Weakly-Supervised Relation Extraction in Legal Knowledge Bases

There are many conceptions of e-governance (Malkia, Anttiroiko, & Savolainen, 2004; Reinermann & Lucke, 2002). Our view is that e-governance is about the use of information and communications technology to improve the quality and efficiency of all phases of the life cycle of legislation. In this conception, computer models of legislation play a central role. We use the term “model” in a broad way, to cover every kind of data model of legislation or metadata about legislation, at various levels of abstraction or detail, including full text, hypertext, diagrams and other visualization methods, and legal knowledge-bases using Artificial Intelligence knowledge representation techniques. The appropriate kind of model depends on the particular task to be supported. In this article, the focus will be on the use of legal knowledge systems (LKS) to support the implementation phase of the life cycle of legislation. Legal Knowledge Systems are also known as legal knowledge-based systems (LKBS). LKS can greatly improve the correctness, consistency, transparency and, last but not least, the efficiency of the administration of complex legislation. The rest of this article is organized as follows. The next section explains the relevance of legal knowledge systems for governance. This is followed by a section motivating the use of LKS to support tasks in the implementation phase of the life cycle of legislation and providing a brief introduction to LKS technology. Next, various application scenarios for implementing public policy and legislation using LKS are discussed. Although research on technology for legal knowledge systems continues, it is a mature technology with many impressive applications in regular use by public administration. The article concludes by reiterating its main points and identifying open research issues.

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A Semi-automated Entity Relation Extraction Mechanism with Weakly Supervised Learning for Chinese Medical Webpages

Smart Health - Lecture Notes in Computer Science ◽

10.1007/978-3-319-59858-1_5 ◽

2017 ◽

pp. 44-56 ◽

Cited By ~ 1

Author(s):

Zhao Liu ◽

Jian Tong ◽

Jinguang Gu ◽

Kai Liu ◽

Bo Hu

Keyword(s):

Supervised Learning ◽

Relation Extraction ◽

Weakly Supervised Learning ◽

Extraction Mechanism ◽

Entity Relation Extraction ◽

Weakly Supervised

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Causal Knowledge Extraction through Large-Scale Text Mining

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i09.7092 ◽

2020 ◽

Vol 34 (09) ◽

pp. 13610-13611

Author(s):

Oktie Hassanzadeh ◽

Debarun Bhattacharjya ◽

Mark Feblowitz ◽

Kavitha Srinivas ◽

Michael Perrone ◽

...

Keyword(s):

Large Scale ◽

Causal Relation ◽

Relation Extraction ◽

Commercial Application ◽

Causal Knowledge ◽

Text Documents ◽

Semantic Constraints ◽

Enterprise Risk ◽

Supervised Methods ◽

Weakly Supervised

In this demonstration, we present a system for mining causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of causal relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a causal relation. It also provides evidence from an input corpus supporting the existence of a causal relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of causal relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.

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Learning to Transfer Relational Representations through Analogy

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.330110015 ◽

2019 ◽

Vol 33 ◽

pp. 10015-10016

Author(s):

Gaetano Rossiello ◽

Alfio Gliozzo ◽

Michael Glass

Keyword(s):

State Of The Art ◽

Relation Extraction ◽

Knowledge Bases ◽

The State ◽

Large Set ◽

Relational Information ◽

Siamese Network ◽

Distant Supervision ◽

Novel Approach ◽

Art Methods

We propose a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. We collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. This dataset is adopted to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. The model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a relation extraction task.

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Biomedical Relation Extraction Using Distant Supervision

Scientific Programming ◽

10.1155/2020/8893749 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Nada Boudjellal ◽

Huaping Zhang ◽

Asif Khan ◽

Arshad Ahmad

Keyword(s):

Big Data ◽

Information Extraction ◽

State Of The Art ◽

Relation Extraction ◽

Knowledge Bases ◽

Structured Data ◽

Distant Supervision ◽

Future Challenges ◽

Unstructured Information ◽

Biomedical Relation Extraction

With the accelerating growth of big data, especially in the healthcare area, information extraction is more needed currently than ever, for it can convey unstructured information into an easily interpretable structured data. Relation extraction is the second of the two important tasks of relation extraction. This study presents an overview of relation extraction using distant supervision, providing a generalized architecture of this task based on the state-of-the-art work that proposed this method. Besides, it surveys the methods used in the literature targeting this topic with a description of different knowledge bases used in the process along with the corpora, which can be helpful for beginner practitioners seeking knowledge on this subject. Moreover, the limitations of the proposed approaches and future challenges were highlighted, and possible solutions were proposed.

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Linking chemical and disease entities to ontologies by integrating PageRank with extracted relations from literature

Journal of Cheminformatics ◽

10.1186/s13321-020-00461-4 ◽

2020 ◽

Vol 12 (1) ◽

Author(s):

Pedro Ruas ◽

Andre Lamurias ◽

Francisco M. Couto

Keyword(s):

Digital Libraries ◽

Information Overload ◽

Relation Extraction ◽

Knowledge Bases ◽

Entity Linking ◽

Personalized Pagerank ◽

Named Entity ◽

Manual Curation ◽

Low Performance ◽

Gold Standards

Abstract Background Named Entity Linking systems are a powerful aid to the manual curation of digital libraries, which is getting increasingly costly and inefficient due to the information overload. Models based on the Personalized PageRank (PPR) algorithm are one of the state-of-the-art approaches, but these have low performance when the disambiguation graphs are sparse. Findings This work proposes a Named Entity Linking framework designated by Relation Extraction for Entity Linking (REEL) that uses automatically extracted relations to overcome this limitation. Our method builds a disambiguation graph, where the nodes are the ontology candidates for the entities and the edges are added according to the relations established in the text, which the method extracts automatically. The PPR algorithm and the information content of each ontology are then applied to choose the candidate for each entity that maximises the coherence of the disambiguation graph. We evaluated the method on three gold standards: the subset of the CRAFT corpus with ChEBI annotations (CRAFT-ChEBI), the subset of the BC5CDR corpus with disease annotations from the MEDIC vocabulary (BC5CDR-Diseases) and the subset with chemical annotations from the CTD-Chemical vocabulary (BC5CDR-Chemicals). The F1-Score achieved by REEL was 85.8%, 80.9% and 90.3% in these gold standards, respectively, outperforming baseline approaches. Conclusions We demonstrated that RE tools can improve Named Entity Linking by capturing semantic information expressed in text missing in Knowledge Bases and use it to improve the disambiguation graph of Named Entity Linking models. REEL can be adapted to any text mining pipeline and potentially to any domain, as long as there is an ontology or other knowledge Base available.

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