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.

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 Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports

2021 ◽  
Vol 3 (2) ◽  
pp. 299-317
Author(s):  
Patrick Schrempf ◽  
Hannah Watson ◽  
Eunsoo Park ◽  
Maciej Pajak ◽  
Hamish MacKinnon ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Imaging Data ◽  
Data Synthesis ◽  
Domain Specific ◽  
Radiology Reports ◽  
Training Examples ◽  
Analysis Models ◽  
Difficult Cases

Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previously, we showed that, by extending BERT with a per-label attention mechanism, we can train a single model to perform automatic extraction of many labels in parallel. However, if we rely on pure data-driven learning, the model sometimes fails to learn critical features or learns the correct answer via simplistic heuristics (e.g., that “likely” indicates positivity), and thus fails to generalise to rarer cases which have not been learned or where the heuristics break down (e.g., “likely represents prominent VR space or lacunar infarct” which indicates uncertainty over two differential diagnoses). In this work, we propose template creation for data synthesis, which enables us to inject expert knowledge about unseen entities from medical ontologies, and to teach the model rules on how to label difficult cases, by producing relevant training examples. Using this technique alongside domain-specific pre-training for our underlying BERT architecture i.e., PubMedBERT, we improve F1 micro from 0.903 to 0.939 and F1 macro from 0.512 to 0.737 on an independent test set for 33 labels in head CT reports for stroke patients. Our methodology offers a practical way to combine domain knowledge with machine learning for text classification tasks.

scholarly journals A META-TOOL TO SUPPORT THE DEVELOPMENT OF KNOWLEDGE ENGINEERING METHODOLOGIES AND PROJECTS

2012 ◽  
Vol 22 (08) ◽  
pp. 1055-1083
Author(s):  
JOSÉ ELOY FLÓREZ ◽  
JAVIER CARBÓ ◽  
FERNANDO FERNÁNDEZ
Keyword(s):  
Knowledge Acquisition ◽  
Ad Hoc ◽  
Knowledge Engineering ◽  
Domain Specific ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Combined Application ◽  
Processing Information ◽  
Knowledge Acquisition Methods ◽  
Applied Knowledge

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).

scholarly journals ONTOLOGY-BASED GENERATION OF MULTILINGUAL QUESTIONS FOR ASSESSMENT IN MEDICAL EDUCATION

2020 ◽  
pp. 001
Author(s):  
Maja Radović ◽  
Nenad Petrović ◽  
Milorad Tošić
Keyword(s):  
Medical Education ◽  
Knowledge Representation ◽  
Teaching And Learning ◽  
English Language ◽  
Automatic Generation ◽  
Assessment Process ◽  
Innovative Teaching ◽  
Domain Specific ◽  
Level Of Automation ◽  
High Level

The requirements of state-of-the-art curricula and teaching processes in medical education have brought both new and improved the existing assessment methods. Recently, several promising methods have emerged, among them the Comprehensive Integrative Puzzle (CIP), which shows great potential. However, the construction of such questions requires high efforts of a team of experts and is time-consuming. Furthermore, despite the fact that English language is accepted as an international language, for educational purposes there is also a need for representing data and knowledge in native language. In this paper, we present an approach for automatic generation of CIP assessment questions based on using ontologies for knowledge representation. In this way, it is possible to provide multilingual support in the teaching and learning process because the same ontological concept can be applied to corresponding language expressions in different languages. The proposed approach shows promising results indicated by dramatic speeding up of construction of CIP questions compared to manual methods. The presented results represent a strong indication that adoption of ontologies for knowledge representation may enable scalability in multilingual domain-specific education regardless of the language used. High level of automation in the assessment process proven on the CIP method in medical education as one of the most challenging domains, promises high potential for new innovative teaching methodologies in other educational domains as well.

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.

scholarly journals Automatic Generation of Truffle-based Interpreters for Domain-Specific Languages.

2020 ◽  
Vol 19 (2) ◽  
pp. 1:1
Author(s):  
Manuel Leduc ◽  
Gwendal Jouneaux ◽  
Thomas Degueule ◽  
Gurvan Le Guernic ◽  
Olivier Barais ◽  
...  
Keyword(s):  
Automatic Generation ◽  
Domain Specific Languages ◽  
Domain Specific

scholarly journals Modeling Biological Systems Using Crowdsourcing

2018 ◽  
Vol 43 (3) ◽  
pp. 219-243 ◽  
Author(s):  
Szymon Wasik
Keyword(s):  
Systems Biology ◽  
Serious Games ◽  
Domain Knowledge ◽  
Biological Systems ◽  
Effective Technique ◽  
Design Models ◽  
Modeling Systems ◽  
Vast Network

Abstract Crowdsourcing is a very effective technique for outsourcing work to a vast network usually comprising anonymous people. In this study, we review the application of crowdsourcing to modeling systems originating from systems biology. We consider a variety of verified approaches, including well-known projects such as EyeWire, FoldIt, and DREAM Challenges, as well as novel projects conducted at the European Center for Bioinformatics and Genomics. The latter projects utilized crowdsourced serious games to design models of dynamic biological systems, and it was demonstrated that these models could be used successfully to involve players without domain knowledge. We conclude the review of these systems by providing 10 guidelines to facilitate the efficient use of crowdsourcing.

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