Integrating Heterogeneous Data About Quebec Literature into an IFLA LRM Knowledge Base

2021 ◽  
pp. 373-391
Author(s):  
Ludovic Font ◽  
Dominique Piché ◽  
Amal Zouaq ◽  
Michel Gagnon
Keyword(s):  
Knowledge Base ◽  
Heterogeneous Data ◽  
Quebec Literature

scholarly journals Diffusion enables integration of heterogeneous data and user-driven learning in a desktop knowledge-base

2021 ◽  
Vol 17 (8) ◽  
pp. e1009283
Author(s):  
Tomasz Konopka ◽  
Sandra Ng ◽  
Damian Smedley
Keyword(s):  
Knowledge Base ◽  
Nearest Neighbor ◽  
Heterogeneous Data ◽  
Data Types ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Heterogeneous Datasets ◽  
Domain Specific Knowledge ◽  
High Throughput Experiments ◽  
Gene Symbols

Integrating reference datasets (e.g. from high-throughput experiments) with unstructured and manually-assembled information (e.g. notes or comments from individual researchers) has the potential to tailor bioinformatic analyses to specific needs and to lead to new insights. However, developing bespoke analysis pipelines from scratch is time-consuming, and general tools for exploring such heterogeneous data are not available. We argue that by treating all data as text, a knowledge-base can accommodate a range of bioinformatic data types and applications. We show that a database coupled to nearest-neighbor algorithms can address common tasks such as gene-set analysis as well as specific tasks such as ontology translation. We further show that a mathematical transformation motivated by diffusion can be effective for exploration across heterogeneous datasets. Diffusion enables the knowledge-base to begin with a sparse query, impute more features, and find matches that would otherwise remain hidden. This can be used, for example, to map multi-modal queries consisting of gene symbols and phenotypes to descriptions of diseases. Diffusion also enables user-driven learning: when the knowledge-base cannot provide satisfactory search results in the first instance, users can improve the results in real-time by adding domain-specific knowledge. User-driven learning has implications for data management, integration, and curation.

Gastrointestinal Motility Online Educational Endeavor

2010 ◽  
pp. 163-182
Author(s):  
Shiu-chung Au ◽  
Amar Gupta
Keyword(s):  
Knowledge Base ◽  
Gastrointestinal Motility ◽  
Medical Information ◽  
Current Knowledge ◽  
Heterogeneous Data ◽  
High Quality ◽  
Online Journal ◽  
Related Information ◽  
Scholarly Publications ◽  
Current Knowledge Base

Medical information has been traditionally maintained in books, journals, and specialty periodicals. Now, a growing number of people, including patients and caregivers, turn to a variety of sources on the Internet, most of which are run by commercial entities, to retrieve healthcare-related information. The next area of growth will be sites that focus on specific fields of medicine, featuring high quality data culled from scholarly publications, operated by eminent domain specialists. One such site is being developed for the field of Gastrointestinal Motility; it further augments the innovations of existing healthcare information sites with the intention of serving the diverse needs of lay people, medical students, and experts in the field. The site, called Gastrointestinal Motility Online, leverages the strengths of online textbooks, which have a high degree of organization, in conjunction with the strengths of online journal collections, which are more comprehensive and focused, to produce a knowledge base that can be easily updated, but still provides authoritative and high quality information to users. In addition to implementing existing Web technologies such as Wiki- and Amazon-style commenting options, Gastrointestinal Motility Online uses automatic methods to assemble information from various heterogeneous data sources to create a coherent, cogent, and current knowledge base serving a diverse base of users.

DeepDepict

2021 ◽  
Vol 15 (5) ◽  
pp. 1-16
Author(s):  
Shaoyang Hao ◽  
Bin Guo ◽  
Hao Wang ◽  
Yunji Liang ◽  
Lina Yao ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Heterogeneous Data ◽  
Experimental Results ◽  
Labor Costs ◽  
Time Consumption ◽  
Descriptive Information ◽  
Generic Framework ◽  
Comprehensive Knowledge ◽  
Public Dataset ◽  
Purchase Behaviors

In e-commerce platforms, the online descriptive information of products shows significant impacts on the purchase behaviors. To attract potential buyers for product promotion, numerous workers are employed to write the impressive product descriptions. The hand-crafted product descriptions are less-efficient with great labor costs and huge time consumption. Meanwhile, the generated product descriptions do not take consideration into the customization and the diversity to meet users’ interests. To address these problems, we propose one generic framework, namely DeepDepict, to automatically generate the information-rich and personalized product descriptive information. Specifically, DeepDepict leverages the graph attention to retrieve the product-related knowledge from external knowledge base to enrich the diversity of products, constructs the personalized lexicon to capture the linguistic traits of individuals for the personalization of product descriptions, and utilizes multiple pointer-generator network to fuse heterogeneous data from multi-sources to generate informative and personalized product descriptions. We conduct intensive experiments on one public dataset. The experimental results show that DeepDepict outperforms existing solutions in terms of description diversity, BLEU, and personalized degree with significant margin gain, and is able to generate product descriptions with comprehensive knowledge and personalized linguistic traits.

Continual Word Embedding Based for Matching Lightweight Ontologies

2014 ◽  
Vol 556-562 ◽  
pp. 6281-6285
Author(s):  
Zhen Le Wu ◽  
Ying Li ◽  
Yong Bin Wang ◽  
Yan Jiao Zang
Keyword(s):  
Knowledge Base ◽  
Heterogeneous Data ◽  
Word Embedding ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Ontology Alignment Evaluation Initiative

Ontology matching is the task of finding alignments between two different ontologies. It has become the key point of building knowledge base and integrating heterogeneous data. In this paper, a novel ontology matching approach that is based on continual word embedding is proposed. We describe in details how is skip-gram model adapted to capture the semantic of words to learn the word embedding. After computing the name similarity of concepts, similarity flooding algorithm is used to fix the initial similarity. Experiments on Ontology Alignment Evaluation Initiative (OAEI) benchmark without instances show that the proposed method significantly improves the quality of mappings.

Ontology-Based Unified Representation of Dynamic Simulation Models in Engineering Design

2018 ◽  
Author(s):  
Jun Yu ◽  
Zhenjun Ming ◽  
Guoxin Wang ◽  
Yan Yan ◽  
Xiaoping Lan
Keyword(s):  
Simulation Model ◽  
Knowledge Base ◽  
Engineering Design ◽  
Dynamic Simulation ◽  
Collaborative Design ◽  
Simulation Models ◽  
Heterogeneous Data ◽  
Automation Systems ◽  
Dynamic Simulation Model ◽  
Definition Of

The development of complex product dynamic simulation models and the integration of design automation systems require knowledge from multiple heterogeneous data sources and tools. Because of the heterogeneity of model data, the integration of tools and data is a time-consuming and error-prone task. The main objective of this study is to provide a unified model of dynamic simulation for engineering design, which serves as a knowledge base to support the development of a dynamic simulation model. The integration of knowledge is realized through (i) definition of the structure and interface during the design phase of the dynamic simulation model, and (ii) definition of a model-driven integrated environment configuration process during the runtime phase. In order to achieve interoperability among the different simulation models in a collaborative design environment, we build a “Demand-Resources-Service-Knowledge-Process (DKRSP)” ontology that formally represents the semantics of dynamic simulation models. Based on the ontology, a knowledge base is created for the management of dynamic simulation knowledge. The efficacy of the ontology and the knowledge base are demonstrated using a transmission design example.

scholarly journals RADAR: Resilient Application for Dependable Aided Reporting

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 463
Author(s):  
Antonia Azzini ◽  
Nicola Cortesi ◽  
Giuseppe Psaila
Keyword(s):  
Knowledge Base ◽  
Data Model ◽  
Common Knowledge ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Application Domain ◽  
Report Generation ◽  
Domain Experts ◽  
Continuous Generation ◽  
Data Definition

Many organizations must produce many reports for various reasons. Although this activity could appear simple to carry out, this fact is not at all true: indeed, generating reports requires the collection of possibly large and heterogeneous data sets. Furthermore, different professional figures are involved in the process, possibly with different skills (database technicians, domain experts, employees): the lack of common knowledge and of a unifying framework significantly obstructs the effective and efficient definition and continuous generation of reports. This paper presents a novel framework named RADAR, which is the acronym for “Resilient Application for Dependable Aided Reporting”: the framework has been devised to be a ”bridge” between data and employees in charge of generating reports. Specifically, it builds a common knowledge base in which database administrators and domain experts describe their knowledge about the application domain and the gathered data; this knowledge can be browsed by employees to find out the relevant data to aggregate and insert into reports, while designing report layouts; the framework assists the overall process from data definition to report generation. The paper presents the application scenario and the vision by means of a running example, defines the data model and presents the architecture of the framework.

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