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 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 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 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.

scholarly journals The Data Set Knowledge Graph: Creating a Linked Open Data Source for Data Sets

2021 ◽  
pp. 1-30
Author(s):  
Michael Färber ◽  
David Lamprecht
Keyword(s):  
Open Data ◽  
Knowledge Graph ◽  
Data Sets ◽  
Name Disambiguation ◽  
Scientific Publications ◽  
Data Set ◽  
High Data ◽  
Academic Knowledge ◽  
Scientific Disciplines ◽  
Knowledge Graphs

Abstract Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets.

scholarly journals Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning

2019 ◽  
Vol 9 (13) ◽  
pp. 2720 ◽  
Author(s):  
Mingxiong Zhao ◽  
Han Wang ◽  
Jin Guo ◽  
Di Liu ◽  
Cheng Xie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Language Processing ◽  
Intelligent Control ◽  
Chinese Text ◽  
Industrial Revolution ◽  
Knowledge Graph ◽  
Text Learning ◽  
Knowledge Graphs ◽  
Traditional Manufacturing

The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field.

scholarly journals Dual Quaternion Embeddings for Link Prediction

2021 ◽  
Vol 11 (12) ◽  
pp. 5572
Author(s):  
Liming Gao ◽  
Huiling Zhu ◽  
Hankz Hankui Zhuo ◽  
Jin Xu 
Keyword(s):  
Artificial Intelligence ◽  
Link Prediction ◽  
State Of The Art ◽  
Knowledge Graph ◽  
Dual Quaternion ◽  
Novel Approach ◽  
Quaternion Space ◽  
Knowledge Graphs ◽  
Low Dimensional

The applications of knowledge graph have received much attention in the field of artificial intelligence. The quality of knowledge graphs is, however, often influenced by missing facts. To predict the missing facts, various solid transformation based models have been proposed by mapping knowledge graphs into low dimensional spaces. However, most of the existing transformation based approaches ignore that there are multiple relations between two entities, which is common in the real world. In order to address this challenge, we propose a novel approach called DualQuatE that maps entities and relations into a dual quaternion space. Specifically, entities are represented by pure quaternions and relations are modeled based on the combination of rotation and translation from head to tail entities. After that we utilize interactions of different translations and rotations to distinguish various relations between head and tail entities. Experimental results exhibit that the performance of DualQuatE is competitive compared to the existing state-of-the-art models.

TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion

2021 ◽  
Vol 15 (3) ◽  
pp. 1-17
Author(s):  
Luyi Bai ◽  
Xiangnan Ma ◽  
Mingcheng Zhang ◽  
Wenting Yu
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Knowledge Graph ◽  
Modeling Process ◽  
Knowledge Graphs

Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the Goodness of relations and the Closeness of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao
Keyword(s):  
Data Storage ◽  
Program Analysis ◽  
Web Application ◽  
Security Analysis ◽  
Knowledge Graph ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Knowledge Graphs ◽  
Mobile Malware Detection ◽  
Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate.  Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention.  This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP).  OWASP maintains lists of the top ten security threats to web and mobile applications.  We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code.  We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.”  We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

ANALGESIC AND ANTI-INFLAMMATORY EFFECTS OF ACETYLSALICYLIC ACID: PHYSIOLOGICAL MECHANISMS

2020 ◽  
Vol 6(72) (1) ◽  
pp. 197-219
Author(s):  
I. V. Cheretaev ◽  
D. R. Khusainov ◽  
E. N. Chuyan ◽  
M. Yu. Ravaeva ◽  
A. N. Gusev ◽  
...  
Keyword(s):  
Acetylsalicylic Acid ◽  
Physiological Mechanism ◽  
The Body ◽  
Physiological Mechanisms ◽  
Scientific Publications ◽  
Thermal Pain ◽  
Order Of Magnitude ◽  
Anti Inflammatory ◽  
Number Of Publications ◽  
Electrical Stimuli

The purpose of the review is to summarize current literature data and the results of our own research on the analgesic and anti-inflammatory effects of acetylsalicylic acid, as well as the physiological mechanisms underlying them. This acid is the most studied reference representative of salicylates, which is convenient to consider the physiological effects characteristic in general for this group of chemical and medicinal products. Acetylsalicylic acid has analgesic properties against thermal pain and pain caused by electrical stimuli, as well as a pronounced anti-inflammatory effect. The realization of these properties depends on the peculiarities of aspirin metabolism in the body, ion and synaptic mechanisms for controlling the functional state of the cell, neurotransmitter systems of the сentral nervous system, and mechanisms of peripheral and сentral analgesia. Analgesic properties of acetylsalicylic acid founded not only in normal, but also in ultra-small doses. Various physical and especially chemical factors significantly change their effects. This increases the interest in studying the analgesic activity of salicylates and their physiological mechanisms, since such studies can serve as a basis for creating new non-steroidal anti-inflammatory drugs with low toxicity and high safety for patients, and improve the strategy of their practical use. Currently, the most detailed study of the physiological mechanism of analgesic and anti-inflammatory action of aspirin and its main metabolite – salicylic acid. However, it should be note that despite the abundance of existing data obtained in scientific studies of the effects of aspirin and its practical use, there are a number of unexplained aspects of the action of this drug, the mechanism of which has not yet been deciphered. The continuing interest in the effects and mechanisms of action of this drug and in connection with the expansion of its use evidenced by a consistently high number of scientific publications on aspirin in the most famous foreign and domestic publications. At the same time, the number of publications about aspirin is an order of magnitude higher than about any other drug known to humanity.

scholarly journals Global research activity on antimicrobial resistance in food-producing animals

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Waleed M. Sweileh
Keyword(s):  
Antimicrobial Resistance ◽  
Hot Spots ◽  
One Health ◽  
Research Collaboration ◽  
Search Query ◽  
Scientific Publications ◽  
Research Theme ◽  
The Usa ◽  
Number Of Publications ◽  
Number Of Citations

Abstract Background Antimicrobial resistance (AMR) is a global challenge that requires a “One Health” approach to achieve better public health outcomes for people, animals, and the environment. Numerous bibliometric studies were published on AMR in humans. However, none was published in food-producing animals. The current study aimed at assessing and analyzing scientific publications on AMR in food-producing animals. Method A validated search query was developed and entered in Scopus advanced search function to retrieve and quantitatively analyze relevant documents. Bibliometric indicators and mapping were presented. The study period was from 2000 to 2019. Results The search query retrieved 2852 documents. During the period from 2015 to 2019, approximately 48% of the retrieved documents were published. The article about the discovery of plasmid-mediated colistin resistance in pigs received the highest number of citations (n = 1970). The Journal of Food Protection (n = 123; 4.3%) ranked first in the number of publications while the Applied and Environmental Microbiology journal ranked first in the number of citations per document. The USA led with 576 (20.2%) documents followed by China (n = 375; 13.1%). When the number of publications was standardized by income and population size, India (n = 51.5) ranked first followed by China (n = 38.3) and Brazil (n = 13.4). The growth of publications from China exceeded that of the USA in the last 3 years of the study period. Research collaboration in this field was inadequate. Mapping author keywords showed that E. coli, Salmonella, poultry, Campylobacter, chicken, cattle, and resistant genes were most frequent. The retrieved documents existed in five research themes. The largest research theme was about AMR in Salmonella in food-producing animals. The most recent research theme was about the dissemination and molecular transfer of AMR genes into the environment and among different bacterial strains. Conclusion Hot spots of research on AMR in food-producing animals match the world regions of reported hot spots of AMR in animals. Research collaboration in this field is of great importance, especially with low- and middle-income countries. Data on AMR need to be collected nationally and internationally to implement the “One Health” approach in the fight against AMR.

Sign in / Sign up

Export Citation Format

Share Document