SALKG: A Semantic Annotation System for Building a High-quality Legal Knowledge Graph

Author(s):  
Mingwei Tang ◽  
Cui Su ◽  
Haihua Chen ◽  
Jingye Qu ◽  
Junhua Ding
2021 ◽  
Author(s):  
Arthur Lackner ◽  
Said Fathalla ◽  
Mojtaba Nayyeri ◽  
Andreas Behrend ◽  
Rainer Manthey ◽  
...  

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.


Author(s):  
Anastasia Dimou

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.


2013 ◽  
Vol 8 (3) ◽  
Author(s):  
Ruojuan Xue ◽  
Wenpeng Lu ◽  
Jinyong Cheng

Author(s):  
Francesco Sovrano ◽  
Monica Palmirani ◽  
Fabio Vitali

This paper presents the Open Knowledge Extraction (OKE) tools combined with natural language analysis of the sentence in order to enrich the semantic of the legal knowledge extracted from legal text. In particular the use case is on international private law with specific regard to the Rome I Regulation EC 593/2008, Rome II Regulation EC 864/2007, and Brussels I bis Regulation EU 1215/2012. A Knowledge Graph (KG) is built using OKE and Natural Language Processing (NLP) methods jointly with the main ontology design patterns defined for the legal domain (e.g., event, time, role, agent, right, obligations, jurisdiction). Using critical questions, underlined by legal experts in the domain, we have built a question answering tool capable to support the information retrieval and to answer to these queries. The system should help the legal expert to retrieve the relevant legal information connected with topics, concepts, entities, normative references in order to integrate his/her searching activities.


2022 ◽  
Vol 12 (2) ◽  
pp. 715
Author(s):  
Luodi Xie ◽  
Huimin Huang ◽  
Qing Du

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.


2021 ◽  
pp. 27-39
Author(s):  
Luoqiu Li ◽  
Zhen Bi ◽  
Hongbin Ye ◽  
Shumin Deng ◽  
Hui Chen ◽  
...  

2014 ◽  
Vol 4 (1) ◽  
pp. 69
Author(s):  
Chekry Abderrahman ◽  
Oriche Aziz ◽  
Khaldi Mohamed

This paper presents a system based on intelligent agents for the semantic annotation of learning resources taking into account the context of training. Semantic annotations systems rarely treat existing semantic annotations in the field of distance education (e-learning), most researchers in the field of education limits annotations to specific cases (teacher annotation, learner annotation, annotation of electronic documents etc.) these annotations are edited by users with an annotation tools, by cons in our approach, we propose a semantic annotation system based on intelligent agents that manage semantic annotations of educational resources, these annotations are guided by domain ontologies and ontology applications. We believe that the original resource annotations, a storehouse of learning objects standardized by LOM profile, these learning objects are managed using an ontology learning.


2020 ◽  
Vol 34 (05) ◽  
pp. 7367-7374
Author(s):  
Khalid Al-Khatib ◽  
Yufang Hou ◽  
Henning Wachsmuth ◽  
Charles Jochim ◽  
Francesca Bonin ◽  
...  

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.


2020 ◽  
Vol 37 (1) ◽  
pp. 175-178
Author(s):  
Víctor Rodríguez Doncel ◽  
Elena Montiel Ponsoda

Lynx is an innovation project in Europe whose objective is to develop services for legal compliance. A legal knowledge graph is built over multilingual, multijurisdictional documents using semantic web technologies. A collection of services implementing natural language techniques enables better legal information retrieval, cross-lingual answering of questions and information discovery. Three use cases are discussed, as well as the overall impact of the project.  


2021 ◽  
Author(s):  
Houda Alberts ◽  
Ningyuan Huang ◽  
Yash Deshpande ◽  
Yibo Liu ◽  
Kyunghyun Cho ◽  
...  

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