The REA Pattern, Knowledge Structures, and Conceptual Modeling Performance

2005 ◽  
Vol 19 (2) ◽  
pp. 57-77 ◽  
Author(s):  
Gregory J. Gerard
Keyword(s):  
Domain Knowledge ◽  
Conceptual Models ◽  
Conceptual Modeling ◽  
Knowledge Structure ◽  
Specific Pattern ◽  
Accounting Information Systems ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Business Setting ◽  
Structured Knowledge

Most database textbooks on conceptual modeling do not cover domainspecific patterns. The texts emphasize notation, apparently assuming that notation enables individuals to correctly model domain-specific knowledge acquired from experience. However, the domain knowledge acquired may not aid in the construction of conceptual models if it is not structured to support conceptual modeling. This study uses the Resources Events Agents (REA) pattern as an example of a domain-specific pattern that can be encoded as a knowledge structure for conceptual modeling of accounting information systems (AIS), and tests its effects on the accuracy of conceptual modeling in a familiar business setting. Fifty-three undergraduate and forty-six graduate students completed recall tasks designed to measure REA knowledge structure. The accuracy of participants' conceptual models was positively related to REA knowledge structure. Results suggest it is insufficient to know only conceptual modeling notation because structured knowledge of domain-specific patterns reduces design errors.

scholarly journals Cross-Fertilizing Deep Web Analysis and Ontology Enrichment

2017 ◽  
Author(s):  
Marilena Oita ◽  
Antoine Amarilli ◽  
Pierre Senellart
Keyword(s):  
Domain Knowledge ◽  
Deep Web ◽  
Web Pages ◽  
Complete Understanding ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Web Crawlers ◽  
New Perspective ◽  
The Impact

Deep Web databases, whose content is presented as dynamically-generated Web pages hidden behind forms, have mostly been left unindexed by search engine crawlers. In order to automatically explore this mass of information, many current techniques assume the existence of domain knowledge, which is costly to create and maintain. In this article, we present a new perspective on form understanding and deep Web data acquisition that does not require any domain-specific knowledge. Unlike previous approaches, we do not perform the various steps in the process (e.g., form understanding, record identification, attribute labeling) independently but integrate them to achieve a more complete understanding of deep Web sources. Through information extraction techniques and using the form itself for validation, we reconcile input and output schemas in a labeled graph which is further aligned with a generic ontology. The impact of this alignment is threefold: first, the resulting semantic infrastructure associated with the form can assist Web crawlers when probing the form for content indexing; second, attributes of response pages are labeled by matching known ontology instances, and relations between attributes are uncovered; and third, we enrich the generic ontology with facts from the deep Web.

Incremental Ontology Population and Enrichment through Semantic-based Text Mining

2015 ◽  
Vol 11 (3) ◽  
pp. 44-66 ◽  
Author(s):  
Saira Gillani ◽  
Andrea Ko
Keyword(s):  
Text Mining ◽  
Domain Knowledge ◽  
Learning System ◽  
Domain Specific ◽  
Automatic Categorization ◽  
New Concepts ◽  
Domain Specific Knowledge ◽  
E Learning ◽  
Ontology Population ◽  
It Audit

Higher education and professional trainings often apply innovative e-learning systems, where ontologies are used for structuring domain knowledge. To provide up-to-date knowledge for the students, ontology has to be maintained regularly. It is especially true for IT audit and security domain, because technology is changing fast. However manual ontology population and enrichment is a complex task that require professional experience involving a lot of efforts. The authors' paper deals with the challenges and possible solutions for semi-automatic ontology enrichment and population. ProMine has two main contributions; one is the semantic-based text mining approach for automatically identifying domain-specific knowledge elements; the other is the automatic categorization of these extracted knowledge elements by using Wiktionary. ProMine ontology enrichment solution was applied in IT audit domain of an e-learning system. After ten cycles of the application ProMine, the number of automatically identified new concepts are tripled and ProMine categorized new concepts with high precision and recall.

Research on feature-based opinion mining using topic maps

2016 ◽  
Vol 34 (3) ◽  
pp. 435-456 ◽  
Author(s):  
Lixin Xia ◽  
Zhongyi Wang ◽  
Chen Chen ◽  
Shanshan Zhai
Keyword(s):  
Domain Knowledge ◽  
Opinion Mining ◽  
Specific Knowledge ◽  
Content Type ◽  
Topic Maps ◽  
Domain Specific ◽  
Product Features ◽  
Domain Specific Knowledge ◽  
Feature Based ◽  
Integrate Domain

Purpose Opinion mining (OM), also known as “sentiment classification”, which aims to discover common patterns of user opinions from their textual statements automatically or semi-automatically, is not only useful for customers, but also for manufacturers. However, because of the complexity of natural language, there are still some problems, such as domain dependence of sentiment words, extraction of implicit features and others. The purpose of this paper is to propose an OM method based on topic maps to solve these problems. Design/methodology/approach Domain-specific knowledge is key to solve problems in feature-based OM. On the one hand, topic maps, as an ontology framework, are composed of topics, associations, occurrences and scopes, and can represent a class of knowledge representation schemes. On the other hand, compared with ontology, topic maps have many advantages. Thus, it is better to integrate domain-specific knowledge into OM based on topic maps. This method can make full use of the semantic relationships among feature words and sentiment words. Findings In feature-level OM, most of the existing research associate product features and opinions by their explicit co-occurrence, or use syntax parsing to judge the modification relationship between opinion words and product features within a review unit. They are mostly based on the structure of language units without considering domain knowledge. Only few methods based on ontology incorporate domain knowledge into feature-based OM, but they only use the “is-a” relation between concepts. Therefore, this paper proposes feature-based OM using topic maps. The experimental results revealed that this method can improve the accuracy of the OM. The findings of this study not only advance the state of OM research but also shed light on future research directions. Research limitations/implications To demonstrate the “feature-based OM using topic maps” applications, this work implements a prototype that helps users to find their new washing machines. Originality/value This paper presents a new method of feature-based OM using topic maps, which can integrate domain-specific knowledge into feature-based OM effectively. This method can improve the accuracy of the OM greatly. The proposed method can be applied across various application domains, such as e-commerce and e-government.

Workflows Científicos com Apoio de Bases de Conhecimento em Tempo Real

2020 ◽  
Author(s):  
Victor S. Bursztyn ◽  
Jonas Dias ◽  
Marta Mattoso
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Large Scale ◽  
Workflow Engine ◽  
Workflow Execution ◽  
Human In The Loop ◽  
Domain Specific ◽  
Political Sciences ◽  
Provenance Data ◽  
Domain Specific Knowledge

One major challenge in large-scale experiments is the analytical capacity to contrast ongoing results with domain knowledge. We approach this challenge by constructing a domain-specific knowledge base, which is queried during workflow execution. We introduce K-Chiron, an integrated solution that combines a state-of-the-art automatic knowledge base construction (KBC) system to Chiron, a well-established workflow engine. In this work we experiment in the context of Political Sciences to show how KBC may be used to improve human-in-the-loop (HIL) support in scientific experiments. While HIL in traditional domain expert supervision is done offline, in K-Chiron it is done online, i.e. at runtime. We achieve results in less laborious ways, to the point of enabling a breed of experiments that could be unfeasible with traditional HIL. Finally, we show how provenance data could be leveraged with KBC to enable further experimentation in more dynamic settings.

Chapter 4. Graph Reasoning Networks and Applications

2021 ◽  
Author(s):  
Qingxing Cao ◽  
Wentao Wan ◽  
Xiaodan Liang ◽  
Liang Lin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Domain Knowledge ◽  
Feature Learning ◽  
External Information ◽  
Feature Representations ◽  
Domain Specific ◽  
The Neural Network ◽  
Structured Knowledge ◽  
Knowledge Graphs

Despite the significant success in various domains, the data-driven deep neural networks compromise the feature interpretability, lack the global reasoning capability, and can’t incorporate external information crucial for complicated real-world tasks. Since the structured knowledge can provide rich cues to record human observations and commonsense, it is thus desirable to bridge symbolic semantics with learned local feature representations. In this chapter, we review works that incorporate different domain knowledge into the intermediate feature representation.These methods firstly construct a domain-specific graph that represents related human knowledge. Then, they characterize node representations with neural network features and perform graph convolution to enhance these symbolic nodes via the graph neural network(GNN).Lastly, they map the enhanced node feature back into the neural network for further propagation or prediction. Through integrating knowledge graphs into neural networks, one can collaborate feature learning and graph reasoning with the same supervised loss function and achieve a more effective and interpretable way to introduce structure constraints.

Conceptual models for automatic generation of knowledge-acquisition tools

1993 ◽  
Vol 8 (1) ◽  
pp. 27-47 ◽  
Author(s):  
Henrik Eriksson ◽  
Mark A. Musen
Keyword(s):  
Knowledge Acquisition ◽  
Domain Knowledge ◽  
Conceptual Models ◽  
Automatic Generation ◽  
Design Models ◽  
Domain Specific ◽  
Generic Design ◽  
Relevant Domain

AbstractInteractive knowledge-acquisition (KA) programs allow users to enter relevant domain knowledge according to a model predefined by the tool developers. KA tools are designed to provide conceptual models of the knowledge to their users. Many different classes of models are possible, resulting in different categories of tools. Whenever it is possible to describe KA tools according to explicit conceptual models, it is also possible to edit the models and to instantiate new KA tools automatically for specialized purposes. Several meta-tools that address this task have been implemented. Meta-tools provide developers of domain-specific KA tools with generic design models, or meta-views, of the emerging KA tools. The same KA tool can be specified according to several alternative meta-views.

scholarly journals A General Method for Transferring Explicit Knowledge into Language Model Pretraining

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ruiqing Yan ◽  
Lanchang Sun ◽  
Fang Wang ◽  
Xiaoming Zhang
Keyword(s):  
Domain Knowledge ◽  
Explicit Knowledge ◽  
Language Model ◽  
Background Knowledge ◽  
Knowledge Bases ◽  
Language Models ◽  
Domain Specific ◽  
Text Understanding ◽  
Domain Specific Knowledge ◽  
General Method

Recently, pretrained language models, such as Bert and XLNet, have rapidly advanced the state of the art on many NLP tasks. They can model implicit semantic information between words in the text. However, it is solely at the token level without considering the background knowledge. Intuitively, background knowledge influences the efficacy of text understanding. Inspired by this, we focus on improving model pretraining by leveraging external knowledge. Different from recent research that optimizes pretraining models by knowledge masking strategies, we propose a simple but general method to transfer explicit knowledge with pretraining. To be specific, we first match knowledge facts from a knowledge base (KB) and then add a knowledge injunction layer to a transformer directly without changing its architecture. This study seeks to find the direct impact of explicit knowledge on model pretraining. We conduct experiments on 7 datasets using 5 knowledge bases in different downstream tasks. Our investigation reveals promising results in all the tasks. The experiment also verifies that domain-specific knowledge is superior to open-domain knowledge in domain-specific task, and different knowledge bases have different performances in different tasks.

Cross-Disciplinary Collaboration

2020 ◽  
pp. 21-32
Author(s):  
Daphne Leong
Keyword(s):  
Intercultural Communication ◽  
Domain Knowledge ◽  
Mutual Influence ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Cross Domain ◽  
Domain Specific Knowledge ◽  
Epistemic Objects

This chapter describes the things and people that facilitate collaboration across disciplines: shared items, shared objectives, and shared agents. (These concepts draw from literature on collaboration in the sciences and from research on intercultural communication.) Shared items function differently from discipline to discipline, while being identifiable across disciplines. Shared objectives comprise activity objects, the prospective outcomes of collaboration, and epistemic objects, knowledge sought. Shared agents function within and across two or more disciplines. In this book, shared items are represented primarily by scores (and recordings), activity objects by the book’s chapters, epistemic objects by interpretations of pieces and of analysis-performance relations, and shared agents by scholar-performers or performer-scholars. Mechanisms and processes of collaboration are briefly described: strategies for collaborating when views diverge, and degrees of collaborative convergence (working in parallel, translating or mediating knowledge for mutual influence, transforming domain-specific knowledge into new cross-domain knowledge).

scholarly journals An ADOxx Based Environment for Problem Based Learning in Manufacturing Systems Design

2019 ◽  
Vol 290 ◽  
pp. 14003 ◽  
Author(s):  
Ion Dan Mironescu
Keyword(s):  
Domain Knowledge ◽  
Manufacturing Systems ◽  
Learning Experience ◽  
Problem Based Learning ◽  
Systems Design ◽  
Learning By Doing ◽  
Design Solution ◽  
Learning Material ◽  
Domain Specific ◽  
Domain Specific Knowledge

The Problem Based Learning (PBL) as student centred approach and learning-by-doing method is suited for the modern higher education. However, the first contact with the method can be overwhelming for the students, in the absence of prior domain knowledge. The preparation of the learning material can be time and resource consuming for the teacher. The goal of the research was the implementation of an environment that should enhance the learning experience for the student and reduce the implementation burden for the teacher. The environment is based on the ADOxx platform and allows the collaboration of the learner teams and the teacher-learner interaction on three levels. The Metamodeling level supports the development of the domain-specific language used in the modelling of the manufacturing system; this activity stimulates and directs the gathering and consolidation of domain-specific knowledge. The modelling level allows the development of alternative design solution using models of the factory components. The Simulation level allows the analysis of these variants. The environment supports the teacher in developing instructional scaffolding and uses cases to ease the learners the first time contact with PBL. The functionality of the environment is presented using the case of designing a flexible food production line.

scholarly journals myDIG: Personalized Illicit Domain-Specific Knowledge Discovery with No Programming

2019 ◽  
Vol 11 (3) ◽  
pp. 59 ◽  
Author(s):  
Mayank Kejriwal ◽  
Pedro Szekely
Keyword(s):  
Law Enforcement ◽  
Knowledge Discovery ◽  
Securities Fraud ◽  
Specific Knowledge ◽  
Domain Experts ◽  
Domain Specific ◽  
Programming Effort ◽  
Domain Specific Knowledge ◽  
Structured Knowledge ◽  
Set Up

With advances in machine learning, knowledge discovery systems have become very complicated to set up, requiring extensive tuning and programming effort. Democratizing such technology so that non-technical domain experts can avail themselves of these advances in an interactive and personalized way is an important problem. We describe myDIG, a highly modular, open source pipeline-construction system that is specifically geared towards investigative users (e.g., law enforcement) with no programming abilities. The myDIG system allows users both to build a knowledge graph of entities, relationships, and attributes for illicit domains from a raw HTML corpus and also to set up a personalized search interface for analyzing the structured knowledge. We use qualitative and quantitative data from five case studies involving investigative experts from illicit domains such as securities fraud and illegal firearms sales to illustrate the potential of myDIG.

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