scholarly journals Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Open Data ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
Knowledge Graph ◽  
Cross Domain ◽  
Disaster Data ◽  
Knowledge Graphs ◽  
Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

scholarly journals Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 241
Author(s):  
Lan Huang ◽  
Yuanwei Zhao ◽  
Bo Wang ◽  
Dongxu Zhang ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Data Integration ◽  
Semantic Similarity ◽  
Domain Knowledge ◽  
Similarity Criteria ◽  
Knowledge Graph ◽  
Legacy Systems ◽  
High Quality ◽  
Cross Domain ◽  
Knowledge Graphs

Knowledge graph-based data integration is a practical methodology for heterogeneous legacy database-integrated service construction. However, it is neither efficient nor economical to build a new cross-domain knowledge graph on top of the schemas of each legacy database for the specific integration application rather than reusing the existing high-quality knowledge graphs. Consequently, a question arises as to whether the existing knowledge graph is compatible with cross-domain queries and with heterogenous schemas of the legacy systems. An effective criterion is urgently needed in order to evaluate such compatibility as it limits the quality upbound of the integration. This research studies the semantic similarity of the schemas from the aspect of properties. It provides a set of in-depth criteria, namely coverage and flexibility, to evaluate the pairwise compatibility between the schemas. It takes advantage of the properties of knowledge graphs to evaluate the overlaps between schemas and defines the weights of entity types in order to perform precise compatibility computation. The effectiveness of the criteria obtained to evaluate the compatibility between knowledge graphs and cross-domain queries is demonstrated using a case study.

scholarly journals A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

2018 ◽  
Vol 10 (9) ◽  
pp. 3245 ◽  
Author(s):  
Tianxing Wu ◽  
Guilin Qi ◽  
Cheng Li ◽  
Meng Wang
Keyword(s):  
Artificial Intelligence ◽  
Question Answering ◽  
Knowledge Representation And Reasoning ◽  
Knowledge Graph ◽  
Development History ◽  
One Belt One Road ◽  
History Of ◽  
Knowledge Graphs ◽  
The One ◽  
The Impact

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.

scholarly journals Virtual Knowledge Graphs: An Overview of Systems and Use Cases

2019 ◽  
Vol 1 (3) ◽  
pp. 201-223 ◽  
Author(s):  
Guohui Xiao ◽  
Linfang Ding ◽  
Benjamin Cogrel ◽  
Diego Calvanese
Keyword(s):  
Data Integration ◽  
Domain Knowledge ◽  
Data Access ◽  
Use Cases ◽  
Future Research ◽  
Knowledge Graph ◽  
Research Directions ◽  
Wide Range ◽  
Future Research Directions ◽  
Knowledge Graphs

In this paper, we present the virtual knowledge graph (VKG) paradigm for data integration and access, also known in the literature as Ontology-based Data Access. Instead of structuring the integration layer as a collection of relational tables, the VKG paradigm replaces the rigid structure of tables with the flexibility of graphs that are kept virtual and embed domain knowledge. We explain the main notions of this paradigm, its tooling ecosystem and significant use cases in a wide range of applications. Finally, we discuss future research directions.

scholarly journals Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search

2020 ◽  
Vol 39 (3) ◽  
pp. 595-602
Author(s):  
Aatif Ahmad Khan ◽  
Sanjay Kumar Malik
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Open Data ◽  
Knowledge Bases ◽  
Semantic Search ◽  
Semantic Web Technologies ◽  
Data Format ◽  
Web Technologies ◽  
Cross Domain ◽  
Description Framework

Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.

scholarly journals OpenKG Chain: A Blockchain Infrastructure for Open Knowledge Graphs

2021 ◽  
pp. 1-18
Author(s):  
Huajun Chen ◽  
Ning Hu ◽  
Guilin Qi ◽  
Haofen Wang ◽  
Zhen Bi ◽  
...  
Keyword(s):  
Open Data ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
Cumulative Number ◽  
Trust Network ◽  
Fine Grained ◽  
Knowledge Based ◽  
Granularity Level ◽  
Knowledge Graphs ◽  
Structured Graphs

Abstract The early concept of knowledge graph originates from the idea of the Semantic Web, which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things. Currently publishing knowledge bases as open data on the Web has gained significant attention. In China, CIPS(Chinese Information Processing Society) launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs. Unlike existing open knowledge-based programs, OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure. This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain, a blockchain-based trust network. We have completed the test of the underlying blockchain platform, as well as the on-chain test of OpenKG's dataset and toolset sharing as well as fine-grained knowledge crowdsourcing at the triple level. We have also proposed novel definitions: K-Point and OpenKG Token, which can be considered as a measurement of knowledge value and user value. 1033 knowledge contributors have been involved in two months of testing on the blockchain, and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000. For the first time, We have tested and realized on-chain sharing of knowledge at entity/triple granularity level. At present, all operations on the datasets and toolset in OpenKG.CN, as well as the triplets in OpenBase, are recorded on the chain, and corresponding value will also be generated and assigned in a trusted mode. Via this effort, OpenKG chain looks to provide a more credible and traceable knowledge-sharing platform for the knowledge graph community.

scholarly journals A Framework for Creating Knowledge Graphs of Scientific Software Metadata

2021 ◽  
pp. 1-37
Author(s):  
Aidan Kelley ◽  
Daniel Garijo
Keyword(s):  
Artificial Intelligence ◽  
Computational Methods ◽  
Web Sites ◽  
Knowledge Graph ◽  
Scientific Software ◽  
Scientific Publications ◽  
Knowledge Graphs ◽  
Set Up ◽  
Number Of Publications

Abstract An increasing number of researchers rely on computational methods to generate or manipulate the results described in their scientific publications. Software created to this end—scientific software—is key to understanding, reproducing, and reusing existing work in many disciplines, ranging from Geosciences to Astronomy or Artificial Intelligence. However, scientific software is usually challenging to find, set up, and compare to similar software due to its disconnected documentation (dispersed in manuals, readme files, web sites, and code comments) and the lack of structured metadata to describe it. As a result, researchers have to manually inspect existing tools in order to understand their differences and incorporate them into their work. This approach scales poorly with the number of publications and tools made available every year. In this paper we address these issues by introducing a framework for automatically extracting scientific software metadata from its documentation (in particular, their readme files); a methodology for structuring the extracted metadata in a Knowledge Graph (KG) of scientific software; and an exploitation framework for browsing and comparing the contents of the generated KG. We demonstrate our approach by creating a KG with metadata from over ten thousand scientific software entries from public code repositories.

scholarly journals Discovering alignment relations with Graph Convolutional Networks: A biomedical case study

Semantic Web ◽  
2021 ◽  
pp. 1-20
Author(s):  
Pierre Monnin ◽  
Chedy Raïssi ◽  
Amedeo Napoli ◽  
Adrien Coulet
Keyword(s):  
Domain Knowledge ◽  
Knowledge Graph ◽  
Convolutional Networks ◽  
Real World Application ◽  
Web Of Data ◽  
Knowledge Graphs ◽  
The Web ◽  
Node Embeddings ◽  
Weak Similarity

Knowledge graphs are freely aggregated, published, and edited in the Web of data, and thus may overlap. Hence, a key task resides in aligning (or matching) their content. This task encompasses the identification, within an aggregated knowledge graph, of nodes that are equivalent, more specific, or weakly related. In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster. We conducted experiments with this approach on the real world application of aligning knowledge in the field of pharmacogenomics, which motivated our study. We particularly investigated the interplay between domain knowledge and GCN models with the two following focuses. First, we applied inference rules associated with domain knowledge, independently or combined, before learning node embeddings, and we measured the improvements in matching results. Second, while our GCN model is agnostic to the exact alignment relations (e.g., equivalence, weak similarity), we observed that distances in the embedding space are coherent with the “strength” of these different relations (e.g., smaller distances for equivalences), letting us considering clustering and distances in the embedding space as a means to suggest alignment relations in our case study.

scholarly journals Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction

2021 ◽  
Vol 13 (16) ◽  
pp. 3209
Author(s):  
Steven Dewitte ◽  
Jan P. Cornelis ◽  
Richard Müller ◽  
Adrian Munteanu
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Weather Forecast ◽  
Heterogeneous Data ◽  
Development Effort ◽  
Decadal Prediction ◽  
Analytical Treatment ◽  
Climate Monitoring ◽  
Science Domain ◽  
New Generation

Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Prediction (DP). The use of AI techniques can lead simultaneously to: (1) a reduction of human development effort, (2) a more efficient use of computing resources and (3) an increased forecast quality. To realise this potential, a new generation of scientists combining atmospheric science domain knowledge and state-of-the-art AI skills needs to be trained. AI should become a cornerstone of future weather and climate observation and modelling systems.

scholarly journals Object-Action Association Extraction from Knowledge Graphs

2021 ◽  
Author(s):  
Alexandros Vassiliades ◽  
Theodore Patkos ◽  
Vasilis Efthymiou ◽  
Antonis Bikakis ◽  
Nick Bassiliades ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Semantic Similarity ◽  
Semantic Information ◽  
Research Training ◽  
Relevant Literature ◽  
Physical World ◽  
Knowledge Graph ◽  
Training Systems ◽  
Novel Method ◽  
Knowledge Graphs

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is of utmost importance and a long-lasting goal in Artificial Intelligence (AI) research. Training systems with relevant data is a common approach; yet, it is not always feasible to find the data needed, especially since a big portion of this knowledge is commonsense. In this paper, we propose a novel method for extracting and evaluating relations between objects and actions from knowledge graphs, such as ConceptNet and WordNet. We present a complete methodology of locating, enriching, evaluating, cleaning and exposing knowledge from such resources, taking into consideration semantic similarity methods. One important aspect of our method is the flexibility in deciding how to deal with the noise that exists in the data. We compare our method with typical approaches found in the relevant literature, such as methods that exploit the topology or the semantic information in a knowledge graph, and embeddings. We test the performance of these methods on the Something-Something Dataset.

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