Artificial Intelligence Knowledge Graph for Dynamic Networks: An Incremental Partition Algorithm

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

Future Internet ◽

10.3390/fi13050124 ◽

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.

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

Sustainability ◽

10.3390/su10093245 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3245 ◽

Cited By ~ 7

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.

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GeoAI for Topographic Mapping Feature Extraction to Knowledge Graph

Abstracts of the ICA ◽

10.5194/ica-abs-2-39-2020 ◽

2020 ◽

Vol 2 ◽

pp. 1-1

Author(s):

E. Lynn Usery

Keyword(s):

Artificial Intelligence ◽

Near Infrared ◽

Model Building ◽

Point Clouds ◽

Knowledge Graph ◽

Topographic Mapping ◽

Topographic Features ◽

Geomorphic Features ◽

Elevation Data ◽

Topographical Maps

Abstract. The U.S Geological Survey is exploring the use of machine learning and geospatial artificial intelligence (GeoAI) for topographic mapping tasks. These automated tasks include extracting topographic features such as hydrography, transportation, vegetation canopy, urban 3D structures, and others from raw data including lidar point clouds, color and near infrared images, historic topographic maps, and Web sources of existing geospatial resources. Current (2020) work includes extracting hydrography from elevation data, and geomorphic features with geographic names from historical topographical maps using Deep Learning. Extracted features are included in a geographic information system (GIS), supporting topographic mapping and modeling activities, and as semantic entities in a graph data model, building a knowledge graph for topographic data. These GIS datasets and topographic knowledge graphs can be used in automated topographic mapping processes and artificial intelligence routines that develop data for hydrologic, biologic, and geologic models that form part of the USGS EarthMap vision.

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The roles of data management and analytics in industry 4.0 ecosystems

10.31219/osf.io/7yvte ◽

2020 ◽

Author(s):

Hendro Wicaksono

Keyword(s):

Artificial Intelligence ◽

Industry 4.0 ◽

Linked Data ◽

Data Analytics ◽

Industrial Revolution ◽

Data Driven ◽

Knowledge Graph ◽

Fourth Industrial Revolution ◽

Global Access ◽

Integration Data

The presentation introduces the technologies associated with the fourth industrial revolution which rely on the concept of artificial intelligence. Data is the basis of functioning artificial intelligence technologies. The presentation also explains how data can revolutionize the business by providing global access to physical products through an industry 4.0 ecosystem. The ecosystem contains four pillars: smart product, smart process, smart resources (smart PPR), and data-driven services. Through these four pillars, the industry 4.0 can be implemented in different sectors. The presentation also provides some insights on the roles of linked data (knowledge graph) for data integration, data analytics, and machine learning in industry 4.0 ecosystem. Project examples in smart city, healthcare, and agriculture sectors are also described. Finally, the presentation discusses the implications of the introduced concepts on the Indonesian context.

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A Framework for Creating Knowledge Graphs of Scientific Software Metadata

Quantitative Science Studies ◽

10.1162/qss_a_00167 ◽

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.

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The Mobilisation of AI in Education: A Bourdieusean Field Analysis

Sociology ◽

10.1177/0038038520967888 ◽

2020 ◽

pp. 003803852096788

Author(s):

Huw C Davies ◽

Rebecca Eynon ◽

Cory Salveson

Keyword(s):

Artificial Intelligence ◽

Educational Technology ◽

Field Analysis ◽

Knowledge Graph ◽

Novel Method

Artificial Intelligence (AI) is currently hailed as a ‘solution’ to perceived problems in education. Though few sociologists of education would agree with its deterministic claims, this AI solutionist thinking is gaining significant currency. In this article, using a relatively novel method for sociology – a knowledge graph – together with Bourdieusean theory, we critically examine how and why different stakeholders in education, educational technology and policy are valorising AI, the main concepts, such as personalisation, they collectively endorse and their incentives for doing so. Drawing on this analysis, we argue that AI is currently being mobilised in education in problematic ways and advocate for more systematic sociological thinking and research to re-orientate the field to account for society’s structural conditions.

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Causal reasoning and symbolic relationships in Medieval Illuminations

Journal of Data Mining & Digital Humanities ◽

10.46298/jdmdh.4459 ◽

2019 ◽

Vol Special Issue on Data Science... ◽

Author(s):

Djibril Diarra ◽

Martine Clouzot ◽

Christophe Nicolle

Keyword(s):

Artificial Intelligence ◽

Social Media ◽

Cultural Values ◽

Middle Ages ◽

Causal Reasoning ◽

Semantic Relations ◽

Knowledge Graph ◽

Future Evolution ◽

Visual Metaphors ◽

Logical Modelling

This work applies knowledge engineering’s techniques to medieval illuminations. Inside it, an illumination is considered as a knowledge graph which was used by some elites in the Middle Ages to represent themselves as a social group and exhibit the events in their lives, and their cultural values. That graph is based on combinations of symbolic elements linked each to others with semantic relations. Those combinations were used to encode visual metaphors and influential messages whose interpretations are sometimes tricky for not experts. Our work aims to describe the meaning of those elements through logical modelling using ontologies. To achieve that, we construct logical reasoning rules and simulate them using artificial intelligence mechanisms. The goal is to facilitate the interpretation of illuminations and provide, in a future evolution of current social media, logical formalisation of new encoding and information transmission services.

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Object-Action Association Extraction from Knowledge Graphs

10.3233/ssw210048 ◽

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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