Research on Domain Adaptation for SMT Based on Specific Domain Knowledge

2016 ◽  
pp. 43-60 ◽  
Author(s):  
Yanqing He ◽  
Liang Ding ◽  
Ying Li
Keyword(s):  
Domain Knowledge ◽  
Domain Adaptation ◽  
Specific Domain

Application of a methodological approach to compare ontologies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yudith Cardinale ◽  
Maria Alejandra Cornejo-Lupa ◽  
Alexander Pinto-De la Gala ◽  
Regina Ticona-Herrera
Keyword(s):  
Domain Knowledge ◽  
Design Methodology ◽  
Methodological Approach ◽  
Quality Model ◽  
Specific Domain ◽  
Content Type ◽  
Localization And Mapping ◽  
Golden Standard ◽  
Knowledge Levels ◽  
Practical Implications

Purpose This study aims to the OQuaRE quality model to the developed methodology. Design/methodology/approach Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them. Findings In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain. Practical implications To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains. Originality/value Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

A fast genetic algorithm for solving architectural design optimization problems

2015 ◽  
Vol 29 (4) ◽  
pp. 457-469 ◽  
Author(s):  
Zhouzhou Su ◽  
Wei Yan
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Domain Knowledge ◽  
Architectural Design ◽  
Optimization Problems ◽  
Computing Time ◽  
Divide And Conquer ◽  
Specific Domain ◽  
Building Performance Simulation ◽  
Simulation And Optimization

AbstractBuilding performance simulation and genetic algorithms are powerful techniques for helping designers make better design decisions in architectural design optimization. However, they are very time consuming and require a significant amount of computing power. More time is needed when two techniques work together. This has become the primary impediment in applying design optimization to real-world projects. This study focuses on reducing the computing time in genetic algorithms when building simulation techniques are involved. In this study, we combine two techniques (offline simulation and divide and conquer) to effectively improve the run time in these architectural design optimization problems, utilizing architecture-specific domain knowledge. The improved methods are evaluated with a case study of a nursing unit design to minimize the nurses’ travel distance and maximize daylighting performance in patient rooms. Results show the computing time can be saved significantly during the simulation and optimization process.

scholarly journals Abstractive Text Summary of COVID-19 Documents based on LSTM Method and Word Embedding

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 1011-1022
Author(s):  
Saja Naeem Turky ◽  
Ahmed Sabah Ahmed AL-Jumaili ◽  
Rajaa K. Hasoun
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Input Sequence ◽  
Word Embedding ◽  
Suggested Model ◽  
Specific Domain ◽  
Scientific Texts ◽  
Proposed Model ◽  
Pass Through ◽  
Single Statement

An abstractive summary is a process of producing a brief and coherent summary that contains the original text's main concepts. In scientific texts, summarization has generally been restricted to extractive techniques. Abstractive methods that use deep learning have proven very effective in summarizing articles in public fields, like news documents. Because of the difficulty of the neural frameworks for learning specific domain- knowledge especially in NLP task, they haven't been more applied to documents that are related to a particular domain such as the medical domain. In this study, an abstractive summary is proposed. The proposed system is applied to the COVID-19 dataset which a collection of science documents linked to the coronavirus and associated illnesses, in this work 12000 samples from this dataset have been used. The suggested model is an abstractive summary model that can read abstracts of Covid-19 papers then create summaries in the style of a single-statement headline. A text summary model has been designed based on the LSTM method architecture. The proposed model includes using a glove model for word embedding which is converts input sequence to vector forms, then these vectors pass through LSTM layers to produce the summary. The results indicate that using an LSTM and glove model for word embedding together improves the summarization system's performance. This system was evaluated by rouge metrics and it achieved (43.6, 36.7, 43.6) for Rouge-1, Rouge-2, and Rouge-L respectively.

A Bisociative Design Framework for Knowledge Discovery Across Seemingly Unrelated Product Domains

2012 ◽  
Author(s):  
Conrad S. Tucker ◽  
Sung Woo Kang
Keyword(s):  
Domain Knowledge ◽  
Current Data ◽  
Data Driven ◽  
Design Framework ◽  
Specific Domain ◽  
Cross Domain ◽  
Product Design Process ◽  
Product Domains ◽  
Multiple Domains ◽  
Novel Design

The Bisociative Design framework proposed in this work aims to quantify hidden, previously unknown design synergies/insights across seemingly unrelated product domains. Despite the overabundance of data characterizing the digital age, designers still face tremendous challenges in transforming data into knowledge throughout the design processes. Data driven methodologies play a significant role in the product design process ranging from customer preference modeling to detailed engineering design. Existing data driven methodologies employed in the design community generate mathematical models based on data relating to a specific domain and are therefore constrained in their ability to discover novel design insights beyond the domain itself (I.e., cross domain knowledge). The Bisociative Design framework proposed in this work overcomes the limitations of current data driven design methodologies by decomposing design artifacts into form patterns, function patterns and behavior patterns and then evaluating potential cross-domain design insights through a proposed multidimensional Bisociative Design metric. A hybrid marine model involving multiple domains (capable of flight and marine navigation) is used as a case study to demonstrate the proposed Bisociative Design framework and explain how associations and novel design models can be generated through the discovery of hidden, previously unknown patterns across multiple, unrelated domains.

FactQA: question answering over domain knowledge graph based on two-level query expansion

2019 ◽  
Vol 54 (1) ◽  
pp. 34-63 ◽  
Author(s):  
Xiaoming Zhang ◽  
Mingming Meng ◽  
Xiaoling Sun ◽  
Yu Bai
Keyword(s):  
Domain Knowledge ◽  
Query Expansion ◽  
Question Answering ◽  
Knowledge Graph ◽  
Metallic Materials ◽  
Specific Domain ◽  
Content Type ◽  
Domain Specific ◽  
Data Matching ◽  
Logical Rules

Purpose With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG. Design/methodology/approach The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method. Findings The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches. Originality/value Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.

Unifying Top-Down Views by Task-Specific Domain Adaptation

2020 ◽  
pp. 1-14
Author(s):  
Jianzhe Lin ◽  
Tianze Yu ◽  
Lichao Mou ◽  
Xiaoxiang Zhu ◽  
Rabab Kreidieh Ward ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Top Down ◽  
Specific Domain
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