scholarly journals VisualSem: a high-quality knowledge graph for vision and language

2021 ◽  
Author(s):  
Houda Alberts ◽  
Ningyuan Huang ◽  
Yash Deshpande ◽  
Yibo Liu ◽  
Kyunghyun Cho ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
High Quality ◽  
Vision And Language

scholarly journals Analysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade

2021 ◽  
Author(s):  
Arthur Lackner ◽  
Said Fathalla ◽  
Mojtaba Nayyeri ◽  
Andreas Behrend ◽  
Rainer Manthey ◽  
...  
Keyword(s):  
Computer Science ◽  
Analytical Study ◽  
Subjective Evaluation ◽  
Knowledge Exchange ◽  
Scholarly Communication ◽  
Knowledge Graph ◽  
Crowd Sourcing ◽  
High Quality ◽  
Publish Or Perish

AbstractThe publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics.

High-Quality Knowledge Graphs Generation: R2RML and RML Comparison, Rules Validation and Inconsistency Resolution

2020 ◽  
Author(s):  
Anastasia Dimou
Keyword(s):  
State Of The Art ◽  
The State ◽  
Knowledge Graph ◽  
High Quality ◽  
Inconsistency Resolution ◽  
Knowledge Graphs ◽  
Rdf Graphs ◽  
Graph Generation

In this chapter, an overview of the state of the art on knowledge graph generation is provided, with focus on the two prevalent mapping languages: the W3C recommended R2RML and its generalisation RML. We look into details on their differences and explain how knowledge graphs, in the form of RDF graphs, can be generated with each one of the two mapping languages. Then we assess if the vocabulary terms were properly applied to the data and no violations occurred on their use, either using R2RML or RML to generate the desired knowledge graph.

scholarly journals A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs

2022 ◽  
Vol 12 (2) ◽  
pp. 715
Author(s):  
Luodi Xie ◽  
Huimin Huang ◽  
Qing Du
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Space ◽  
Knowledge Graph ◽  
High Quality ◽  
Gaussian Distributions ◽  
Benchmark Datasets ◽  
Semantic Spaces ◽  
Knowledge Graphs ◽  
Low Dimensional

Knowledge graph (KG) embedding has been widely studied to obtain low-dimensional representations for entities and relations. It serves as the basis for downstream tasks, such as KG completion and relation extraction. Traditional KG embedding techniques usually represent entities/relations as vectors or tensors, mapping them in different semantic spaces and ignoring the uncertainties. The affinities between entities and relations are ambiguous when they are not embedded in the same latent spaces. In this paper, we incorporate a co-embedding model for KG embedding, which learns low-dimensional representations of both entities and relations in the same semantic space. To address the issue of neglecting uncertainty for KG components, we propose a variational auto-encoder that represents KG components as Gaussian distributions. In addition, compared with previous methods, our method has the advantages of high quality and interpretability. Our experimental results on several benchmark datasets demonstrate our model’s superiority over the state-of-the-art baselines.

scholarly journals End-to-End Argumentation Knowledge Graph Construction

2020 ◽  
Vol 34 (05) ◽  
pp. 7367-7374
Author(s):  
Khalid Al-Khatib ◽  
Yufang Hou ◽  
Henning Wachsmuth ◽  
Charles Jochim ◽  
Francesca Bonin ◽  
...  
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Knowledge Graph ◽  
Exploratory Search ◽  
Text Generation ◽  
Fake News ◽  
High Quality ◽  
Web Based ◽  
Knowledge Graphs ◽  
End To End

This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.

scholarly journals Research on Cultivating Students' Innovative Ability in College Physical Education based on Knowledge Graph

2021 ◽  
Vol 14 ◽  
pp. 7-11
Author(s):  
Ping Luo
Keyword(s):  
Big Data ◽  
Physical Education ◽  
Teaching Methods ◽  
Modern Society ◽  
Knowledge Graph ◽  
The Internet ◽  
Learning Method ◽  
High Quality ◽  
New Learning ◽  
Innovation Ability

As a new learning method rooted in the Internet and big data, knowledge graph can exercise people's sensitivity and activity of thinking, and plays an important role in cultivating students' innovation ability. However, at present, there are few studies on the combination of knowledge graph and innovation ability. Therefore, this study chooses to combine knowledge graph with college physical education (P.E.) students' innovation ability to carry out research in the field of education. In college P.E., by analyzing the factors that restrict the cultivation of students' innovative ability, this paper puts forward some methods to cultivate students' innovative ability, such as changing educational concepts, innovating teaching contents and teaching methods, adjusting evaluation methods, and innovating teaching modes, to make students become high-quality talents with all-round physical and mental development and the needs of modern society. Therefore, it is necessary to continuously deepen the educational reform, so that students can continuously improve their interest in P.E. and their innovative ability can be stimulated to a greater extent.

scholarly journals Neural Entity Summarization with Joint Encoding and Weak Supervision

2020 ◽  
Author(s):  
Junyou Li ◽  
Gong Cheng ◽  
Qingxia Liu ◽  
Wen Zhang ◽  
Evgeny Kharlamov ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Neural Model ◽  
Knowledge Graph ◽  
Graph Structure ◽  
High Quality ◽  
Manual Work ◽  
Weak Supervision ◽  
Entity Summarization ◽  
Joint Encoding

In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions, called entity summaries. Existing solutions to entity summarization are mainly unsupervised. In this paper, we present a supervised approach NEST that is based on our novel neural model to jointly encode graph structure and text in KGs and generate high-quality diversified summaries. Since it is costly to obtain manually labeled summaries for training, our supervision is weak as we train with programmatically labeled data which may contain noise but is free of manual work. Evaluation results show that our approach significantly outperforms the state of the art on two public benchmarks.

scholarly journals Explainable recommendation based on knowledge graph and multi-objective optimization

2021 ◽  
Author(s):  
Lijie Xie ◽  
Zhaoming Hu ◽  
Xingjuan Cai ◽  
Wensheng Zhang ◽  
Jinjun Chen
Keyword(s):  
Optimization Algorithm ◽  
Recommendation System ◽  
High Accuracy ◽  
Knowledge Graph ◽  
Multi Objective Optimization ◽  
High Quality ◽  
Candidate List ◽  
Multi Objective ◽  
Target User ◽  
Explainable Recommendation

AbstractRecommendation system is a technology that can mine user's preference for items. Explainable recommendation is to produce recommendations for target users and give reasons at the same time to reveal reasons for recommendations. The explainability of recommendations that can improve the transparency of recommendations and the probability of users choosing the recommended items. The merits about explainability of recommendations are obvious, but it is not enough to focus solely on explainability of recommendations in field of explainable recommendations. Therefore, it is essential to construct an explainable recommendation framework to improve the explainability of recommended items while maintaining accuracy and diversity. An explainable recommendation framework based on knowledge graph and multi-objective optimization is proposed that can optimize the precision, diversity and explainability about recommendations at the same time. Knowledge graph connects users and items through different relationships to obtain an explainable candidate list for target user, and the path between target user and recommended item is used as an explanation basis. The explainable candidate list is optimized through multi-objective optimization algorithm to obtain the final recommendation list. It is concluded from the results about experiments that presented explainable recommendation framework provides high-quality recommendations that contains high accuracy, diversity and explainability.

scholarly journals Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 160
Author(s):  
John P. McCrae ◽  
Pranab Mohanty ◽  
Siddharth Narayanan ◽  
Bianca Pereira ◽  
Paul Buitelaar ◽  
...  
Keyword(s):  
Language Processing ◽  
Financial Services ◽  
State Of The Art ◽  
Knowledge Graph ◽  
Financial Industry ◽  
High Quality ◽  
Processing Technologies ◽  
Knowledge Graphs ◽  
Graph System ◽  
New Applications

Knowledge graphs are proving to be an increasingly important part of modern enterprises, and new applications of such enterprise knowledge graphs are still being found. In this paper, we report on the experience with the use of an automatic knowledge graph system called Saffron in the context of a large financial enterprise and show how this has found applications within this enterprise as part of the “Conversation Concepts Artificial Intelligence” tool. In particular, we analyse the use cases for knowledge graphs within this enterprise, and this led us to a new extension to the knowledge graph system. We present the results of these adaptations, including the introduction of a semi-supervised taxonomy extraction system, which includes analysts in-the-loop. Further, we extend the kinds of relations extracted by the system and show how the use of the BERTand ELMomodels can produce high-quality results. Thus, we show how this tool can help realize a smart enterprise and how requirements in the financial industry can be realised by state-of-the-art natural language processing technologies.

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