Using domain knowledge for fostering the collaborative ability of a web dialogue system

2011 ◽  
Author(s):  
Marta Gatius ◽  
Meritxell Gonzalez
Keyword(s):  
Domain Knowledge ◽  
Dialogue System

DOMAIN KNOWLEDGE ACQUISITION AND PLAN RECOGNITION BY PROBABILISTIC REASONING

2004 ◽  
Vol 13 (02) ◽  
pp. 333-365
Author(s):  
MANOLIS MARAGOUDAKIS ◽  
ARISTOMENIS THANOPOULOS ◽  
KYRIAKOS SGARBAS ◽  
NIKOS FAKOTAKIS
Keyword(s):  
Natural Language ◽  
Domain Knowledge ◽  
Probabilistic Reasoning ◽  
Probability Distributions ◽  
Semantic Representation ◽  
Community Acquired Pneumonia ◽  
Plan Recognition ◽  
Dialogue System ◽  
Scoring Method ◽  
Text Corpora

This paper introduces a statistical framework for extracting medical domain knowledge from heterogeneous corpora. The acquired information is incorporated into a natural language understanding agent and applied to DIKTIS, an existing web-based educational dialogue system for the chemotherapy of nosocomial and community acquired pneumonia, aiming at providing a more intelligent natural language interaction. Unlike the majority of existing dialogue understanding engines, the presented system automatically encodes semantic representation of a user's query using Bayesian networks. The structure of the networks is determined from annotated dialogue corpora using the Bayesian scoring method, thus eliminating the tedious and costly process of manually coding domain knowledge. The conditional probability distributions are estimated during a training phase using data obtained from the same set of dialogue acts. In order to cope with words absent from our restricted dialogue corpus, a separate offline module was incorporated, which estimates their semantic role from both medical and general raw text corpora, correlating them with known lexical-semantically similar words or predefined topics. Lexical similarity is identified on the basis of both contextual similarity and co-occurrence in conjunctive expressions. The evaluation of the platform was performed against the existing language natural understanding module of DIKTIS, the architecture of which is based on manually embedded domain knowledge.

scholarly journals MTSS: Learn from Multiple Domain Teachers and Become a Multi-Domain Dialogue Expert

2020 ◽  
Vol 34 (05) ◽  
pp. 8608-8615
Author(s):  
Shuke Peng ◽  
Feng Ji ◽  
Zehao Lin ◽  
Shaobo Cui ◽  
Haiqing Chen ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Single Domain ◽  
Student Model ◽  
State Representation ◽  
Dialogue System ◽  
Specific Domain ◽  
Domain Specific ◽  
School Teaching ◽  
Novel Method ◽  
Multiple Domain

How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits the quality of dialogue policy, and further affects the generated response. In this paper, we propose a novel method to acquire a satisfying policy and subtly circumvent the knotty dialogue state representation problem in the multi-domain setting. Inspired by real school teaching scenarios, our method is composed of multiple domain-specific teachers and a universal student. Each individual teacher only focuses on one specific domain and learns its corresponding domain knowledge and dialogue policy based on a precisely extracted single domain dialogue state representation. Then, these domain-specific teachers impart their domain knowledge and policies to a universal student model and collectively make this student model a multi-domain dialogue expert. Experiment results show that our method reaches competitive results with SOTAs in both multi-domain and single domain setting.

Automated Assessment of Domain Knowledge With Online Knowledge Mapping

2006 ◽  
Author(s):  
Gregory K. W. K. Chung ◽  
Eva L. Baker ◽  
David G. Brill ◽  
Ravi Sinha ◽  
Farzad Saadat ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Knowledge Mapping ◽  
Automated Assessment

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

Classification of means and methods of the Web semantic retrieval

2017 ◽  
pp. 030-050
Author(s):  
J.V. Rogushina ◽  
Keyword(s):  
Search Engines ◽  
Domain Knowledge ◽  
Information Needs ◽  
Web Search ◽  
User Interaction ◽  
Query Languages ◽  
Semantic Search ◽  
Semantic Retrieval ◽  
The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

Sign in / Sign up

Export Citation Format

Share Document