Mixed-Level Knowledge Representations and Variable-Depth Inference in Natural Language Processing

1997 ◽  
Vol 06 (04) ◽  
pp. 481-509 ◽  
Author(s):  
Michael Hess
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Search Strategy ◽  
Horn Clause ◽  
Variable Depth ◽  
Levels Of Abstraction ◽  
Knowledge Representations ◽  
Depth Inference ◽  
Different Levels

A system is described that uses a mixed-level knowledge representation based on standard Horn Clause Logic to represent (part of) the meaning of natural language documents. A variable-depth search strategy is outlined that distinguishes between the different levels of abstraction in the knowledge representation to locate specific passages in the documents. A detailed description of the linguistic aspects of the system is given. Mixed-level representations as well as variable-depth search strategies are applicable in fields outside that of NLP.

Guru

2012 ◽  
pp. 156-171 ◽  
Author(s):  
Andrew M. Olney ◽  
Natalie K. Person ◽  
Arthur C. Graesser
Keyword(s):  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Understanding ◽  
Natural Language Generation ◽  
Language Understanding ◽  
Language Generation ◽  
Processing Techniques

The authors discuss Guru, a conversational expert ITS. Guru is designed to mimic expert human tutors using advanced applied natural language processing techniques including natural language understanding, knowledge representation, and natural language generation.

Alternative Ways for Knowledge Collection, Indexing and Robust Language Retrieval

1998 ◽  
Vol 37 (04/05) ◽  
pp. 315-326 ◽  
Author(s):  
C. Lovis ◽  
A.-M. Rassinoux ◽  
J.-R. Scherrer ◽  
R. H. Baud
Keyword(s):  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Building Blocks ◽  
Linguistic Knowledge ◽  
Recent Experience ◽  
Medical Texts ◽  
Natural Components

AbstractDefinitions are provided of the key entities in knowledge representation for Natural Language Processing (NLP). Starting from the words, which are the natural components of any sentence, both the role of expressions and the decomposition of words into their parts are emphasized. This leads to the notion of concepts, which are either primitive or composite depending on the model where they are created. The problem of finding the most adequate degree of granularity for a concept is studied. From this reflection on basic Natural Language Processing components, four categories of linguistic knowledge are recognized, that are considered to be the building blocks of a Medical Linguistic Knowledge Base (MLKB). Following on the tracks of a recent experience in building a natural language-based patient encoding browser, a robust method for conceptual indexing and query of medical texts is presented with particular attention to the scheme of knowledge representation.

scholarly journals Ontology-Assisted Database Integration to Support Natural Language Processing and Biomedical Data-mining

2004 ◽  
Vol 1 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Jean-Luc Verschelde ◽  
Mariana Casella Dos Santos ◽  
Tom Deray ◽  
Barry Smith ◽  
Werner Ceusters
Keyword(s):  
Data Mining ◽  
Natural Language ◽  
Language Processing ◽  
Biomedical Ontology ◽  
Biological Data ◽  
Heterogeneous Databases ◽  
Database Integration ◽  
Biomedical Data ◽  
Biological Phenomena ◽  
Different Levels

Summary Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the purposes of making computers understand medical natural language.

scholarly journals Toward Requirements and Design Traceability Using Natural Language Processing

2018 ◽  
Vol 3 (7) ◽  
pp. 42 ◽  
Author(s):  
Omer Salih Dawood ◽  
Abd-El-Kader Sahraoui
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Unified Modeling Language ◽  
Rule Engine ◽  
Natural Languages ◽  
Unified Modeling ◽  
Requirement Document ◽  
Different Levels

The paper aimed to address the problem of incompleteness and inconsistency between requirements and design stages, and how to make efficient linking between these stages. Software requirements written in natural languages (NL), Natural Language Processing (NLP) can be used to process requirements. In our research we built a framework that can be used to generate design diagrams from requirements in semi-automatic way, and make traceability between requirements and design phases, and in contrast. Also framework shows how to manage traceability in different levels, and how to apply changes to different artifacts. Many traceability reports can be generated based on developed framework. After Appling this model we obtained good results. Based on our case study the model generate a class diagram depends on central rule engine, and traceability was built and can be managed in visualize manner. We proposed to continue this research as its very critical area by adding more Unified Modeling Language(UML) diagrams, and apply changes directly inside software requirement document.

scholarly journals A Lexical Knowledge Representation Model for Natural Language Understanding

2009 ◽  
Vol 1 (4) ◽  
pp. 17-35 ◽  
Author(s):  
Ping Chen ◽  
Wei Ding ◽  
Chengmin Ding
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Natural Language Understanding ◽  
Commonsense Reasoning ◽  
Lexical Knowledge ◽  
Language Understanding ◽  
Representation Model ◽  
Intelligent Information

Knowledge representation is essential for semantics modeling and intelligent information processing. For decades researchers have proposed many knowledge representation techniques. However, it is a daunting problem how to capture deep semantic information effectively and support the construction of a large-scale knowledge base efficiently. This article describes a new knowledge representation model, SenseNet, which provides semantic support for commonsense reasoning and natural language processing. SenseNet is formalized with a Hidden Markov Model. An inference algorithm is proposed to simulate human-like natural language understanding procedure. A new measurement, confidence, is introduced to facilitate the natural language understanding. The authors present a detailed case study of applying SenseNet to retrieving compensation information from company proxy filings.

scholarly journals A Lexical Knowledge Representation Model for Natural Language Understanding

2012 ◽  
pp. 193-210
Author(s):  
Ping Chen ◽  
Wei Ding ◽  
Chengmin Ding
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Natural Language Understanding ◽  
Commonsense Reasoning ◽  
Language Understanding ◽  
Representation Model ◽  
Intelligent Information ◽  
Representation Techniques

Knowledge representation is essential for semantics modeling and intelligent information processing. For decades researchers have proposed many knowledge representation techniques. However, it is a daunting problem how to capture deep semantic information effectively and support the construction of a large-scale knowledge base efficiently. This paper describes a new knowledge representation model, SenseNet, which provides semantic support for commonsense reasoning and natural language processing. SenseNet is formalized with a Hidden Markov Model. An inference algorithm is proposed to simulate human-like natural language understanding procedure. A new measurement, confidence, is introduced to facilitate the natural language understanding. The authors present a detailed case study of applying SenseNet to retrieving compensation information from company proxy filings.

Enhancing a Rigorous Reuse Process with Natural Language Requirement Specifications

2002 ◽  
pp. 129-142
Author(s):  
Laura Felice ◽  
Carmen Leonardi ◽  
Liliana Favre ◽  
Maria Virginia Mauco
Keyword(s):  
Natural Language ◽  
Formal Specifications ◽  
Levels Of Abstraction ◽  
Reusable Components ◽  
Reusable Component ◽  
Software Engineers ◽  
Language Requirement ◽  
The One ◽  
Requirement Specifications ◽  
Different Levels

Reusability is the ability to use the same software elements for constructing many different applications. Formal specifications can help to semiautomatic design processes based on reusable components. However, during the first stages of development, when the interaction with the stakeholders is crucial, the use of client-oriented requirements engineering techniques seems to be necessary in order to enhance the communication between the stakeholders and the software engineers. In this chapter, we propose a systematic reuse approach that integrates natural language requirement specifications with formal specifications in RSL (RAISE Specification Language). On the one hand, some heuristics are described to develop a formal specification in RSL starting from models belonging to the Requirements Baseline. On the other hand, we have defined a reusable component model that integrates RSL specifications at different levels of abstraction, as well as presented a process with reuse based on the model.

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