5,015 research outputs found
Feasibility report: Delivering case-study based learning using artificial intelligence and gaming technologies
This document describes an investigation into the technical feasibility of a game to support learning based on case studies. Information systems students using the game will conduct fact-finding interviews with virtual characters. We survey relevant technologies in computational linguistics and games. We assess the applicability of the various approaches and propose an architecture for the game based on existing techniques. We propose a phased development plan for the development of the game
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Retrieving information from heterogeneous freight data sources to answer natural language queries
textThe ability to retrieve accurate information from databases without an extensive knowledge of the contents and organization of each database is extremely beneficial to the dissemination and utilization of freight data. The challenges, however, are: 1) correctly identifying only the relevant information and keywords from questions when dealing with multiple sentence structures, and 2) automatically retrieving, preprocessing, and understanding multiple data sources to determine the best answer to userâs query. Current named entity recognition systems have the ability to identify entities but require an annotated corpus for training which in the field of transportation planning does not currently exist. A hybrid approach which combines multiple models to classify specific named entities was therefore proposed as an alternative. The retrieval and classification of freight related keywords facilitated the process of finding which databases are capable of answering a question. Values in data dictionaries can be queried by mapping keywords to data element fields in various freight databases using ontologies. A number of challenges still arise as a result of different entities sharing the same names, the same entity having multiple names, and differences in classification systems. Dealing with ambiguities is required to accurately determine which database provides the best answer from the list of applicable sources. This dissertation 1) develops an approach to identify and classifying keywords from freight related natural language queries, 2) develops a standardized knowledge representation of freight data sources using an ontology that both computer systems and domain experts can utilize to identify relevant freight data sources, and 3) provides recommendations for addressing ambiguities in freight related named entities. Finally, the use of knowledge base expert systems to intelligently sift through data sources to determine which ones provide the best answer to a userâs question is proposed.Civil, Architectural, and Environmental Engineerin
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
We present the architecture and the evaluation of a new system for
recognizing textual entailment (RTE). In RTE we want to identify automatically
the type of a logical relation between two input texts. In particular, we are
interested in proving the existence of an entailment between them. We conceive
our system as a modular environment allowing for a high-coverage syntactic and
semantic text analysis combined with logical inference. For the syntactic and
semantic analysis we combine a deep semantic analysis with a shallow one
supported by statistical models in order to increase the quality and the
accuracy of results. For RTE we use logical inference of first-order employing
model-theoretic techniques and automated reasoning tools. The inference is
supported with problem-relevant background knowledge extracted automatically
and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or
other, more experimental sources with, e.g., manually defined presupposition
resolutions, or with axiomatized general and common sense knowledge. The
results show that fine-grained and consistent knowledge coming from diverse
sources is a necessary condition determining the correctness and traceability
of results.Comment: 25 pages, 10 figure
Is question answering fit for the Semantic Web? A survey
With the recent rapid growth of the Semantic Web (SW), the processes of searching and querying content that is both massive in scale and heterogeneous have become increasingly challenging. User-friendly interfaces, which can support end users in querying and exploring this novel and diverse, structured information space, are needed to make the vision of the SW a reality. We present a survey on ontology-based Question Answering (QA), which has emerged in recent years to exploit the opportunities offered by structured semantic information on the Web. First, we provide a comprehensive perspective by analyzing the general background and history of the QA research field, from influential works from the artificial intelligence and database communities developed in the 70s and later decades, through open domain QA stimulated by the QA track in TREC since 1999, to the latest commercial semantic QA solutions, before tacking the current state of the art in open userfriendly interfaces for the SW. Second, we examine the potential of this technology to go beyond the current state of the art to support end-users in reusing and querying the SW content. We conclude our review with an outlook for this novel research area, focusing in particular on the R&D directions that need to be pursued to realize the goal of efficient and competent retrieval and integration of answers from large scale, heterogeneous, and continuously evolving semantic sources
Terminology and ontology development for semantic annotation : A use case on sepsis and adverse events
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Language, logic and ontology: uncovering the structure of commonsense knowledge
The purpose of this paper is twofold: (i) we argue that the structure of commonsense knowledge must be discovered, rather than invented; and (ii) we argue that natural
language, which is the best known theory of our (shared) commonsense knowledge, should itself be used as a guide to discovering the structure of commonsense knowledge. In addition to suggesting a systematic method to the discovery of the structure of commonsense knowledge, the method we propose seems to also provide an explanation for a number of phenomena in natural language, such as metaphor, intensionality, and the semantics of nominal compounds. Admittedly, our ultimate goal is quite ambitious, and it is no less than the systematic âdiscoveryâ of a well-typed
ontology of commonsense knowledge, and the subsequent formulation of the longawaited goal of a meaning algebra
Using natural language processing for question answering in closed and open domains
With regard to the growth in the amount of social, environmental, and biomedical information available digitally, there is a growing need for Question Answering (QA) systems that can empower users to master this new wealth of information. Despite recent progress in QA, the quality of interpretation and extraction of the desired answer is not adequate. We believe that striving for higher accuracy in QA systems is subject to on-going research, i.e., it is better to have no answer is better than wrong answers. However, there are diverse queries, which the state of the art QA systems cannot interpret and answer properly.
The problem of interpreting a question in a way that could preserve its syntactic-semantic structure is considered as one of the most important challenges in this area. In this work we focus on the problems of semantic-based QA systems and analyzing the effectiveness of NLP techniques, query mapping, and answer inferencing both in closed (first scenario) and open (second scenario) domains. For this purpose, the architecture of Semantic-based closed and open domain Question Answering System (hereafter âScoQASâ) over ontology resources is presented with two different prototyping: Ontology-based closed domain and an open domain under Linked Open Data (LOD) resource.
The ScoQAS is based on NLP techniques combining semantic-based structure-feature patterns for question classification and creating a question syntactic-semantic information structure (QSiS). The QSiS provides an actual potential by building constraints to formulate the related terms on syntactic-semantic aspects and generating a question graph (QGraph) which facilitates making inference for getting a precise answer in the closed domain. In addition, our approach provides a convenient method to map the formulated comprehensive information into SPARQL query template to crawl in the LOD resources in the open domain.
The main contributions of this dissertation are as follows:
1. Developing ScoQAS architecture integrated with common and specific components compatible with closed and open domain ontologies.
2. Analysing userâs question and building a question syntactic-semantic information structure (QSiS), which is constituted by several processes of the methodology: question classification, Expected Answer Type (EAT) determination, and generated constraints.
3. Presenting an empirical semantic-based structure-feature pattern for question classification and generalizing heuristic constraints to formulate the relations between the features in the recognized pattern in terms of syntactical and semantical.
4. Developing a syntactic-semantic QGraph for representing core components of the question.
5. Presenting an empirical graph-based answer inference in the closed domain.
In a nutshell, a semantic-based QA system is presented which provides some experimental results over the closed and open domains. The efficiency of the ScoQAS is evaluated using measures such as precision, recall, and F-measure on LOD challenges in the open domain. We focus on quantitative evaluation in the closed domain scenario. Due to the lack of predefined benchmark(s) in the first scenario, we define measures that demonstrate the actual complexity of the problem and the actual efficiency of the solutions. The results of the analysis corroborate the performance and effectiveness of our approach to achieve a reasonable accuracy.Con respecto al crecimiento en la cantidad de informaciĂłn social, ambiental y biomĂ©dica disponible digitalmente, existe una creciente necesidad de sistemas de la bĂșsqueda de la respuesta (QA) que puedan ofrecer a los usuarios la gestiĂłn de esta nueva cantidad de informaciĂłn. A pesar del progreso reciente en QA, la calidad de interpretaciĂłn y extracciĂłn de la respuesta deseada no es la adecuada. Creemos que trabajar para lograr una mayor precisiĂłn en los sistemas de QA es todavĂa un campo de investigaciĂłn abierto. Es decir, es mejor no tener respuestas que tener respuestas incorrectas. Sin embargo, existen diversas consultas que los sistemas de QA en el estado del arte no pueden interpretar ni responder adecuadamente. El problema de interpretar una pregunta de una manera que podrĂa preservar su estructura sintĂĄctica-semĂĄntica es considerado como uno de los desafĂos mĂĄs importantes en esta ĂĄrea. En este trabajo nos centramos en los problemas de los sistemas de QA basados en semĂĄntica y en el anĂĄlisis de la efectividad de las tĂ©cnicas de PNL, y la aplicaciĂłn de consultas e inferencia respuesta tanto en dominios cerrados (primer escenario) como abiertos (segundo escenario). Para este propĂłsito, la arquitectura del sistema de bĂșsqueda de respuestas en dominios cerrados y abiertos basado en semĂĄntica (en adelante "ScoQAS") sobre ontologĂas se presenta con dos prototipos diferentes: en dominio cerrado basado en el uso de ontologĂas y un dominio abierto dirigido a repositorios de Linked Open Data (LOD). El ScoQAS se basa en tĂ©cnicas de PNL que combinan patrones de caracterĂsticas de estructura semĂĄnticas para la clasificaciĂłn de preguntas y la creaciĂłn de una estructura de informaciĂłn sintĂĄctico-semĂĄntica de preguntas (QSiS). El QSiS proporciona una manera la construcciĂłn de restricciones para formular los tĂ©rminos relacionados en aspectos sintĂĄctico-semĂĄnticos y generar un grafo de preguntas (QGraph) el cual facilita derivar inferencias para obtener una respuesta precisa en el dominio cerrado. AdemĂĄs, nuestro enfoque proporciona un mĂ©todo adecuado para aplicar la informaciĂłn integral formulada en la plantilla de consulta SPARQL para navegar en los recursos LOD en el dominio abierto. Las principales contribuciones de este trabajo son los siguientes: 1. El desarrollo de la arquitectura ScoQAS integrada con componentes comunes y especĂficos compatibles con ontologĂas de dominio cerrado y abierto. 2. El anĂĄlisis de la pregunta del usuario y la construcciĂłn de una estructura de informaciĂłn sintĂĄctico-semĂĄntica de las preguntas (QSiS), que estĂĄ constituida por varios procesos de la metodologĂa: clasificaciĂłn de preguntas, determinaciĂłn del Tipo de Respuesta Esperada (EAT) y las restricciones generadas. 3. La presentaciĂłn de un patrĂłn empĂrico basado en la estructura semĂĄntica para clasificar las preguntas y generalizar las restricciones heurĂsticas para formular las relaciones entre las caracterĂsticas en el patrĂłn reconocido en tĂ©rminos sintĂĄcticos y semĂĄnticos. 4. El desarrollo de un QGraph sintĂĄctico-semĂĄntico para representar los componentes centrales de la pregunta. 5. La presentaciĂłn de la respuesta inferida a partir de un grafo empĂrico en el dominio cerrado. En pocas palabras, se presenta un sistema semĂĄntico de QA que proporciona algunos resultados experimentales sobre los dominios cerrados y abiertos. La eficiencia del ScoQAS se evalĂșa utilizando medidas tales como una precisiĂłn, cobertura y la medida-F en desafĂos LOD para el dominio abierto. Para el dominio cerrado, nos centramos en la evaluaciĂłn cuantitativa; su precisiĂłn se analiza en una ontologĂa empresarial. La falta de un banco la pruebas predefinidas es uno de los principales desafĂos de la evaluaciĂłn en el primer escenario. Por lo tanto, definimos medidas que demuestran la complejidad real del problema y la eficiencia real de las soluciones. Los resultados del anĂĄlisis corroboran el rendimient
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