Event prediction from news text using subgraph embedding and graph sequence mining

Date

2022-2-28

Author

Çekinel, Recep Fırat
Karagöz, Pınar

Metadata

Show full item record

Item Usage Stats

204
views

0
downloads

Event detection from textual content by using text mining concepts is a well-researched field in the literature. On the other hand, graph modeling and graph embedding techniques in recent years provide an opportunity to represent textual contents as graphs. Text can be enriched with additional attributes in graphs, and the complex relationships can be captured within the graphs. In this paper, we focus on news prediction and model the problem as subgraph prediction. More specifically, we aim to predict the news skeleton in the form of a subgraph. To this aim, graph-based representations of news articles are constructed and a graph mining based pattern extraction method is proposed. The proposed method consists of three main steps. Initially, graph representation of the news text is constructed. Afterwards, frequent subgraph mining and sequential rule mining algorithms are adapted for pattern prediction on graph sequences. We consider that a subgraph captures the main story of the contents, and the sequential rules indicate the subgraph patterns’ temporal relationships. Finally, extracted sequential patterns are used for predicting the future news skeleton (i.e. main features of the news). In order to measure the similarity, graph embedding techniques are also employed. The proposed method is analyzed on both a collection of news from an online newspaper and on a benchmark news dataset against baseline methods.

Subject Keywords

Computer Networks and Communications, Hardware and Architecture, Software

URI

https://hdl.handle.net/11511/96284

Journal

World Wide Web

DOI

https://doi.org/10.1007/s11280-021-01002-1

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

RELIEF-MM: effective modality weighting for multimedia information retrieval Yilmaz, Turgay; Yazıcı, Adnan; Kitsuregawa, Masaru (Springer Science and Business Media LLC, 2014-07-01) Fusing multimodal information in multimedia data usually improves the retrieval performance. One of the major issues in multimodal fusion is how to determine the best modalities. To combine the modalities more effectively, we propose a RELIEF-based modality weighting approach, named as RELIEF-MM. The original RELIEF algorithm is extended for weaknesses in several major issues: class-specific feature selection, complexities with multi-labeled data and noise, handling unbalanced datasets, and using the algori...
Optimum design and operation of 'a pump-piping-storage system' Kaplan, H; Seireg, A; Dölen, Melik (Inderscience Publishers, 2001-01-01) The study reported in this paper is undertaken to develop a computer simulation and an optimum design and operation strategy for a general storage system. The influence of various design parameters on the total operation cost of the system for a general delivery regime is also studied.
SWARM-based data delivery in Social Internet of Things Hasan, Mohammed Zaki; Al-Turjman, Fadi (Elsevier BV, 2019-03-01) Social Internet of Things (SIoTs) refers to the rapidly growing network of connected objects and people that are able to collect and exchange data using embedded sensors. To guarantee the connectivity among these objects and people, fault tolerance routing has to be significantly considered. In this paper, we propose a bio-inspired particle multi-swarm optimization (PMSO) routing algorithm to construct, recover and select k-disjoint paths that tolerates the failure while satisfying quality of service (QoS) ...
Path planning for mobile-anchor based wireless sensor network localization: Static and dynamic schemes Erdemir, Ecenaz; Tuncer, Temel Engin (Elsevier BV, 2018-08-01) In wireless sensor networks, node locations are required for many applications. Usually, anchors with known positions are employed for localization. Sensor positions can be estimated more efficiently by using mobile anchors (MAs). Finding the best MA trajectory is an important problem in this context. Various path planning algorithms are proposed to localize as many sensors as possible by following the shortest path with minimum number of anchors. In this paper, path planning algorithms for MA assisted loca...
Graph-based multilevel temporal video segmentation Sakarya, Ufuk; TELATAR, ZİYA (Springer Science and Business Media LLC, 2008-11-01) This paper presents a graph-based multilevel temporal video segmentation method. In each level of the segmentation, a weighted undirected graph structure is implemented. The graph is partitioned into clusters which represent the segments of a video. Three low-level features are used in the calculation of temporal segments' similarities: visual content, motion content and shot duration. Our strength factor approach contributes to the results by improving the efficiency of the proposed method. Experiments sho...

Citation Formats

R. F. Çekinel and P. Karagöz, “Event prediction from news text using subgraph embedding and graph sequence mining,” World Wide Web, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/96284.