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Journal articles on the topic 'Open information extraction'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 June 2025

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1

Vo, Duc-Thuan, and Ebrahim Bagheri. "Open information extraction." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (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 typ

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

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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 (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 parsi

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Sally, Mohamed Ali El-Morsy, Hussein Mahmoud, and M. Mousa Hamdy. "Arabic open information extraction system using dependency parsing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 541–51. https://doi.org/10.11591/ijece.v12i1.pp541-551.

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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 th

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Hwang, Hyunsun, and Changki Lee. "BERT-based Korean Open Information Extraction." KIISE Transactions on Computing Practices 26, no. 9 (2020): 414–18. http://dx.doi.org/10.5626/ktcp.2020.26.9.414.

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Etzioni, Oren, Michele Banko, Stephen Soderland, and Daniel S. Weld. "Open information extraction from the web." Communications of the ACM 51, no. 12 (2008): 68–74. http://dx.doi.org/10.1145/1409360.1409378.

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Bassa, Akim, Mark Kroll, and Roman Kern. "GerIE - An Open Information Extraction System for the German Language." JUCS - Journal of Universal Computer Science 24, no. (1) (2018): 2–24. https://doi.org/10.3217/jucs-024-01-0002.

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Open Information Extraction (OIE) allows to extract relations from a text without the need of domain-speci_c training data. To date, most of the research on OIE has been focused to the English language and little or no research has been conducted on other languages, including German. To tackle this problem, we developed GerIE, an OIE system for the German language. We surveyed the literature on OIE in order to identify concepts that may apply to the German language. Our system is based on the output of a German dependency parser and a number of handcrafted rules to extract the propositions. To

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Sena, Cleiton Fernando Lima, and Daniela Barreiro Claro. "InferPortOIE: A Portuguese Open Information Extraction system with inferences." Natural Language Engineering 25, no. 2 (2018): 287–306. http://dx.doi.org/10.1017/s135132491800044x.

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AbstractNowadays, there is an increasing amount of digital data. In the case of the Web, daily, a vast collection of data is generated, whose contents are heterogeneous. A significant portion of this data is available in a natural language format. Open Information Extraction (Open IE) enables the extraction of facts from large quantities of texts written in natural language. In this work, we propose an Open IE method to extract facts from texts written in Portuguese. We developed two new rules that generalize the inference by transitivity and by symmetry. Consequently, this approach increases

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Kadu, Payal. "Knowledge Extraction from Text Document Using Open Information Extraction Technique." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 2 (2020): 2280–83. http://dx.doi.org/10.30534/ijatcse/2020/208922020.

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Han, Jiabao, and Hongzhi Wang. "Transformer based network for Open Information Extraction." Engineering Applications of Artificial Intelligence 102 (June 2021): 104262. http://dx.doi.org/10.1016/j.engappai.2021.104262.

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Ali, Sally, Hamdy Mousa, and M. Hussien. "A Review of Open Information Extraction Techniques." IJCI. International Journal of Computers and Information 6, no. 1 (2019): 20–28. http://dx.doi.org/10.21608/ijci.2019.35099.

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Claro, Daniela Barreiro, Marlo Souza, Clarissa Castellã Xavier, and Leandro Oliveira. "Multilingual Open Information Extraction: Challenges and Opportunities." Information 10, no. 7 (2019): 228. http://dx.doi.org/10.3390/info10070228.

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The number of documents published on the Web in languages other than English grows every year. As a consequence, the need to extract useful information from different languages increases, highlighting the importance of research into Open Information Extraction (OIE) techniques. Different OIE methods have dealt with features from a unique language; however, few approaches tackle multilingual aspects. In those approaches, multilingualism is restricted to processing text in different languages, rather than exploring cross-linguistic resources, which results in low precision due to the use of gene

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Weld, Daniel S., Raphael Hoffmann, and Fei Wu. "Using Wikipedia to bootstrap open information extraction." ACM SIGMOD Record 37, no. 4 (2009): 62–68. http://dx.doi.org/10.1145/1519103.1519113.

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Mora-Cross, Maria, William Ulate, Chacón Brandon Retana, Portillo María Biarreta, Ramírez Josué David Castro, and Madriz Jose Chavarria. "Structuring Information from Plant Morphological Descriptions using Open Information Extraction." Biodiversity Information Science and Standards 7 (September 21, 2023): e113055. https://doi.org/10.3897/biss.7.113055.

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Taxonomic literature keeps records of the planet's biodiversity and gives access to the knowledge needed for research and sustainable management. The number of publications generated is quite large: the corpus of biodiversity literature includes tens of millions of figures and taxonomic treatments. Unfortunately, most of the taxonomic descriptions are from scientific publications in text format. With more than 61 million digitized pages in the Biodiversity Heritage Library (BHL), only 467,265 taxonomic treatments are available in the Biodiversity Literature Repository. To obtain highly structu

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Zdebskyi, Petro, Andriy Berko, and Lyubomyr Chyrun. "Information system for extraction of information from open web resources." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 12 (December 15, 2022): 141–68. http://dx.doi.org/10.23939/sisn2022.12.141.

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The purpose of the work is to develop a project of an information and reference system for finding answers to questions based on the highest degree of comparison using text content from open English- language web resources. Examples of such questions can be: “What is the best book ever?”, “What is the most popular IDE for Python”. The result of the functioning of the information and reference system is a ranked list of answers based on the frequency of appearance of each of the answer options. Also, a numerical characteristic of the probability of the preference of a particular answer over oth

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Abreu, Sandra Collovini de, and Renata Vieira. "RelP: Portuguese Open Relation Extraction." KNOWLEDGE ORGANIZATION 44, no. 3 (2017): 163–77. http://dx.doi.org/10.5771/0943-7444-2017-3-163.

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Soderland, Stephen, Brendan Roof, Bo Qin, Shi Xu, Mausam, and Oren Etzioni. "Adapting Open Information Extraction to Domain-Specific Relations." AI Magazine 31, no. 3 (2010): 93. http://dx.doi.org/10.1609/aimag.v31i3.2305.

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Information extraction (IE) can identify a set of relations from free text to support question answering (QA). Until recently, IE systems were domain-specific and needed a combination of manual engineering and supervised learning to adapt to each target domain. A new paradigm, Open IE operates on large text corpora without any manual tagging of relations, and indeed without any pre-specified relations. Due to its open-domain and open-relation nature, Open IE is purely textual and is unable to relate the surface forms to an ontology, if known in advance. We explore the steps needed to adapt Ope

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Zadgaonkar, Ashwini V. "Natural Language Understanding Using Open Information Extraction Technique." International Journal of Computer Sciences and Engineering 6, no. 1 (2018): 347–50. http://dx.doi.org/10.26438/ijcse/v6i1.347350.

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Rahat, Mahmoud, and Alireza Talebpour. "Parsa: An open information extraction system for Persian." Digital Scholarship in the Humanities 33, no. 4 (2018): 874–93. http://dx.doi.org/10.1093/llc/fqy003.

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Khairova, Nina, Orken Mamyrbayev, Kuralay Mukhsina, Anastasiia Kolesnyk, and Saurabh Pratap. "Logical-linguistic model for multilingual Open Information Extraction." Cogent Engineering 7, no. 1 (2020): 1714829. http://dx.doi.org/10.1080/23311916.2020.1714829.

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Glauber, Rafael, and Daniela Barreiro Claro. "A systematic mapping study on open information extraction." Expert Systems with Applications 112 (December 2018): 372–87. http://dx.doi.org/10.1016/j.eswa.2018.06.046.

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Liao, Yan, Jialin Hua, Liangqing Luo, Weiying Ping, Xuewen Lu, and Yuansheng Zhong. "APRCOIE: An open information extraction system for Chinese." SoftwareX 26 (May 2024): 101649. http://dx.doi.org/10.1016/j.softx.2024.101649.

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Li, Huagang, and Bo Liu. "An Open Relation Extraction System for Web Text Information." Applied Sciences 12, no. 11 (2022): 5718. http://dx.doi.org/10.3390/app12115718.

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Web texts typically undergo the open-ended growth of new relations. Traditional relation extraction methods lack automatic annotation and perform poorly on new relation extraction tasks. We propose an open-domain relation extraction system (ORES) based on distant supervision and few-shot learning to solve this problem. More specifically, we utilize tBERT to design instance selector 1, implementing automatic labeling in the data mining component. Meanwhile, we design example selector 2 based on K-BERT in the new relation extraction component. The real-time data management component outputs new

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De, Souza Erick Nilsen Pereira, Daniela Claro, and Rafael Glauber. "A Similarity Grammatical Structures Based Method for Improving Open Information Systems." JUCS - Journal of Universal Computer Science 24, no. (1) (2018): 43–69. https://doi.org/10.3217/jucs-024-01-0043.

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Open information extraction (Open IE) discovers facts as triples of relationships in texts. A major challenge to Open IE task is to reduce the proportion of invalid extractions. Current methods based on a set of specific features eliminate many inconsistent and incomplete facts. However, these solutions have the disadvantage of being highly language-dependent. This dependence arises from the difficulty in finding the most representative set of features, considering the peculiarities of each language. These solutions require extensive training sets, usually produced with the aid of a specialize

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Zhan, Junlang, and Hai Zhao. "Span Model for Open Information Extraction on Accurate Corpus." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9523–30. http://dx.doi.org/10.1609/aaai.v34i05.6497.

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Open Information Extraction (Open IE) is a challenging task especially due to its brittle data basis. Most of Open IE systems have to be trained on automatically built corpus and evaluated on inaccurate test set. In this work, we first alleviate this difficulty from both sides of training and test sets. For the former, we propose an improved model design to more sufficiently exploit training dataset. For the latter, we present our accurately re-annotated benchmark test set (Re-OIE2016) according to a series of linguistic observation and analysis. Then, we introduce a span model instead of prev

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Vasilkovsky, Michael, Anton Alekseev, Valentin Malykh, et al. "DetIE: Multilingual Open Information Extraction Inspired by Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11412–20. http://dx.doi.org/10.1609/aaai.v36i10.21393.

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State of the art neural methods for open information extraction (OpenIE) usually extract triplets (or tuples) iteratively in an autoregressive or predicate-based manner in order not to produce duplicates. In this work, we propose a different approach to the problem that can be equally or more successful. Namely, we present a novel single-pass method for OpenIE inspired by object detection algorithms from computer vision. We use an order-agnostic loss based on bipartite matching that forces unique predictions and a Transformer-based encoder-only architecture for sequence labeling. The proposed

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Sahnoun, Sihem, Samir Elloumi, and Sadok Ben Yahia. "Event detection based on open information extraction and ontology." Journal of Information and Telecommunication 4, no. 3 (2020): 383–403. http://dx.doi.org/10.1080/24751839.2020.1763007.

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Sena, Cleiton Fernando Lima, and Daniela Barreiro Claro. "PragmaticOIE: a pragmatic open information extraction for Portuguese language." Knowledge and Information Systems 62, no. 9 (2020): 3811–36. http://dx.doi.org/10.1007/s10115-020-01442-7.

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Wang, Zixiang, Tongliang Li, and Zhoujun Li. "Unsupervised Numerical Information Extraction via Exploiting Syntactic Structures." Electronics 12, no. 9 (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 feature

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Daoud, Daoud M., and Samir Abou El-Seoud. "Employing Information Extraction for Building Mobile Applications." International Journal of Interactive Mobile Technologies (iJIM) 11, no. 2 (2017): 99. http://dx.doi.org/10.3991/ijim.v11i2.6569.

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We describe a SMS-based information system called CATS, which allows posting and searching through free Arabic text using Information Extraction (IE) technology. We discuss the challenges of applying IE technology for unedited real Arabic text. In addition, we describe the structure of this system and our approach to produce an open robust system capable of including more sub domains with the minimum effort.

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Kang, Tian, Shaodian Zhang, Youlan Tang, et al. "EliIE: An open-source information extraction system for clinical trial eligibility criteria." Journal of the American Medical Informatics Association 24, no. 6 (2017): 1062–71. http://dx.doi.org/10.1093/jamia/ocx019.

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Abstract Objective To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0. Materials and Methods EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring. Informaticians and domain experts were recruited to design an annotation guideline and ge

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MAHULE, Rajesh, and Om Prakash VYAS. "Leveraging linked open data information extraction for data mining applications." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 4874–84. http://dx.doi.org/10.3906/elk-1412-28.

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Reshadat, Vahideh, and Heshaam Faili. "A New Open Information Extraction System Using Sentence Difficulty Estimation." Computing and Informatics 38, no. 4 (2019): 986–1008. http://dx.doi.org/10.31577/cai_2019_4_986.

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Li, Tongliang, Zixiang Wang, Linzheng Chai, et al. "mt4CrossOIE: Multi-stage tuning for cross-lingual open information extraction." Expert Systems with Applications 255 (December 2024): 124760. http://dx.doi.org/10.1016/j.eswa.2024.124760.

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Carta, Salvatore, Pietro Fariello, Alessandro Giuliani, Leonardo Piano, Alessandro Sebastian Podda, and Sandro Gabriele Tiddia. "SailGenie: SAiling expertIse to knowLedge Graph through opEN Information Extraction." Procedia Computer Science 225 (2023): 2224–33. http://dx.doi.org/10.1016/j.procs.2023.10.213.

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Rahat, Mahmoud, Alireza Talebpour, and Seyedamin Monemian. "A recursive algorithm for open information extraction from Persian texts." International Journal of Computer Applications in Technology 57, no. 3 (2018): 193. http://dx.doi.org/10.1504/ijcat.2018.092978.

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Monemian, Seyedamin, Mahmoud Rahat, and Alireza Talebpour. "A recursive algorithm for open information extraction from Persian texts." International Journal of Computer Applications in Technology 57, no. 3 (2018): 193. http://dx.doi.org/10.1504/ijcat.2018.10014075.

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Yang, Zhongguo, Mingzhu Zhang, Zhongmei Zhang, Han Li, Chen Liu, and Sikandar Ali. "Lecture Information Service Based on Multiple Features Fusion." International Journal of Software Engineering and Knowledge Engineering 31, no. 04 (2021): 545–62. http://dx.doi.org/10.1142/s0218194021400076.

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Information service is always a hot topic especially when the Web is accessible anywhere. In university, lecture information is very important for students and teachers who want to take part in academic meetings. Therefore, lecture news extraction is an important and imperative task. Many open information extraction methods have been proposed, but due to the high heterogeneity of websites, this task is still a challenge. In this paper, we propose a method based on fusing multiple features to locate lecture news on the university website. These features include the linked relationship between p

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Liu, Xue Bin, Wei Zhao, Chong Ning Li, and De Ji Hu. "Research on the Information Extraction Technology of STEP." Applied Mechanics and Materials 141 (November 2011): 455–59. http://dx.doi.org/10.4028/www.scientific.net/amm.141.455.

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In order to generate STEP-NC data model in keeping with the ISO14649 protocol, extend the STEP model to the field of the CNC, the paper analyzes STEP standard and its file structure, develops the display software which can be read and display the STEP format file based on open source Open CASCADE geometric kernel by using the Visual Studio 2008 software development environment, designs one instance by using Pro/E software, and stored as a STEP format file in Pro/E environment, and then, using the software developed to read and display the STEP file. The results of this study improve the produc

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Song, Linfeng, Ante Wang, Xiaoman Pan, et al. "OpenFact: Factuality Enhanced Open Knowledge Extraction." Transactions of the Association for Computational Linguistics 11 (2023): 686–702. http://dx.doi.org/10.1162/tacl_a_00569.

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Abstract We focus on the factuality property during the extraction of an OpenIE corpus named OpenFact, which contains more than 12 million high-quality knowledge triplets. We break down the factuality property into two important aspects—expressiveness and groundedness—and we propose a comprehensive framework to handle both aspects. To enhance expressiveness, we formulate each knowledge piece in OpenFact based on a semantic frame. We also design templates, extra constraints, and adopt human efforts so that most OpenFact triplets contain enough details. For groundedness, we require the main argu

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Kakkar, D., J. Blossom, and W. Guan. "RINX: A SOLUTION FOR INFORMATION EXTRACTION FROM BIG RASTER DATASETS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W1-2022 (August 5, 2022): 245–50. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-245-2022.

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Abstract. Processing Earth observation data modelled in a time-series of raster format is critical to solving some of the most complex problems in geospatial science ranging from climate change to public health. Researchers are increasingly working with these large raster datasets that are often terabytes in size. At this scale, traditional GIS methods may fail to handle the processing, and new approaches are needed to analyse these datasets. The objective of this work is to develop methods to interactively analyse big raster datasets with the goal of most efficiently extracting vector data ov

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Gentile, Anna Lisa, Ziqi Zhang, and Fabio Ciravegna. "Early Steps Towards Web Scale Information Extraction with LODIE." AI Magazine 36, no. 1 (2015): 55–64. http://dx.doi.org/10.1609/aimag.v36i1.2567.

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Information extraction (IE) is the technique for transforming unstructured textual data into structured representation that can be understood by machines. The exponential growth of the Web generates an exceptional quantity of data for which automatic knowledge capture is essential. This work describes the methodology for web scale information extraction in the LODIE project (linked open data information extraction) and highlights results from the early experiments carried out in the initial phase of the project. LODIE aims to develop information extraction techniques able to scale at web level

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Shelmanov, A. O., D. A. Devyatkin, V. A. Isakov, and I. V. Smirnov. "Open Information Extraction from Texts: Part II. Extraction of Semantic Relationships Using Unsupervised Machine Learning." Scientific and Technical Information Processing 47, no. 6 (2020): 340–47. http://dx.doi.org/10.3103/s0147688220060076.

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Presutti, Valentina, Andrea Giovanni Nuzzolese, Sergio Consoli, Aldo Gangemi, and Recupero Diego Reforgiato. "From hyperlinks to Semantic Web properties using Open Knowledge Extraction." Semantic Web 7, no. 4 (2016): 351–78. https://doi.org/10.5281/zenodo.1204399.

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Open information extraction approaches are useful but insufficient alone for populating the Web with machine readable information as their results are not directly linkable to, and immediately reusable from, other Linked Data sources. This work proposes a novel paradigm, named Open Knowledge Extraction, and its implementation (Legalo) that performs unsupervised, open domain, and abstractive knowledge extraction from text for producing machine readable information. The implemented method is based on the hypothesis that hyperlinks (either created by humans or knowledge extraction tools) provide

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Yi, Qian, Guixuan Zhang, Jie Liu, and Shuwu Zhang. "Movie Scene Event Extraction with Graph Attention Network Based on Argument Correlation Information." Sensors 23, no. 4 (2023): 2285. http://dx.doi.org/10.3390/s23042285.

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Movie scene event extraction is a practical task in media analysis, which aims at extracting structured events from unstructured movie scripts. However, although there have been many studies regarding open domain event extraction, there have only been a few studies focusing on movie scene event extraction. Specifically aimed at instances where different argument roles have the same characteristics in a movie scene, we propose the utilization of the correlation between different argument roles, which is beneficial for both movie scene trigger extraction (trigger identification and classificatio

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Gan, Jianhou, Peng Huang, Juxiang Zhou, and Bin Wen. "Chinese open information extraction based on DBMCSS in the field of national information resources." Open Physics 16, no. 1 (2018): 568–73. http://dx.doi.org/10.1515/phys-2018-0074.

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Abstract Binary entity relationship tuples can be applied in many fields such as knowledge base construction, data mining, pattern extraction, and so on. The purpose of entity relationship mining is discovering and identifying the semantic relationship. As the relationship between entities are different from the general domain, using supervise learning methods to extract entity relationships in the field of ethnicity is difficult. After research, we find that some words can be used in the context of a sentence to describe the semantic relationship. In order to salve the existing difficulties o

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Sun, Siqi, Cheng Huang, Tiejun Wu, and Yi Shen. "SecTKG: A Knowledge Graph for Open-Source Security Tools." International Journal of Intelligent Systems 2023 (August 14, 2023): 1–22. http://dx.doi.org/10.1155/2023/4464974.

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As the complexity of cyberattacks continues to increase, multistage combination attacks have become the primary method of attack. Attackers plan and organize a series of attack steps, using various attack tools to achieve specific goals. Extracting knowledge about these tools is of great significance for both defense and tracing of attacks. We have noticed that there is a wealth of security tool-related knowledge within the open-source community, but research in this area is limited. It is challenging to achieve large-scale automated security tool information extraction. To address this, we pr

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Qi, Shanshan, Limin Zheng, and Feiyu Shang. "Dependency Parsing-based Entity Relation Extraction over Chinese Complex Text." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (2021): 1–34. http://dx.doi.org/10.1145/3450273.

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Open Relation Extraction (ORE) plays a significant role in the field of Information Extraction. It breaks the limitation that traditional relation extraction must pre-define relational types in the annotated corpus and specific domains restrictions, to realize the goal of extracting entities and the relation between entities in the open domain. However, with the increase of sentence complexity, the precision and recall of Entity Relation Extraction will be significantly reduced. To solve this problem, we present an unsupervised Clause_CORE method based on Chinese grammar and dependency parsing

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Borisova, N. "An Approach for Ontology Based Information Extraction." Information Technologies and Control 12, no. 1 (2014): 15–20. http://dx.doi.org/10.1515/itc-2015-0007.

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Abstract An approach for Ontology based Information Extraction (OBIE) from unstructured text in the Bulgarian language is presented in this paper. The presented method and algorithm provide a solution for automatic data extraction from text documents exploiting ontologies. To this end, in addition to the standard tools for processing language resources in an open source free software, a dictionary-based lemmatizer for Bulgarian has been developed and integrated. It is distributed as free software, publicly available to download and use under the GPL v3 license. Due to the specifics of inflecti

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He, Li, Qian Zhang, Jianyong Duan, and Hao Wang. "An Open-Domain Event Extraction Method Incorporating Semantic and Dependent Syntactic Information." Applied Sciences 13, no. 13 (2023): 7942. http://dx.doi.org/10.3390/app13137942.

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Abstract:

Open-domain event extraction is a fundamental task that aims to extract non-predefined types of events from news clusters. Some researchers have noticed that its performance can be enhanced by improving dependency relationships. Recently, graphical convolutional networks (GCNs) have been widely used to integrate dependency syntactic information into neural networks. However, they usually introduce noise and deteriorate the generalization. To tackle this issue, we propose using Bi-LSTM to obtain semantic representations of BERT intermediate layer features and infuse the dependent syntactic info

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