Query Processing over Multiple Knowledge Bases and Text Documents

2021 ◽  
Author(s):  
Marika Nakano ◽  
Toshiyuki Amagasa
Keyword(s):  
Query Processing ◽  
Knowledge Bases ◽  
Text Documents

scholarly journals A semi-automatic approach to construct Vietnamese ontology from online text

2012 ◽  
Vol 13 (5) ◽  
pp. 148 ◽  
Author(s):  
Bao-An Nguyen ◽  
Don-Lin Yang
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Expert Knowledge ◽  
Relation Extraction ◽  
Knowledge Bases ◽  
Instructional Materials ◽  
Formal Representation ◽  
Text Documents ◽  
Ontology Construction ◽  
Question Answering Systems

An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with much needed resources. In such systems, ontology construction is one of the most important phases. Since there are abundant documents on the Internet, useful learning materials can be acquired openly with the use of an ontology.  However, due to the lack of system support for ontology construction, it is difficult to construct self-instructional materials for Vietnamese people. In general, the cost of manual acquisition of ontologies from domain documents and expert knowledge is too high. Therefore, we present a support system for Vietnamese ontology construction using pattern-based mechanisms to discover Vietnamese concepts and conceptual relations from Vietnamese text documents. In this system, we use the combination of statistics-based, data mining, and Vietnamese natural language processing methods to develop concept and conceptual relation extraction algorithms to discover knowledge from Vietnamese text documents. From the experiments, we show that our approach provides a feasible solution to build Vietnamese ontologies used for supporting systems in education.<br /><br />

scholarly journals A Survey on Aspect-Based Sentiment Classification

2021 ◽  
Author(s):  
Gianni Brauwers ◽  
Flavius Frasincar
Keyword(s):  
State Of The Art ◽  
Knowledge Bases ◽  
Sentiment Classification ◽  
Automatic Extraction ◽  
Learning Models ◽  
Text Documents ◽  
Fine Grained ◽  
Knowledge Based ◽  
Transformer Model ◽  
The Web

With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

Mining Free Text for Structure

Data Mining ◽  
2011 ◽  
pp. 278-300
Author(s):  
Vladimir A. Kulyukin ◽  
Robin Burke
Keyword(s):  
Structural Organization ◽  
Knowledge Engineering ◽  
Knowledge Bases ◽  
Free Text ◽  
Learning Approaches ◽  
Structural Components ◽  
Text Documents ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Ultimate Objective

Knowledge of the structural organization of information in documents can be of significant assistance to information systems that use documents as their knowledge bases. In particular, such knowledge is of use to information retrieval systems that retrieve documents in response to user queries. This chapter presents an approach to mining free-text documents for structure that is qualitative in nature. It complements the statistical and machine-learning approaches, insomuch as the structural organization of information in documents is discovered through mining free text for content markers left behind by document writers. The ultimate objective is to find scalable data mining (DM) solutions for free-text documents in exchange for modest knowledge-engineering requirements. The problem of mining free text for structure is addressed in the context of finding structural components of files of frequently asked questions (FAQs) associated with many USENET newsgroups. The chapter describes a system that mines FAQs for structural components. The chapter concludes with an outline of possible future trends in the structural mining of free text.

Efficient spatio-temporal RDF query processing in large dynamic knowledge bases

2019 ◽  
Author(s):  
Akrivi Vlachou ◽  
Christos Doulkeridis ◽  
Apostolos Glenis ◽  
Georgios M. Santipantakis ◽  
George A. Vouros
Keyword(s):  
Query Processing ◽  
Knowledge Bases ◽  
Spatio Temporal

Ontology Enhancement for Including Newly Acquired Knowledge About Concept Descirptions and Answering Imprecise Queries

2006 ◽  
pp. 189-225 ◽  
Author(s):  
Lipika Dey ◽  
Muhammad Abulaish
Keyword(s):  
Information Extraction ◽  
Query Processing ◽  
Processing System ◽  
Fuzzy Reasoning ◽  
Property Value ◽  
Text Documents ◽  
Text Information ◽  
Structured Knowledge ◽  
Confidence Value ◽  
User Queries

This chapter presents a text-mining-based ontology enhancement and query-processing system. The key ideas introduced here are that of learning and including imprecise concept descriptions into ontology structures. This is essential for ontology-based text information extraction since it is not necessary that text description of the concepts or user-specified descriptions will exactly match stored concept descriptions. The traditional property-value framework for concept description has been extended to a property-value-qualifier framework for this purpose. The system also supports ontology enhancement by identifying, defining, and adding new precise and imprecise concept descriptions mined from text documents. The acquired knowledge is stored in a structured knowledge base for answering user queries. Since user queries may contain concept descriptions, which do not exactly match stored or known concepts, the query processor uses fuzzy reasoning for query processing. Each answer is accompanied by a confidence value that reflects its similarity to the original query concept.

Sign in / Sign up

Export Citation Format

Share Document