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Journal articles on the topic 'Information Extraction'

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Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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1

Zdravcheva, Neli. "INFORMATION EXTRACTION FROM MULTISPECTRAL SATELLITE IMAGES." Journal Scientific and Applied Research 24, no. 1 (November 23, 2023): 25–31. http://dx.doi.org/10.46687/jsar.v24i1.364.

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The article analyzes various methods and approaches of modern remote sensing that can be used in the processing of multispectral satellite images in order to effectively extract visual information about territories for which preliminary data is not available. Attention is paid to the creation of new derivative images (synthesized and indexed) and to performing pixel-oriented computer non supervised classification. A series of experiments have been made that clearly reveal the advantages and conveniences of remote retrieval of information from multispectral satellite images in a territory for which reference objects and other data acquired in situ are not available.

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2

Sarawagi, Sunita. "Information Extraction." Foundations and Trends® in Databases 1, no. 3 (2007): 261–377. http://dx.doi.org/10.1561/1900000003.

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Cowie, Jim, and Wendy Lehnert. "Information extraction." Communications of the ACM 39, no. 1 (January 1996): 80–91. http://dx.doi.org/10.1145/234173.234209.

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4

McCallum, Andrew. "Information Extraction." Queue 3, no. 9 (November 2005): 48–57. http://dx.doi.org/10.1145/1105664.1105679.

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Grishman, Ralph. "Information Extraction." IEEE Intelligent Systems 30, no. 5 (September 2015): 8–15. http://dx.doi.org/10.1109/mis.2015.68.

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6

Kassaie, Besat, and Frank Wm Tompa. "Autonomously Computable Information Extraction." Proceedings of the VLDB Endowment 16, no. 10 (June 2023): 2431–43. http://dx.doi.org/10.14778/3603581.3603585.

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Most optimization techniques deployed in information extraction systems assume that source documents are static. Instead, extracted relations can be considered to be materialized views defined by a language built on regular expressions. Using this perspective, we can provide an efficient verifier (using static analysis) that can be used to avoid the high cost of re-extracting information after an update. In particular, we propose an efficient mechanism to identify updates for which we can autonomously compute an extracted relation. We present experimental results that support the feasibility and practicality of this mechanism in real world extraction systems.

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Ling, Xiao, and Daniel Weld. "Temporal Information Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1385–90. http://dx.doi.org/10.1609/aaai.v24i1.7512.

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Research on information extraction (IE) seeks to distill relational tuples from natural language text, such as the contents of the WWW. Most IE work has focussed on identifying static facts, encoding them as binary relations. This is unfortunate, because the vast majority of facts are fluents, only holding true during an interval of time. It is less helpful to extract PresidentOf(Bill-Clinton, USA) without the temporal scope 1/20/93 — 1/20/01. This paper presents TIE, a novel, information-extraction system, which distills facts from text while inducing as much temporal information as possible. In addition to recognizing temporal relations between times and events, TIE performs global inference, enforcing transitivity to bound the start and ending times for each event. We introduce the notion of temporal entropy as a way to evaluate the performance of temporal IE systems and present experiments showing that TIE outperforms three alternative approaches.

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Turmo, Jordi, Alicia Ageno, and Neus Català. "Adaptive information extraction." ACM Computing Surveys 38, no. 2 (July 25, 2006): 4. http://dx.doi.org/10.1145/1132956.1132957.

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9

Vo, Duc-Thuan, and Ebrahim Bagheri. "Open information extraction." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630003. http://dx.doi.org/10.1142/s2425038416300032.

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Open information extraction (Open IE) systems aim to obtain relation tuples with highly scalable extraction in portable across domain by identifying a variety of relation phrases and their arguments in arbitrary sentences. The first generation of Open IE learns linear chain models based on unlexicalized features such as Part-of-Speech (POS) or shallow tags to label the intermediate words between pair of potential arguments for identifying extractable relations. Open IE currently is developed in the second generation that is able to extract instances of the most frequently observed relation types such as Verb, Noun and Prep, Verb and Prep, and Infinitive with deep linguistic analysis. They expose simple yet principled ways in which verbs express relationships in linguistics such as verb phrase-based extraction or clause-based extraction. They obtain a significantly higher performance over previous systems in the first generation. In this paper, we describe an overview of two Open IE generations including strengths, weaknesses and application areas.

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Aumann, Yonatan, Ronen Feldman, Yair Liberzon, Benjamin Rosenfeld, and Jonathan Schler. "Visual information extraction." Knowledge and Information Systems 10, no. 1 (April 4, 2006): 1–15. http://dx.doi.org/10.1007/s10115-006-0014-x.

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11

Gao, Zhangchi, and Shoubin Li. "Joint Information Extraction Model Based on Feature Sharing." International Journal of Emerging Technologies and Advanced Applications 1, no. 2 (March 26, 2024): 16–18. http://dx.doi.org/10.62677/ijetaa.2402107.

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To address the challenge of efficiently and accurately extracting entities, relationships, and events from unstructured text, a joint information extraction model based on feature sharing is proposed. This model utilizes the contextual information of entities, relationships, and events, and integrates entity extraction, relationship extraction, and event extraction tasks through a multi-feature cascade encoder to achieve joint extraction. To validate the effectiveness of the model, comparative analysis was conducted on military news datasets, comparing against two typical information extraction models. Results demonstrated superiority over current state-of-the-art baselines.

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Naoe, Kensuke, Hideyasu Sasaki, and Yoshiyasu Takefuji. "Information Hiding by Machine Learning." International Journal of Organizational and Collective Intelligence 2, no. 1 (January 2011): 21–48. http://dx.doi.org/10.4018/ijoci.2011010102.

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In this paper, the authors propose information hiding by machine learning: a method of key generation for information extracting using neural network. The method consists of three layers for information hiding. First, the proposed method prepares feature extraction keys, which are saved by feature extraction attributes like feature coordinates and the region of frequency coefficients. Second, the proposed method prepares hidden patterns in advance to the embedding procedure as a watermark signal of the target contents. Finally, the proposed method generates information extraction keys by using machine learning to output presented hidden patterns. The proper hidden patterns are generated with the proper information extraction key and feature extraction key. In the experiments, the authors show that the proposed method is robust to high pass filtering and JPEG compression. The proposed method contributes to secure visual information hiding without damaging any detailed data of the target content.

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Zhu, Wenhao, Chaoyou Ju, Wei Xu, Jiaoxiong Xia, and Li Fu. "Extracting Medical Records with Hierarchical Information Extraction Method." Information Technology Journal 12, no. 18 (September 1, 2013): 4441–46. http://dx.doi.org/10.3923/itj.2013.4441.4446.

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14

Pang, Yueyong, Chi Zhang, Liangchen Zhou, Bingxian Lin, and Guonian Lv. "Extracting Indoor Space Information in Complex Building Environments." ISPRS International Journal of Geo-Information 7, no. 8 (August 9, 2018): 321. http://dx.doi.org/10.3390/ijgi7080321.

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Indoor space information extraction is an important aspect of reconstruction for building information modeling and a necessary process for geographic information system from outdoor to indoor. Entity model extracting methods provide advantages in terms of accuracy for building indoor spaces, as compared with network and grid model methods, and the extraction results can be converted into a network or grid model. However, existing entity model extracting methods based on a search loop do not consider the complex indoor environment of a building, such as isolated columns and walls or cross-floor spaces. In this study, such complex indoor environments are analyzed in detail, and a new approach for extracting buildings’ indoor space information is proposed. This approach is based on indoor space boundary calculation, the Boolean difference for single-floor space extraction, relationship reconstruction, and cross-floor space extraction. The experimental results showed that the proposed method can accurately extract indoor space information from the complex indoor environment of a building with geometric, semantic, and relationship information. This study is theoretically important for better understanding the complexity of indoor space extraction and practically important for improving the modeling accuracy of buildings.

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15

Gutierrez, Fernando, Dejing Dou, Stephen Fickas, Daya Wimalasuriya, and Hui Zong. "A hybrid ontology-based information extraction system." Journal of Information Science 42, no. 6 (July 11, 2016): 798–820. http://dx.doi.org/10.1177/0165551515610989.

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Information Extraction is the process of automatically obtaining knowledge from plain text. Because of the ambiguity of written natural language, Information Extraction is a difficult task. Ontology-based Information Extraction (OBIE) reduces this complexity by including contextual information in the form of a domain ontology. The ontology provides guidance to the extraction process by providing concepts and relationships about the domain. However, OBIE systems have not been widely adopted because of the difficulties in deployment and maintenance. The Ontology-based Components for Information Extraction (OBCIE) architecture has been proposed as a form to encourage the adoption of OBIE by promoting reusability through modularity. In this paper, we propose two orthogonal extensions to OBCIE that allow the construction of hybrid OBIE systems with higher extraction accuracy and a new functionality. The first extension utilizes OBCIE modularity to integrate different types of implementation into one extraction system, producing a more accurate extraction. For each concept or relationship in the ontology, we can select the best implementation for extraction, or we can combine both implementations under an ensemble learning schema. The second extension is a novel ontology-based error detection mechanism. Following a heuristic approach, we can identify sentences that are logically inconsistent with the domain ontology. Because the implementation strategy for the extraction of a concept is independent of the functionality of the extraction, we can design a hybrid OBIE system with concepts utilizing different implementation strategies for extracting correct or incorrect sentences. Our evaluation shows that, in the implementation extension, our proposed method is more accurate in terms of correctness and completeness of the extraction. Moreover, our error detection method can identify incorrect statements with a high accuracy.

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16

Akimov, Dmitry Aleksandrovich. "Automatic Extraction of Scientific Information from Open Access Publications." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (February 28, 2020): 383–91. http://dx.doi.org/10.5373/jardcs/v12sp3/20201273.

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17

P. Vishwakarma, Virendra, Sujata Pandey, and M. N. Gupta. "Fuzzy based Pixel wise Information Extraction for Face Recognition." International Journal of Engineering and Technology 2, no. 1 (2010): 117–23. http://dx.doi.org/10.7763/ijet.2010.v2.109.

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18

Zhu, Dengyun, Qi Guo, Dongjiao Zhang, and Fucheng Wan. "Information Extraction Research Review." Journal of Physics: Conference Series 1769, no. 1 (January 1, 2021): 012004. http://dx.doi.org/10.1088/1742-6596/1769/1/012004.

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19

Thompson, Paul. "Concept-Based Information Extraction." Integrated Computer-Aided Engineering 1, no. 6 (November 1, 1994): 537–46. http://dx.doi.org/10.3233/ica-1994-1606.

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20

Murphy, A. J., N. W. Ratcliffe, D. A. H. Johnson, and D. J. Dewhurst. "Information Extraction from Speech." Annals of Otology, Rhinology & Laryngology 96, no. 1_suppl (January 1987): 69–71. http://dx.doi.org/10.1177/00034894870960s135.

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As an approach to the separation of the information in speech from speaker-specific attributes, we have used a modified cepstral analysis followed by a peak-tracking process in the cepstral domain. The resulting tracks show a high degree of similarity between different male speakers saying the same words and is a basis for speaker-independent word recognition. The method does not involve pitch frequency determination.

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21

Wang, Daisy Zhe, Michael J. Franklin, Minos Garofalakis, and Joseph M. Hellerstein. "Querying probabilistic information extraction." Proceedings of the VLDB Endowment 3, no. 1-2 (September 2010): 1057–67. http://dx.doi.org/10.14778/1920841.1920974.

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22

Gregg, Dawn G., and Steven Walczak. "Adaptive web information extraction." Communications of the ACM 49, no. 5 (May 2006): 78–84. http://dx.doi.org/10.1145/1125944.1125945.

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23

Craven, Mark. "Information extraction from Medline." ACM SIGBIO Newsletter 18, no. 3 (December 1998): 10. http://dx.doi.org/10.1145/956034.956049.

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24

Nakatani, Tomofumi, and Shogo Nakamura. "Extraction of phonemic information." Journal of the Acoustical Society of America 95, no. 1 (January 1994): 590. http://dx.doi.org/10.1121/1.408271.

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25

BOULAKNADEL, Siham, and Fadoua ATAA ALLAH. "Amazigh Biographical Information Extraction." International Journal of Knowledge Engineering and Data Mining 8, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijkedm.2023.10053903.

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26

Allah, Fadoua Ataa, and Siham Boulaknadel. "Amazigh biographical information extraction." International Journal of Knowledge Engineering and Data Mining 8, no. 1 (2023): 27–42. http://dx.doi.org/10.1504/ijkedm.2023.135715.

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27

Aguilar, Carlos, Lydia Sánchez, and Manuel Campos. "Extraction of Information of Audio-Visual Contents." tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 9, no. 2 (October 30, 2011): 543–50. http://dx.doi.org/10.31269/triplec.v9i2.311.

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In this article we show how it is possible to use Channel Theory (Barwise and Seligman, 1997) for modeling the process of information extraction realized by audiences of audio-visual contents. To do this, we rely on the concepts pro- posed by Channel Theory and, especially, its treatment of representational systems. We then show how the information that an agent is capable of extracting from the content depends on the number of channels he is able to establish between the content and the set of classifications he is able to discriminate. The agent can endeavor the extraction of information through these channels from the totality of content; however, we discuss the advantages of extracting from its constituents in order to obtain a greater number of informational items that represent it. After showing how the extraction process is endeavored for each channel, we propose a method of representation of all the informative values an agent can obtain from a content using a matrix constituted by the channels the agent is able to establish on the content (source classifications), and the ones he can understand as individual (destination classifications). We finally show how this representation allows reflecting the evolution of the informative items through the evolution of audio-visual content.

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Aguilar, Carlos, Lydia Sánchez, and Manuel Campos. "Extraction of Information of Audio-Visual Contents." tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 9, no. 2 (October 30, 2011): 543–50. http://dx.doi.org/10.31269/vol9iss2pp543-550.

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In this article we show how it is possible to use Channel Theory (Barwise and Seligman, 1997) for modeling the process of information extraction realized by audiences of audio-visual contents. To do this, we rely on the concepts pro- posed by Channel Theory and, especially, its treatment of representational systems. We then show how the information that an agent is capable of extracting from the content depends on the number of channels he is able to establish between the content and the set of classifications he is able to discriminate. The agent can endeavor the extraction of information through these channels from the totality of content; however, we discuss the advantages of extracting from its constituents in order to obtain a greater number of informational items that represent it. After showing how the extraction process is endeavored for each channel, we propose a method of representation of all the informative values an agent can obtain from a content using a matrix constituted by the channels the agent is able to establish on the content (source classifications), and the ones he can understand as individual (destination classifications). We finally show how this representation allows reflecting the evolution of the informative items through the evolution of audio-visual content.

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29

Mohamed Ali El-Morsy, Sally, Mahmoud Hussein, and Hamdy M. Mousa. "Arabic open information extraction system using dependency parsing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 541. http://dx.doi.org/10.11591/ijece.v12i1.pp541-551.

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<p>Arabic is a Semitic language and one of the most natural languages distinguished by the richness in morphological enunciation and derivation. This special and complex nature makes extracting information from the Arabic language difficult and always needs improvement. Open information extraction systems (OIE) have been emerged and used in different languages, especially in English. However, it has almost not been used for the Arabic language. Accordingly, this paper aims to introduce an OIE system that extracts the relation tuple from Arabic web text, exploiting Arabic dependency parsing and thinking carefully about all possible text relations. Based on clause types' propositions as extractable relations and constituents' grammatical functions, the identities of corresponding clause types are established. The proposed system named Arabic open information extraction(AOIE) can extract highly scalable Arabic text relations while being domain independent. Implementing the proposed system handles the problem using supervised strategies while the system relies on unsupervised extraction strategies. Also, the system has been implemented in several domains to avoid information extraction in a specific field. The results prove that the system achieves high efficiency in extracting clauses from large amounts of text.</p>

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30

Zhang, Zhi Ming, Shuai Shuai Huang, and Ping Li. "A Web Information Extraction Method Based on HTML Parser." Advanced Materials Research 774-776 (September 2013): 1802–6. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1802.

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With the rapid development of Internet, and surge in the amount of information on the Internet, how to accurately and quickly get the information of the users really need, such as the title, links, and pictures, is the hotspot. This paper proposed a fast web information extraction method based on html parser, this paper validated the effect of the proposed method by extracting commodities information of e-commerce website, the results show that the accuracy of the information extraction by our method is higher than the extraction method based on regular expressions, and the extraction time is greatly shortened.

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Wang, Yuyu, Gang Li, Wei Tang, Yussif M. Awelisah, and Ling Lin. "A Dynamic Spectrum extraction method for extracting blood scattering information — Dual-position extraction method." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 221 (October 2019): 116965. http://dx.doi.org/10.1016/j.saa.2019.03.075.

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32

Scholtz, Brenda, Thashen Padayachy, and Oluwande Adewoyin. "Pilot Testing of an Information Extraction (IE) Prototype for Legal Research." African Journal of Information and Communication, no. 25 (June 30, 2020): 1–20. http://dx.doi.org/10.23962/10539/29192.

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This article presents findings from pilot testing of elements of an information extraction (IE) prototype designed to assist legal researchers in engaging with case law databases. The prototype that was piloted seeks to extract, from legal case documents, relevant and accurate information on cases referred to (CRTs) in the source cases. Testing of CRT extraction from 50 source cases resulted in only 38% (n = 19) of the extractions providing an accurate number of CRTs. In respect of the prototype’s extraction of CRT attributes (case title, date, journal, and action), none of the 50 extractions produced fully accurate attribute information. The article outlines the prototype, the pilot testing process, and the test findings, and then concludes with a discussion of where the prototype needs to be improved.

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Jusoh, Shaidah, and Hejab M. Alfawareh. "Opinion analysis from reviews using information extraction and fuzzy sets." International Journal of Academic Research 6, no. 2 (March 30, 2014): 253–58. http://dx.doi.org/10.7813/2075-4124.2014/6-2/a.36.

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34

Wang, Zixiang, Tongliang Li, and Zhoujun Li. "Unsupervised Numerical Information Extraction via Exploiting Syntactic Structures." Electronics 12, no. 9 (April 24, 2023): 1977. http://dx.doi.org/10.3390/electronics12091977.

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Numerical information plays an important role in various fields such as scientific, financial, social, statistics, and news. Most prior studies adopt unsupervised methods by designing complex handcrafted pattern-matching rules to extract numerical information, which can be difficult to scale to the open domain. Other supervised methods require extra time, cost, and knowledge to design, understand, and annotate the training data. To address these limitations, we propose QuantityIE, a novel approach to extracting numerical information as structured representations by exploiting syntactic features of both constituency parsing (CP) and dependency parsing (DP). The extraction results may also serve as distant supervision for zero-shot model training. Our approach outperforms existing methods from two perspectives: (1) the rules are simple yet effective, and (2) the results are more self-contained. We further propose a numerical information retrieval approach based on QuantityIE to answer analytical queries. Experimental results on information extraction and retrieval demonstrate the effectiveness of QuantityIE in extracting numerical information with high fidelity.

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35

Gu, Jun Hua, Jie Song, Na Zhang, and Yan Liu Liu. "A Method of Web Information Automatic Extraction Based on XML." Applied Mechanics and Materials 20-23 (January 2010): 178–83. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.178.

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With the increasingly high-speed of the internet as well as the increase in the amount of data it contains, users are finding it more and more difficult to gain useful information from the web. How to extract accurate information from the Web efficiently has become an urgent problem. Web information extraction technology has emerged to solve this kind of problem. The method of Web information auto-extraction based on XML is designed through standardizing the HTML document using data translation algorism, forming an extracting rule base by learning the XPath expression of samples, and using extraction rule base to realize auto-extraction of pages of same kind. The results show that this approach should lead to a higher recall ratio and precision ratio, and the result should have a self-description, making it convenient for founding data extraction system of each domain.

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36

Wang, Haitao, Tong Zhu, Mingtao Wang, Guoliang Zhang, and Wenliang Chen. "A Prior Information Enhanced Extraction Framework for Document-level Financial Event Extraction." Data Intelligence 3, no. 3 (2021): 460–76. http://dx.doi.org/10.1162/dint_a_00103.

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Document-level financial event extraction (DFEE) is the task of detecting events and extracting the corresponding event arguments in financial documents, which plays an important role in information extraction in the financial domain. This task is challenging as the financial documents are generally long text and event arguments of one event may be scattered in different sentences. To address this issue, we proposed a novel Prior Information Enhanced Extraction framework (PIEE) for DFEE, leveraging prior information from both event types and pre-trained language models. Specifically, PIEE consists of three components: event detection, event argument extraction, and event table filling. In event detection, we identify the event type. Then, the event type is explicitly used for event argument extraction. Meanwhile, the implicit information within language models also provides considerable cues for event arguments localization. Finally, all the event arguments are filled in an event table by a set of predefined heuristic rules. To demonstrate the effectiveness of our proposed framework, we participated in the share task of CCKS2020 Task 4-2: Document-level Event Arguments Extraction. On both Leaderboard A and Leaderboard B, PIEE took the first place and significantly outperformed the other systems.

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Sethi, Ganesh K., and Rajesh K. Bawa. "Text Information Extraction: in Context of Indian Languages." Advanced Materials Research 433-440 (January 2012): 5012–19. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5012.

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Text data present in images contains useful information and its extraction involves detection, localization, extraction, enhancement and recognition. However, the problem is challenging due to fact that text can have various styles, size, orientations, alignments, effect of lighting conditions. While a large number of techniques have been proposed in the past for extracting text from images and video frames for foreign languages, not much research has been carried out for Indian languages. The purpose of this paper is to review various algorithms for the problem for foreign as well as for the for the Indian languages.

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Kuttaiyapillai, Dhanasekaran, Anand Madasamy, Shobanadevi Ayyavu, and Md Shohel Sayeed. "Clinical named entity extraction for extracting information from medical data." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (September 1, 2024): 1722. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1722-1731.

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Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine learning approaches rely on the time-consuming process of creating corpora and cannot extract information for specific needs and locations in certain situations. This paper presents a clinical NER approach based on a convolutional neural network (CNN) for better use of specific features around medical entities and analyzes the performance of the proposed approach through fine-tuning NER with effective pre-training on the BC5CDR dataset. The proposed method uses annotation of entities for various medical concepts. The second stage develops a clinically NER method. This proposed method shows interesting results on the performance measures achieving a precision of 92.57%, recall of 92.22%, and F1- measure of 91.6%.

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39

Lehnert, W., C. Cardie, D. Fisher, J. McCarthy, E. Riloff, and S. Soderland. "Evaluating an Information Extraction System." Integrated Computer-Aided Engineering 1, no. 6 (November 1, 1994): 453–72. http://dx.doi.org/10.3233/ica-1994-1601.

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40

Fiori, Alessandro, Alberto Grand, Giulia Bruno, Francesco Gavino Brundu, Domenico Schioppa, and Andrea Bertotti. "Information Extraction from Microarray Data." Journal of Database Management 25, no. 1 (January 2014): 29–58. http://dx.doi.org/10.4018/jdm.2014010102.

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Nowadays, a huge amount of high throughput molecular data are available for analysis and provide novel and useful insights into complex biological systems, through the acquisition of a high-resolution picture of their molecular status in defined experimental conditions. In this context, microarrays are a powerful tool to analyze thousands of gene expression values with a single experiment. A number of approaches have been developed to detecting genes highly correlated to diseases, selecting genes that exhibit a similar behavior under specific conditions, building models to predict disease outcome based on genetic profiles, and inferring regulatory networks. This paper discusses popular and recent data mining techniques (i.e., Feature Selection, Clustering, Classification, and Association Rule Mining) applied to microarray data. The main characteristics of microarray data and preprocessing procedures are presented to understand the critical issues introduced by gene expression values analysis. Each technique is analyzed, and relevant examples of pertinent literature are reported. Moreover, real use cases exploiting analytic pipelines that use these methods are also introduced. Finally, future directions of data mining research on microarray data are envisioned.

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41

Kim, Jaeyoon. "Legislative Research Using Information Extraction." Korean Journal of Law and Economics 19, no. 1 (April 30, 2022): 1–18. http://dx.doi.org/10.46758/kjle.2022.04.19.1.1.

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42

Chong, William, and Angela Goh. "FIES: Financial Information Extraction System." Information Services & Use 17, no. 4 (October 1, 1997): 215–23. http://dx.doi.org/10.3233/isu-1997-17401.

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43

Hope, Carol J., Jennifer Hornung Garvin, and Brian C. Sauer. "Information extraction from narrative data." American Journal of Health-System Pharmacy 69, no. 6 (March 15, 2012): 455–61. http://dx.doi.org/10.2146/ajhp110135.

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44

Feldman, Ronen, Yizhar Regev, and Maya Gorodetsky. "A modular information extraction system." Intelligent Data Analysis 12, no. 1 (February 18, 2008): 51–71. http://dx.doi.org/10.3233/ida-2008-12104.

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Gottlob, Georg, and Christoph Koch. "Logic-based web information extraction." ACM SIGMOD Record 33, no. 2 (June 2004): 87–94. http://dx.doi.org/10.1145/1024694.1024711.

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Balaji, H., and Dr A. Govardhan. "Information Extraction with Semantic Clustering." i-manager's Journal on Computer Science 3, no. 1 (May 15, 2015): 15–20. http://dx.doi.org/10.26634/jcom.3.1.3437.

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赵, 海霞. "Knowledge Graph Oriented Information Extraction." Hans Journal of Data Mining 10, no. 04 (2020): 282–302. http://dx.doi.org/10.12677/hjdm.2020.104030.

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Asoodeh, Shahab, Mario Diaz, Fady Alajaji, and Tamás Linder. "Information Extraction Under Privacy Constraints." Information 7, no. 1 (March 10, 2016): 15. http://dx.doi.org/10.3390/info7010015.

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Tanaka, Masahiro, Ryo Hamamura, and Andrzej Bargiela. "Information Extraction from Traffic Images." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2004 (May 5, 2004): 41–46. http://dx.doi.org/10.5687/sss.2004.41.

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