Crime Event Localization and Deduplication

Rollo, Federica; Po, Laura

doi:10.1007/978-3-030-62466-8_23

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…An example of CEM usage is given in Figure 2 that illustrates two news articles instances referring to the same crime event (cem:Theft) happened in a city named Castelvetro di Modena. The news articles are taken from the Italian Crime News dataset 7 [11] which collects some news articles from the "Gazzetta di Modena" newspaper and extracts semantic information from the news articles' body. In particular, one of the two news articles in the Figure derives from the translation from Italian to English of news published in March 2022.…”

Section: Exemplar Crime Event Annotationmentioning

confidence: 99%

CEM: an Ontology for Crime Events in Newspaper Articles

Rollo

Castellucci

2023

Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing

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The adoption of semantic technologies for the representation of crime events can help law enforcement agencies (LEAs) in crime prevention and investigation. Moreover, online newspapers and social networks are valuable sources for crime intelligence gathering. In this paper, we propose a new lightweight ontology to model crime events as they are usually described in online news articles. The Crime Event Model (CEM) can integrate specific data about crimes, i.e., where and when they occurred, who is involved (author, victim, and other subjects involved), which is the reason for the occurrence, and details about the source of information (e.g., the news article). Extracting structured data from multiple online sources and interconnecting them in a Knowledge Graph using CEM allow events relationships extraction, patterns and trends identification, and event recommendation. The CEM ontology is available at https://w3id.org/CEMontology.

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Section: Exemplar Crime Event Annotationmentioning

confidence: 99%

CEM: an Ontology for Crime Events in Newspaper Articles

Rollo

Castellucci

2023

Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing

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“…In Italy, police crime reports are not available to citizens, only some aggregated analyses are published yearly. Indeed, several works concerning crime analysis exploit news articles [8][9][10][11]. Detailed information about the crime events can be extracted through the application of Natural Language Processing (NLP) techniques to the news articles' text.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The texts to categorize are extracted from an Italian dataset of crime news articles. The information about the news articles is collected by the Crime Ingestion App [11], a Java application that aims at extracting, geolocalizing and deduplicating crime-related news articles from two online newspapers of the province of Modena, in Italy ("ModenaToday" 2 and "Gazzetta di Modena" 3 ). The selection of these newspapers is motivated by their popularity.…”

Section: Italian Crime News Datasetmentioning

confidence: 99%

Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

Rollo

Bonisoli

2022

Lecture Notes in Business Information Processing

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The automatic categorization of crime news is useful to create statistics on the type of crimes occurring in a certain area. This assignment can be treated as a text categorization problem. Several studies have shown that the use of word embeddings improves outcomes in many Natural Language Processing (NLP), including text categorization. The scope of this paper is to explore the use of word embeddings for Italian crime news text categorization. The approach followed is to compare different document pre-processing, Word2Vec models and methods to obtain word embeddings, including the extraction of bigrams and keyphrases. Then, supervised and unsupervised Machine Learning categorization algorithms have been applied and compared. In addition, the imbalance issue of the input dataset has been addressed by using Synthetic Minority Oversampling Technique (SMOTE) to oversample the elements in the minority classes. Experiments conducted on an Italian dataset of 17,500 crime news articles collected from 2011 till 2021 show very promising results. The supervised categorization has proven to be better than the unsupervised categorization, overcoming 80% both in precision and recall, reaching an accuracy of 0.86. Furthermore, lemmatization, bigrams and keyphrase extraction are not so decisive. In the end, the availability of our model on GitHub together with the code we used to extract word embeddings allows replicating our approach to other corpus either in Italian or other languages.

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“…Impact on adoption of semantic web technologies: In the context of the Semantic Web, a variety of models and applications, including link prediction, creation of domain-specific knowledge graphs [9] and Question Answering for event-centric questions [6] make use of geographic data. Semantic technologies have been applied to a variety of domains that require spatio-temporal data, including crime localization, transport data, and historical maps [26,28,29]. Furthermore, with the increased availability of mobile devices, location-based algorithms such as next location recommendation or trip planning evolved.…”

Section: Predicted Impactmentioning

confidence: 99%

GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale

Tempelmeier,

Gottschalk,

Demidova

2021

Preprint

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OpenStreetMap (OSM) is currently the richest publicly available information source on geographic entities (e.g., buildings and roads) worldwide. However, using OSM entities in machine learning models and other applications is challenging due to the large scale of OSM, the extreme heterogeneity of entity annotations, and a lack of a well-defined ontology to describe entity semantics and properties. This paper presents GeoVectors -a unique, comprehensive world-scale linked open corpus of OSM entity embeddings covering the entire OSM dataset and providing latent representations of over 980 million geographic entities in 180 countries. The GeoVectors corpus captures semantic and geographic dimensions of OSM entities and makes these entities directly accessible to machine learning algorithms and semantic applications. We create a semantic description of the GeoVectors corpus, including identity links to the Wikidata and DBpedia knowledge graphs to supply context information. Furthermore, we provide a SPARQL endpoint -a semantic interface that offers direct access to the semantic and latent representations of geographic entities in OSM.

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Crime Event Localization and Deduplication

Cited by 14 publications

References 12 publications

CEM: an Ontology for Crime Events in Newspaper Articles

CEM: an Ontology for Crime Events in Newspaper Articles

Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale

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