual graphs (CGs) combine a logical foundation based on EGs with features of the semantic networks used in artificial intelligence and computational linguistics. CG design principles address logical, linguistic, and cognitive requirements: a formal semantics defined by the ISO standard for Common Logic; the flexibility to support the expressiveness, context dependencies, and metalevel commentary of natural language; and cognitively realistic operations for reasoning by induction, deduction, abduction, and analogy. To accommodate the vagueness and ambiguities of natural language, informal heuristics can supplement the formal semantics. With sufficient background knowledge and a clarifying dialog, informal graphs can be refined to any degree of precision. Peirce claimed that the rules for reasoning with EGs generate “a moving picture of the action of the mind in thought.” Some philosophers and psychologists agree: Peirce’s diagrams and rules are a good candidate for a natural logic that reflects the neural processes that support thought and language. They are psychologically realistic and computationally efficient.

Improving Habitability of Natural Language Interfaces for Querying Ontologies With Feedback and Clarifcation Dialogues

Natural Language Interfaces (NLIs) are a viable, human-readable alternative to complex, formal query languages like SPARQL, which are typically used for accessing semantically structured data (e.g.RDF and OWL repositories). However, in order to cope with natural language ambiguities, NLIs typically support a more restricted language. A major challenge when designing such restricted languages is habitability–how easily, naturally and effectively users can use the language to express themselves within the constraints imposed by the system. In this paper, we investigate two methods for improving the habitability of a Natural Language Interface: feedback and clarifcation dialogues. We model feedback by showing the user how the system interprets the query,thus suggesting repair through query reformulation. Next, we investigate how clarifcation dialogues can be used to control the query interpretations generated by the system. To reduce the cognitive overhead, clarifcation dialogues are coupled with a l earning mechanism. Both methods are shown to have a positive effect on the overall performance and habitability.

The syzygy surfer: (Ab)using the semantic web to inspire creativity

This article discusses our development of a new web engine, the syzygy surfer, which aims to induce a search/browsing experience that is more creative than traditional search. We do this by purposefully combining the ambiguity of natural language with the precision of semantic web technologies. Here, we set out the framework for our investigation and discuss the context and background ideas that are informing the research. This paper offers some preliminary examples taken from our work in progress on the device and suggests the way ahead for future developments and applications.

Towards developing consistent misuse case models

Secure software development should begin at the early stages of the development life cycle. Misuse case modeling is a technique that stems from traditional use case modeling, which facilitates the elicitation and modeling functional security requirements at the requirements phase. Misuse case modeling is an effective vehicle to potentially identify a large subset of these threats. It is therefore crucial to develop high quality misuse case models otherwise end system developed will be vulnerable to security threats. Templates to describe misuse cases are populated with syntax-free natural language content. The inherent ambiguity of syntax-free natural language coupled with the crucial role of misuse case models in development can have a very detrimental effect. This paper proposes a structure that will guide misuse case authors towards developing consistent misuse case models. This paper also presents a process that utilizes this structure to ensure the consistency of misuse case models as they evolve, eliminating potential damages caused by inconsistencies. A tool was developed to provide automation support for the proposed structure and process. The feasibility and application of this approach were demonstrated using two real-world case studies.

Ontology design patterns to disambiguate relations between genes and gene products in GENIA

MotivationAnnotated reference corpora play an important role in biomedical information extraction. A semantic annotation of the natural language texts in these reference corpora using formal ontologies is challenging due to the inherent ambiguity of natural language. The provision of formal definitions and axioms for semantic annotations offers the means for ensuring consistency as well as enables the development of verifiable annotation guidelines. Consistent semantic annotations facilitate the automatic discovery of new information through deductive inferences.ResultsWe provide a formal characterization of the relations used in the recent GENIA corpus annotations. For this purpose, we both select existing axiom systems based on the desired properties of the relations within the domain and develop new axioms for several relations. To apply this ontology of relations to the semantic annotation of text corpora, we implement two ontology design patterns. In addition, we provide a software application to convert annotated GENIA abstracts into OWL ontologies by combining both the ontology of relations and the design patterns. As a result, the GENIA abstracts become available as OWL ontologies and are amenable for automated verification, deductive inferences and other knowledge-based applications.AvailabilityDocumentation, implementation and examples are available from http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/.

Managing ambiguity in programming by finding unambiguous examples

We propose a new way to raise the level of discourse in the programming process: permit ambiguity, but manage it by linking it to unambiguous examples. This allows programming environments to work with informal descriptions that lack precise semantics, such as natural language descriptions or conceptual diagrams, without requiring programmers to formulate their ideas in a formal language first. As an example of this idea, we present Zones, a code search and reuse interface that connects code with ambiguous natural language statements about its purpose. The backend, called ProcedureSpace, relates purpose statements, static code analysis features, and natural language background knowledge. ProcedureSpace can search for code given statements of purpose or vice versa, and can find code that was never annotated or commented. Since completed Zones searches become annotations, system coverage grows with user interaction. Users in a preliminary study found that reasoning jointly over natural language and programming language helped them reuse code.

SEMCL

To promote global knowledge sharing, one should solve the problem that knowledge representation in diverse natural languages restricts knowledge sharing effectively. Traditional knowledge sharing models are based on natural language processing (NLP) technologies. The ambiguity of natural language is a problem for NLP; however, semantic web technologies can circumvent the problem by enabling human authors to specify meaning in a computer-interpretable form. In this paper, the authors propose a cross-language semantic model (SEMCL) for knowledge sharing, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. Also, this model can match knowledge descriptions in diverse languages. First, the methods used to support searches at the semantic predicate level are given, and the authors present a cross-language approach. Finally, an implementation of the model for the general engineering domain is discussed, and a scenario describing how the model implementation handles semantic cross-language knowledge sharing is given.

Semantic Restructuring of Natural Language Image Captions to Enhance Image Retrieval

The rapid growth in the volume of visual information can make the task of finding and accessing visual information of interest, overwhelming for users. Semantic analysis of image captions can be used in conjunction with image retrieval systems (IMR) to retrieve selected images more precisely. To do this, we first exploit a Natural Language Processing (NLP) framework in order to extract concepts from image captions. Next, an ontology-based framework is deployed in order to resolve natural language ambiguities. The novelty of the proposed framework is that the combination of LSI with the Ontology framework enables the combined framework to tolerate ambiguities and variations in the Ontology. A key feature is that the system can find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the use of LSI based NLP combined with an ontological framework significantly enhances image retrieval.

McTaggart, the ow of time, and the Disanalogy between Time and Space

Abstract McTaggart's negative thesis in his proof for the unreality of time, which contends that the A-series is contradictory, is still today upheld as a proof of the unreality of the properties of past, present, and future, and of the `flow of time'. In my paper, I defend the possibility of a complete and consistent description of the A-series, thus refuting McTaggart's negative thesis. I show that the failure to acknowledge the possibility of such a description is due to an ambiguity in natural language. Once this ambiguity is clarified, and in light of the disanalogy between time and space, the usual description of the A-series, `Event e was future, is present, and will be past', is shown to be a successful description of the change in the temporal A-properties (the `flow of time')

Requirements for tools for ambiguity identification and measurement in natural language requirements specifications

This paper proposes a two-step approach to identifying ambiguities in natural language (NL) requirements specifications (RSs). In the first step, a tool would apply a set of ambiguity measures to a RS in order to identify potentially ambiguous sentences in the RS. In the second step, another tool would show what specifically is potentially ambiguous about each potentially ambiguous sentence. The final decision of ambiguity remains with the human users of the tools. The paper describes several requirements-identification experiments with several small NL RSs using four prototypes of the first tool based on linguistic instruments and resources of different complexity and a manual mock-up of the second tool.

Interval rough mereology and description logic: An approach to formal treatment of imprecision in the Semantic Web ontologies

Learning and maintaining ontologies play critically important roles in the Semantic Web. Ontologies provide formal and explicit semantics to available knowledge and make possible shared understanding of a domain. A great deal of knowledge in the Semantic Web involves different sorts of imprecision. Common examples of imprecise knowledge include various statistical hypotheses, vagueness and ambiguities of natural language and many others. Consequently, ontologies must be expressive enough to handle imprecision. This paper presents an approach to formal handling of granularity as a special kind of imprecision in ontologies. It is based on Soft Computing techniques, in particular, Rough Set and Fuzzy Set theories. The approach also makes heavy use of Rough Mereology - a recently proposed paradigm for approximate reasoning - to compute degrees of imprecision of ontological concepts, relationships and assertions. It is demonstrated how the proposed mereological approach can be used together with the framework for representing uncertainty in Description Logic - family of popular logics for specifying formal ontologies. Finally, the paper introduces interval computations to the area of representing and reasoning with ontologies and shows how intervals can formally capture the imprecision incurred by computing rough inclusion (mereological) functions.

The Complexity Revolution

The Complexity Revolution

Biomimetic design through natural language analysis to facilitate cross-domain information retrieval

Biomimetic, or biologically inspired, design uses analogous biological phenomena to develop solutions for engineering problems. Several instances of biomimetic design result from personal observations of biological phenomena. However, many engineers' knowledge of biology may be limited, thus reducing the potential of biologically inspired solutions. Our approach to biomimetic design takes advantage of the large amount of biological knowledge already available in books, journals, and so forth, by performing keyword searches on these existing natural-language sources. Because of the ambiguity and imprecision of natural language, challenges inherent to natural language processing were encountered. One challenge of retrieving relevant cross-domain information involves differences in domain vocabularies, or lexicons. A keyword meaningful to biologists may not occur to engineers. For an example problem that involved cleaning, that is, removing dirt, a biochemist suggested the keyword “defend.” Defend is not an obvious keyword to most engineers for this problem, nor are the words defend and “clean/remove” directly related within lexical references. However, previous work showed that biological phenomena retrieved by the keyword defend provided useful stimuli and produced successful concepts for the clean/remove problem. In this paper, we describe a method to systematically bridge the disparate biology and engineering domains using natural language analysis. For the clean/remove example, we were able to algorithmically generate several biologically meaningful keywords, including defend, that are not obviously related to the engineering problem. We developed a method to organize and rank the set of biologically meaningful keywords identified, and confirmed that we could achieve similar results for two other examples in encapsulation and microassembly. Although we specifically address cross-domain information retrieval from biology, the bridging process presented in this paper is not limited to biology, and can be used for any other domain given the availability of appropriate domain-specific knowledge sources and references.

Ontology-Based Descriptions for Semantic Discovery and Composition of Geoprocessing Services

The ability to process geospatial data will be a great benefit for spatial data infrastructures. This requires the ability to compose data providing services with geoprocessing services. Discovering suitable geoprocessing services is a major challenge in this endeavour. Current (keyword-based) approaches to service discovery are inherently restricted by the ambiguities of natural language, which can lead to low precision and/or recall. To alleviate these problems, we propose to use an ontology-based approach to GI service discovery, which rests on two ideas. Ontologies describing geospatial operations are used to create descriptions of requirements and service capabilities; matches between these descriptions are identified based on function subtyping. We use a running example from the geospatial domain to analyse which problems can occur in existing keyword- and ontology-based approaches and how the discovery of geoprocessing services differs from other service discovery tasks. The example is also used for illustrating the prototypical implementation of the proposed approach.

A collaborative behavior-based approach for handling ambiguity, uncertainty, and vagueness in robot natural language interfaces

Service robots are used by ordinary people in their houses and offices. For such users, a desirable way to communicate with robots is through natural language interfaces. So far, techniques for developing robot natural language interfaces are far from mature. A major challenge is handling ambiguity, uncertainty, and vagueness in parsing, object resolution, and vague natural language words. In this research, we develop a new approach called the collaborative behavior-based approach, in which behaviors of robots and behaviors of human users, as well as the changes of object states caused by the behaviors, are taken into consideration integratedly in processing natural language user instructions. In this paper, we analyze the special features of a human–robot interface that may affect language understanding, describe our approach that is designed based on the features, and present the implementation and some experimental results.

Ontology‐based retrieval of geographic information

Discovering and accessing suitable geographic information (GI) in the open and distributed environments of current Spatial Data Infrastructures (SDIs) is a crucial task. Catalogues provide searchable repositories of information descriptions, but the mechanisms to support GI retrieval are still insufficient. Problems of semantic heterogeneity caused by the ambiguity of natural language can arise during keyword‐based search in catalogues and when formulating a query to access the discovered data. In this paper, we present an approach to ontology‐based GI retrieval that contributes to solving existing problems of semantic heterogeneity and hides most of the complexity of the required procedure from the requester. A query language and graphical user interface allow a requester to intuitively formulate a query using a well‐known domain vocabulary. From this query, an ontology concept is derived, which is then used to search a catalogue for a data source that provides all the information required to answer the requester's query. If a suitable data source is discovered, the relevant data are accessed through a standardized interface. The approach is implemented through several components that can be used as an extension to standard SDIs.

On the structural ambiguity in natural language that the neural architecture cannot deal with

We argue that van der Velde's & de Kamps's model does not solve the binding problem but merely shifts the burden of constructing appropriate neural representations of sentence structure to unexplained preprocessing of the linguistic input. As a consequence, their model is not able to explain how various neural representations can be assigned to sentences that are structurally ambiguous.

Une méthode de formalisation progressive des exigences basée sur un modèle simulable

Software requirements, usually wrutten in natural language, are the basis for design and functionnal testing phases. Due to natural language ambiguity, the requirements can thus be interpretyed differently while building and validating the software. We thus propose an incremental refinement method from the requirements to models with well-known semantics. From requirements in controlled natural language, we first generate a simulatable functional model based on UML use cases, then the simulation is used to enhance the requirements and to generate tests. We also generate a draft of static model.

Word Sense Disambiguation in the Biomedical Domain: An Overview

There is a trend towards automatic analysis of large amounts of literature in the biomedical domain. However, this can be effective only if the ambiguity in natural language is resolved. In this paper, the current state of research in word sense disambiguation (WSD) is reviewed. Several methods for WSD have already been proposed, but many systems have been tested only on evaluation sets of limited size. There are currently only very few applications of WSD in the biomedical domain. The current direction of research points towards statistically based algorithms that use existing curated data and can be applied to large sets of biomedical literature. There is a need for manually tagged evaluation sets to test WSD algorithms in the biomedical domain. WSD algorithms should preferably be able to take into account both known and unknown senses of a word. Without WSD, automatic metaanalysis of large corpora of text will be error prone.

GLR parsing with multiple grammars for natural language queries

This article presents an approach for parsing natural language queries that integrates multiple subparsers and subgrammars, in contrast to the traditional single grammar and parser approach. In using LR(k) parsers for natural language processing, we are faced with the problem of rapid growth in parsing table sizes as the number of grammar rules increases. We propose to partition the grammar into multiple subgrammars, each having its own parsing table and parser. Grammar partitioning helps reduce the overall parsing table size when compared to using a single grammar. We used the GLR parser with an LR(1) parsing table in our framework because GLR parsers can handle ambiguity in natural language. A parser composition technique then combines the parsers' outputs to produce an overall parse that is the same as the output parse of single parser. Two different strategies were used for parser composition: (i) parser composition by cascading; and (ii) parser composition with predictive pruning.Our experiments were conducted with natural language queries from the ATIS (Air Travel Information Service) domain. We have manually translated the ATIS-3 corpora into Chinese, and consequently we could experiment with grammar partitioning on parallel linguistic corpora. For English, the unpartitioned ATIS grammar has 72,869 states in its parsing table, while the partitioned English grammar has 3,350 states in total. For Chinese, grammar partitioning reduced the overall parsing table size from 29,734 states to 3,894 states. Both results show that grammar partitioning greatly economizes on the overall parsing table size. Language understanding performances were also examined. Parser composition imparts a robust parsing capability in our framework, and hence obtains a higher understanding performance when compared to using a single GLR parser.