Ontology-Based Design Rationale Retrieval Supporting Natural Language Query

2014 ◽  
Author(s):  
Luye Li ◽  
Feiwei Qin ◽  
Shuming Gao ◽  
Xiaolian Qin
Keyword(s):  
Natural Language ◽  
Template Matching ◽  
Sparql Query ◽  
Design Knowledge ◽  
Prototype System ◽  
Design Domain ◽  
Design Rationale ◽  
Generating Method ◽  
Natural Language Query ◽  
Successful Retrieval

Design rationale is an important category of design knowledge. Effective reuse of design rationale depends on its successful retrieval. In this paper, an ontology-based design rationale retrieval approach is presented, based on which, users can input natural language questions to search what they want. First, a database of ontology-based design rationale is constructed, which supports SPARQL query. Then, several SPARQL templates are defined according to different knowledge query requirements in engineering design domain. Moreover, a SPARQL query generating method is proposed to generate SPARQL query from natural language query automatically through template matching and keywords extending. Finally, a design rationale retrieval prototype system is implemented, and the experimental results show the advantages of the proposed approach.

scholarly journals Enhanced SPARQL-based design rationale retrieval

2016 ◽  
Vol 30 (4) ◽  
pp. 406-423
Author(s):  
Luye Li ◽  
Shuming Gao ◽  
Ying Liu ◽  
Xiaolian Qin
Keyword(s):  
Natural Language ◽  
Template Matching ◽  
Sparql Query ◽  
Design Knowledge ◽  
Prototype System ◽  
Semantic Model ◽  
Design Rationale ◽  
Query Generation ◽  
Successful Retrieval ◽  
Issue Based Information System

AbstractDesign rationale (DR) is an important category within design knowledge, and effective reuse of it depends on its successful retrieval. In this paper, an ontology-based DR retrieval approach is presented, which allows users to search by entering normal queries such as questions in natural language. First, an ontology-based semantic model of DR is developed based on the extended issue-based information system-based DR representation in order to effectively utilize the semantics embedded in DR, and a database of ontology-based DR is constructed, which supports SPARQL queries. Second, two SPARQL query generation methods are proposed. The first method generates initial SPARQL queries from natural language queries automatically using template matching, and the other generates initial SPARQL queries automatically from DR record-based queries. In addition, keyword extension and optimization is conducted to enhance the SPARQL-based retrieval. Third, a design rationale retrieval prototype system is implemented. The experimental results show the advantages of the proposed approach.

scholarly journals An Approach to Design Rationale Retrieval Using Ontology-Aided Indexing

2013 ◽  
Author(s):  
Luye Li ◽  
Feiwei Qin ◽  
Shuming Gao
Keyword(s):  
Experimental Results ◽  
Design Knowledge ◽  
Prototype System ◽  
Design Rationale ◽  
Indexing Method ◽  
Successful Retrieval ◽  
Flexible Querying

Design rationale is an important category of design knowledge. Effective reuse of design rationale depends on its successful retrieval. In this paper, an approach to design rationale retrieval using ontology-aided indexing is presented. First, a design rationale ontology is designed based on the extended IBIS-based design rationale representation. Then, an ontology-aided indexing method is given to build indices for design rationale records. Furthermore, three kinds of query modes are developed to support flexible querying. Finally, a prototype system is implemented. The experimental results show the effectiveness of the proposed approach.

Single-shot Semantic Matching Network for Moment Localization in Videos

2021 ◽  
Vol 17 (3) ◽  
pp. 1-14
Author(s):  
Xinfang Liu ◽  
Xiushan Nie ◽  
Junya Teng ◽  
Li Lian ◽  
Yilong Yin
Keyword(s):  
Natural Language ◽  
Fixed Number ◽  
Single Shot ◽  
Semantic Matching ◽  
Matching Network ◽  
Attention Model ◽  
Semantic Relationships ◽  
Natural Language Query ◽  
Benchmark Datasets ◽  
Short Memory

Moment localization in videos using natural language refers to finding the most relevant segment from videos given a natural language query. Most of the existing methods require video segment candidates for further matching with the query, which leads to extra computational costs, and they may also not locate the relevant moments under any length evaluated. To address these issues, we present a lightweight single-shot semantic matching network (SSMN) to avoid the complex computations required to match the query and the segment candidates, and the proposed SSMN can locate moments of any length theoretically. Using the proposed SSMN, video features are first uniformly sampled to a fixed number, while the query sentence features are generated and enhanced by GloVe, long-term short memory (LSTM), and soft-attention modules. Subsequently, the video features and sentence features are fed to an enhanced cross-modal attention model to mine the semantic relationships between vision and language. Finally, a score predictor and a location predictor are designed to locate the start and stop indexes of the query moment. We evaluate the proposed method on two benchmark datasets and the experimental results demonstrate that SSMN outperforms state-of-the-art methods in both precision and efficiency.

Personalized knowledge push system based on design intent and user interest

2015 ◽  
Vol 230 (11) ◽  
pp. 1757-1772 ◽  
Author(s):  
Zhan-Song Wang ◽  
Ling Tian ◽  
Yuan-Hao Wu ◽  
Bei-Bei Liu
Keyword(s):  
Knowledge Acquisition ◽  
Conceptual Design ◽  
Current Knowledge ◽  
Target Function ◽  
Design Knowledge ◽  
Prototype System ◽  
Sorting Algorithm ◽  
Illustrative Case ◽  
User Interest ◽  
Design Intent

Existing knowledge provides important reference for designers in mechanical design activities. However, current knowledge acquisition methods based on information retrieval have the problem of inefficiency and low precision, which mainly meet the requirement for knowledge coverage. To improve the efficiency of knowledge acquisition and ensure the availability of design knowledge, this paper proposes a knowledge push service method based on design intent and user interest. First, the design intent model, which is mainly the formal expression of the target function of conceptual design, is built. Second, the user interest model that consists of domain themes and operation logs is built, and an automatic updating method of user interest is proposed. Third, a matching method of design knowledge based on design intent, and a sorting algorithm of knowledge candidates based on user interest are proposed to realize personalized knowledge active push service. Finally, a prototype system called Personalized Knowledge Push System for Mechanical Conceptual Design (MCD-PKPS) is implemented. An illustrative case demonstrates that the proposed method can successfully improve the efficiency and availability of knowledge acquisition.

scholarly journals Knowledge Graph Question and Answer System for Mechanical Intelligent Manufacturing Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Miaoyuan Shi
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Intelligent Manufacturing ◽  
Focus Of Attention ◽  
Knowledge Graph ◽  
Single Entity ◽  
Question And Answer ◽  
Natural Language Query ◽  
The One ◽  
Answering Questions

With the development of deep learning and its wide application in the field of natural language, the question and answer research of knowledge graph based on deep learning has gradually become the focus of attention. After that, the natural language query is converted into a structured query sentence to identify the entities and attributes in the user’s natural language query and the specified entities and attributes are used to retrieve answers to the knowledge graph. Using the advantage of deep learning in capturing sentence information, it incorporates the attention mechanism to obtain the semantic vector of the relevant attributes in the query and uses the parameter sharing mechanism to insert candidate attributes into the triple in the same model to obtain the semantic vector of typical candidates. The experiment measured that under the 100,000 RDF dataset, the single entity query of the MIQE model does not exceed 3 seconds, and the connection query does not exceed 5 seconds. Under the one-million RDF dataset, the single entity query of the MIQE model does not exceed 8 seconds, and the connection query will not be more than 10 seconds. Experimental data show that the system of knowledge-answering questions of engineering of intelligent construction based on deep learning has good horizontal scalability.

The case for NLP-enhanced database tuning

2021 ◽  
Vol 14 (7) ◽  
pp. 1159-1165
Author(s):  
Immanuel Trummer
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Large Body ◽  
Prototype System ◽  
Web Documents ◽  
Natural Language Text ◽  
Body Of Knowledge ◽  
Challenges And Opportunities ◽  
Automated Tuning ◽  
Database Tuning

A large body of knowledge on database tuning is available in the form of natural language text. We propose to leverage natural language processing (NLP) to make that knowledge accessible to automated tuning tools. We describe multiple avenues to exploit NLP for database tuning, and outline associated challenges and opportunities. As a proof of concept, we describe a simple prototype system that exploits recent NLP advances to mine tuning hints from Web documents. We show that mined tuning hints improve performance of MySQL and Postgres on TPC-H, compared to the default configuration.

Sign in / Sign up

Export Citation Format

Share Document