Crime base: Towards building a knowledge base for crime entities and their relationships from online news papers

Srinivasa, K. G.; Thilagam, P. Santhi

doi:10.1016/j.ipm.2019.102059

Cited by 31 publications

(18 citation statements)

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“…The framework consists of two main stages, namely clustering and extraction, to solve respective tasks mentioned in Section 3 and are discussed in the following sections. Initially, the crime-related Hindi articles are selected by applying topic modeling and knowledge base aided data acquisition method proposed by [2] over the headlines translated to English. The redundancies among the articles are exploited by identifying the comparable articles.…”

Section: Methodsmentioning

confidence: 99%

“…Hence topically re-lated articles are enriched with more new information compare to sententially related articles. The proposed work identifies the comparable articles using the semantic merging procedure mentioned in [2]. As the news articles are published daily, the articles are considered as data streams and an incremental nearest neighborhood algorithm for clustering data streams is adopted [4].…”

Section: Methodsmentioning

confidence: 99%

“…To improve the semantics, the articles are represented as KB facts in the form of triples extracted over the headlines. Accordingly, the headlines from Hindi news articles are translated to English, and facts from the translated headlines are extracted using the method proposed in [2]. When a stream of facts from English and Hindi news articles comes in, we divide them into various windows based on their date of publication.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we extended the work proposed by [2] to develop a KB called "Crime Base" which was enriched with the facts extracted from only English news articles. KB so developed was proved to be incomplete by manually cross verifying the related bilingual English-Hindi language articles.…”

Section: Introductionmentioning

confidence: 99%

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Clustering and Bootstrapping Based Framework for News Knowledge Base Completion

Srinivasa¹,

Thilagam

2021

cai

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Extracting the facts, namely entities and relations, from unstructured sources is an essential step in any knowledge base construction. At the same time, it is also necessary to ensure the completeness of the knowledge base by incrementally extracting the new facts from various sources. To date, the knowledge base completion is studied as a problem of knowledge refinement where the missing facts are inferred by reasoning about the information already present in the knowledge base. However, facts missed while extracting the information from multilingual sources are ignored. Hence, this work proposed a generic framework for knowledge base completion to enrich a knowledge base of crime-related facts extracted from online news articles in the English language, with the facts extracted from low resourced Indian language Hindi news articles. Using the framework, information from any low-resourced language news articles can be extracted without using language-specific tools like POS tags and using an appropriate machine translation tool. To achieve this, a clustering algorithm is proposed, which explores the redundancy among the bilingual collection of news articles by representing the clusters with knowledge base facts unlike the existing Bag of Words representation. From each cluster, the facts extracted from English language articles are bootstrapped to extract the facts from comparable Hindi language articles. This way of bootstrapping within the cluster helps to identify the sentences from a low-resourced language that are enriched with new information related to the facts extracted from a high-resourced language like English. The empirical result shows that the proposed clustering algorithm produced more accurate and high-quality clusters for monolingual and cross-lingual facts, respectively. Experiments also proved that the proposed framework achieves a high recall rate in extracting the new facts from Hindi news articles.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clustering and Bootstrapping Based Framework for News Knowledge Base Completion

Srinivasa¹,

Thilagam

2021

cai

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“…Shrinivasa et al [35] developed a knowledge base named as crime base from online news articles in leading Indian newspapers Times of India and Deccan Chronicle from Jan 2018 to Jun 2018 as crime reports published in newspapers are more authenticate then info available on social media crime base contains crime entities from multiple modalities in machine-readable form which can be useful to law enforcement agencies for crime activities analysis and future predictions. The novelty of this work is considering the image as well as text data for the construction of a knowledge base.…”

Section: Knowledge Base Population For Legal Text Processingmentioning

confidence: 99%

An overview of information extraction techniques for legal document analysis and processing

Zadgaonkar¹,

Agrawal²

2021

IJECE

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<span>In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing.</span>

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Open source intelligence extraction for terrorism‐related information: A review

Chaudhary

Bansal

2022

WIREs Data Min & Knowl

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In this contemporary era, where a large part of the world population is deluged by extensive use of the internet and social media, terrorists have found it a potential opportunity to execute their vicious plans. They have got a befitting medium to reach out to their targets to spread propaganda, disseminate training content, operate virtually, and further their goals. To restrain such activities, information over the internet in context of terrorism needs to be analyzed to channel it to appropriate measures in combating terrorism. Open Source Intelligence (OSINT) accounts for a felicitous solution to this problem, which is an emerging discipline of leveraging publicly accessible sources of information over the internet by effectively utilizing it to extract intelligence. The process of OSINT extraction is broadly observed to be in three phases (i) Data Acquisition, (ii) Data Enrichment, and (iii) Knowledge Inference. In the context of terrorism, researchers have given noticeable contributions in compliance with these three phases. However, a comprehensive review that delineates these research contributions into an integrated workflow of intelligence extraction has not been found. The paper presents the most current review in OSINT, reflecting how the various state‐of‐the‐art tools and techniques can be applied in extracting terrorism‐related textual information from publicly accessible sources. Various data mining and text analysis‐based techniques, that is, natural language processing, machine learning, and deep learning have been reviewed to extract and evaluate textual data. Additionally, towards the end of the paper, we discuss challenges and gaps observed in different phases of OSINT extraction. This article is categorized under: Application Areas > Government and Public Sector Commercial, Legal, and Ethical Issues > Social Considerations Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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Crime base: Towards building a knowledge base for crime entities and their relationships from online news papers

Cited by 31 publications

References 18 publications

Clustering and Bootstrapping Based Framework for News Knowledge Base Completion

Clustering and Bootstrapping Based Framework for News Knowledge Base Completion

An overview of information extraction techniques for legal document analysis and processing

Open source intelligence extraction for terrorism‐related information: A review

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