Specifying ontologies: linguistic aspects in problem-driven knowledge engineering

In this paper a method for controlling the dialog in a natural language (NL) system is presented. It provides a deep modeling of information processing based on time dependent propositional attitudes of the interacting agents. Knowledge about the state of the dialog is represented in a dedicated language and changes of this state are described by a compact set of rules. An appropriate organization of rule application is introduced including the initiation of an adequate system reaction. Finally the application of the method in an NL consultation system is outlined.

Development of natural language systems, apart from the research interest, always worked on the assumption that such systems would be useful in practice. With the arrival of systems and prototypes that can be used, the question needs to be considered as to what makes them usable and what detracts from usability. The initial idea, that the usability of natural language systems was dependent solely on their linguistic capabilities, turned out to be mistaken. Like other systems, they need reasonable diagnostics, legible manuals, and must fit into their environment. As natural language systems, they differ from other systems in how they foreground specific facets of human language behaviour.

15 - UTILIZING KNOWLEDGE: The Components of the SB-ONE Knowledge Representation Workbench

We describe the methods and hardware that we are using to produce a real-time demonstration of an integrated Spoken Language System. We describe algorithms that greatly reduce the computation needed to compute the N-Best sentence hypotheses. To avoid grammar coverage problems we use a fully-connected first-order statistical class grammar. The speech-search algorithm is implemented on a board with a single Intel i860 chip, which provides a factor of 5 speedup over a SUN 4 for straight C code. The board plugs directly into the VME bus of the SUN4, which controls the system and contains the natural language system and application back end.

Aspects of an intelligent interface that provides natural language access to a large body of data distributed over a computer network are described. The overall system architecture is presented, showing how a user is buffered from the actual database management systems (DBMSs) by three layers of insulating components. These layers operate in series to convert natural language queries into calls to DBMSs at remote sites. Attention is then focused on the first of the insulating components, the natural language system. A pragmatic approach to language access that has proved useful for building interfaces to databases is described and illustrated by examples. Special language features that increase system usability, such as spelling correction, processing of incomplete inputs, and run-time system personalization, are also discussed. The language system is contrasted with other work in applied natural language processing, and the system's limitations are analyzed.

This paper reports progress in development of evaluation methodologies for natural language systems. Without a common classification of the problems in natural language understanding authors have no way to specify clearly what their systems do, potential users have no way to compare different systems and researchers have no way to judge the advantages or disadvantages of different approaches to developing systems.

Few studies have compared the interactive use of Boolean and natural language searching systems. We studied the use of three retrieval systems by senior medical students searching on queries generated by actual physicians in a clinical setting. The searchers were randomized to search on two of three different retrieval systems: a Boolean system, a word-based natural language system, and a concept-based natural language system. Our results showed no statistically significant differences in recall or precision among the three systems. Likewise, we found no user preference for any system over the others. In the course of this study we did find, however, a number of problems with traditional measures of retrieval evaluation when applied to the interactive search setting.

In prior work we observed that expert searchers follow well‐defined search procedures in order to obtain comprehensive information on the Web. Motivated by that observation, we developed a prototype domain portal called the Strategy Hub that provides expert search procedures to benefit novice searchers. The search procedures in the prototype were entirely handcrafted by search experts, making further expansion of the Strategy Hub cost‐prohibitive. However, a recent study on the distribution of healthcare information on the web suggested that search procedures can be automatically generated from pages that have been rated based on the extent to which they cover facts relevant to a topic.This paper presents the results of experiments designed to automate the process of rating the extent to which a page covers relevant facts. To automatically generate these ratings, we used two natural language systems, Latent Semantic Analysis and MEAD, to compute the similarity between sentences on the page and each fact. We then used an algorithm to convert these similarity scores to a single rating that represents the extent to which the page covered each fact.These automatic ratings are compared with manual ratings using inter‐rater reliability statistics. Analysis of these statistics reveals the strengths and weaknesses of each tool, and suggests avenues for improvement.

: Ellipsis in English can occur in many different forms. A large class of ellipses is isolated, encompassing such traditionally distinct linguistic rules as Right-node Raising, Gapping, VP-Deletion, Coordinate Reduction, and other phenomena which crosses sentence boundaries. The members of this class of ellipses involve implicit or explicit coordination. A uniform algorithm implemented in the UC (Unix Consultant) natural language system resolves the ellipses in this class, and constraints are given which govern the production of those ellipses. Justification is provided for the approach taken, and its limitations and applications are explored.

On Perceived Conceptual and Methodological Divergences in Linguistic Theory and Cognitive Science: Distributional Analyses, Universal Grammar, and Language Acquisition Authors Garrett Neske New York University PDF Article info Impact Citations Published at 24. March 2010 https://doi.org/10.5964/bioling.8761 Issue: Vol. 4 No. 1 (2010) Section: Briefs Keywords: language acquisition learning computational linguistics universal grammar development Share: Z PlumX Dimensions Views: Total Abstract PDF 10 7 3

The semantic web and open data paradigms are gaining momentum in recent years and more information is being published online following the linked data principles. This enables easy access and processing of data by external services. An example of such services are intelligent conversational agents that provide to the users the ability to interact with a computer system in natural language. Such communication is much more intuitive and facilitates the use of complex services to less skilled users (e.g., elderly) or users with disabilities (e.g., visually impaired) thus providing to these groups access to the huge amount of information stored in the semantic web or specific online services. In this paper, we present a proof-of-concept conversational agent able to provide information about the European cultural heritage and display stored digital content from the Europeana database.

The rapid development in the field of information science and the increase in the usage of information retrieval strategies have empowered users to retrieve more accurate information. The availability of information in a different format and across different has presented colossal difficulties for information retrieval using information retrieval techniques. In this paper, an attempt is made at highlighting the question and answering (QA) systems that provide users the platform to express their question using the Natural Language and also retrieve the response from such systems in Natural Language. The four important modules of any QA system are the Natural Language Question (NLQ) processing, document processing, passage processing, and answer processing. Fundamentally, most QA systems combined several techniques from other fields such as information retrieval, knowledge representation, and natural language processing to process NLQ and present the most insightful response based on the stored document. This paper gives a thorough review of the various survey on the QA systems frameworks, their methodologies, types, approaches, and challenges of QA systems.

Due to their significant role in creative design ideation, databases of causal ontology-based models for biological and technical systems have been developed. However, creating structured database entries through system models using a causal ontology requires the time and effort of experts. Researchers have worked toward developing methods that can automatically generate representations of systems from documents using causal ontologies by leveraging machine learning (ML) techniques. However, these methods use limited, hand-annotated data for building the ML models and have manual touchpoints that are not documented. While opportunities exist to improve the accuracy of these ML models, more importantly, it is required to understand the complete process of generating structured representations using causal ontology. This research proposes a new method and a set of rules to extract information relevant to the constructs of the SAPPhIRE model of causality from descriptions of technical systems in natural language and report the performance of this process. This process aims to understand the information in the context of the entire description. The method starts by identifying the system interactions involving material, energy and information and then builds the causal description of each system interaction using the SAPPhIRE ontology. This method was developed iteratively, verifying the improvements through user trials in every cycle. The user trials of this new method and rules with specialists and novice users of the SAPPhIRE modeling showed that the method helps in accurately and consistently extracting the information relevant to the constructs of the SAPPhIRE model from a given natural language description.

Ces réflexions s’inscrivent dans le cadre des interrogations épistémologiques et méthodologiques concernant l’efficacité des théories linguistiques dans l’objectif de pouvoir les comparer et évaluer sur la base de la manière dont elles se construisent, à savoir sur la base de la prise en compte de l’argumentation. Il s’agit d’un cas particulier dans lequel théorie linguistique et métathéorie semblent coïncider du simple fait que les deux utilisent la structure argumentative comme système de représentation. Dans nos analyses linguistiques, nous avons recours à l’argumentation comme système de représentation du sens, alors que dans l’évaluation des données, nous faisons appel à l’argumentation comme système de représentation de la théorie elle-même. Est-il possible d’éviter l’assimilation ? S’agirait-il en réalité d’une assimilation ? Sinon, que peut-on faire pour retenir la non-identité des deux argumentations tout en les visant simultanément ?

Accuracy and precision remains a challenge for capturing system requirements in general, space systems are no exception. Current research efforts continue to fundamentally rely on natural language (shall statements), which is inherently ambiguous, and thus unable to capture the problem space accurately and precisely. We suggest in this paper a model-based approach to requirements that avoids the use of requirements in natural language and leverages formal modeling and system-theoretic constructs instead. Specifically, the proposed approach extends behavioral and structural model elements of the Systems Modeling Language (SysML) with a system-theoretic definition of a solution space. Considering a system model to be a transformation of inputs into outputs, we model the problem space in this paper as a set of required transformations of inputs into outputs. We apply the proposed model-based approach to formulate a subset of requirements of a satellite's Telemetry, Tracking, and Command (TTC) transponder. We use an existing set of requirements for such a system in natural language as a benchmark and evaluate precision, accuracy, and completeness aspects achieved by the proposed model-based formulation.