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1,120 results on '"Pan, Jeff Z."'

201. Semantic Cooperation and Knowledge Reuse by Using Autonomous Ontologies

Author

Zhao, Yuting, Wang, Kewen, Topor, Rodney, Pan, Jeff Z., Giunchiglia, Fausto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Aberer, Karl, editor, Choi, Key-Sun, editor, Noy, Natasha, editor, Allemang, Dean, editor, Lee, Kyung-Il, editor, Nixon, Lyndon, editor, Golbeck, Jennifer, editor, Mika, Peter, editor, Maynard, Diana, editor, Mizoguchi, Riichiro, editor, Schreiber, Guus, editor, and Cudré-Mauroux, Philippe, editor

Published
2007
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205. Querying the Semantic Web with Preferences

Author

Siberski, Wolf, Pan, Jeff Z., Thaden, Uwe, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cruz, Isabel, editor, Decker, Stefan, editor, Allemang, Dean, editor, Preist, Chris, editor, Schwabe, Daniel, editor, Mika, Peter, editor, Uschold, Mike, editor, and Aroyo, Lora M., editor

Published
2006
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206. Integrating and Querying Parallel Leaf Shape Descriptions

Author

Wang, Shenghui, Pan, Jeff Z., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cruz, Isabel, editor, Decker, Stefan, editor, Allemang, Dean, editor, Preist, Chris, editor, Schwabe, Daniel, editor, Mika, Peter, editor, Uschold, Mike, editor, and Aroyo, Lora M., editor

Published
2006
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207. A Reasoning Algorithm for pD*

Author

Li, Huiying, Wang, Yanbing, Qu, Yuzhong, Pan, Jeff Z., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Mizoguchi, Riichiro, editor, Shi, Zhongzhi, editor, and Giunchiglia, Fausto, editor

Published
2006
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208. f-SWRL: A Fuzzy Extension of SWRL

Author

Pan, Jeff Z., Stoilos, Giorgos, Stamou, Giorgos, Tzouvaras, Vassilis, Horrocks, Ian, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Spaccapietra, Stefano, editor, Aberer, Karl, editor, and Cudré-Mauroux, Philippe, editor

Published
2006
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209. Uncertainty and RuleML Rulebases: A Preliminary Report

Author

Stoilos, Giorgos, Stamou, Giorgos, Tzouvaras, Vassilis, Pan, Jeff Z., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Adi, Asaf, editor, Stoutenburg, Suzette, editor, and Tabet, Said, editor

Published
2005
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210. Ontology–Based Representation and Query Colour Descriptions from Botanical Documents

Author

Wang, Shenghui, Pan, Jeff Z., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meersman, Robert, editor, and Tari, Zahir, editor

Published
2005
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211. OWL-Eu: Adding Customised Datatypes into OWL

Author

Pan, Jeff Z., Horrocks, Ian, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gómez-Pérez, Asunción, editor, and Euzenat, Jérôme, editor

Published
2005
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212. f-SWRL: A Fuzzy Extension of SWRL

Author

Pan, Jeff Z., Stamou, Giorgos, Tzouvaras, Vassilis, Horrocks, Ian, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Duch, Włodzisław, editor, Kacprzyk, Janusz, editor, Oja, Erkki, editor, and Zadrożny, Sławomir, editor

Published
2005
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215. Exploring Provenance in a Linked Data Ecosystem

Author

Corsar, David, Edwards, Peter, Velaga, Nagendra, Nelson, John, Pan, Jeff Z., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Groth, Paul, editor, and Frew, James, editor

Published
2012
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219. The Ubiquitous Semantic Web

Author

Li, Yuan-Fang, primary, Pan, Jeff Z., additional, Krishnaswamy, Shonali, additional, Hauswirth, Manfred, additional, and Nguyen, Hai H., additional

Published
2016
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223. Aspect-based Sentiment Classification with Sequential Cross-modal Semantic Graph

Author

Huang, Yufeng, Chen, Zhuo, Zhang, Wen, Chen, Jiaoyan, Pan, Jeff Z., Yao, Zhen, Xie, Yujie, and Chen, Huajun

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Vision and Pattern Recognition (cs.CV)
Abstract

Multi-modal aspect-based sentiment classification (MABSC) is an emerging classification task that aims to classify the sentiment of a given target such as a mentioned entity in data with different modalities. In typical multi-modal data with text and image, previous approaches do not make full use of the fine-grained semantics of the image, especially in conjunction with the semantics of the text and do not fully consider modeling the relationship between fine-grained image information and target, which leads to insufficient use of image and inadequate to identify fine-grained aspects and opinions. To tackle these limitations, we propose a new framework SeqCSG including a method to construct sequential cross-modal semantic graphs and an encoder-decoder model. Specifically, we extract fine-grained information from the original image, image caption, and scene graph, and regard them as elements of the cross-modal semantic graph as well as tokens from texts. The cross-modal semantic graph is represented as a sequence with a multi-modal visible matrix indicating relationships between elements. In order to effectively utilize the cross-modal semantic graph, we propose an encoder-decoder method with a target prompt template. Experimental results show that our approach outperforms existing methods and achieves the state-of-the-art on two standard datasets MABSC. Further analysis demonstrates the effectiveness of each component and our model can implicitly learn the correlation between the target and fine-grained information of the image., Work in progress

Published
2022
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226. Introduction to the Proceedings of the 9th International Workshop on Semantic Web Enabled Software Engineering (SWESE) 2013

Author

Gröner, Gerd, Pan, Jeff Z., Zhao, Yuting, Kendall, Elisa F., Stojanovic, Ljiljana, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Lomuscio, Alessio R., editor, Nepal, Surya, editor, Patrizi, Fabio, editor, Benatallah, Boualem, editor, and Brandić, Ivona, editor

Published
2014
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233. A Lightweight Treatment of Inexact Dates

Author

Nguyen, Hai H., primary, Taylor, Stuart, additional, Webster, Gemma, additional, Jekjantuk, Nophadol, additional, Mellish, Chris, additional, Pan, Jeff Z., additional, ap Rheinallt, Tristan, additional, and Byrne, Kate, additional

Published
2015
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234. Tab2Know: Building a Knowledge Base from Tables in Scientific Papers

Author

Kruit, Benno, He, Hongyu, Urbani, Jacopo, Pan, Jeff Z., Tamma, Valentina, d’Amato, Claudia, Janowicz, Krzysztof, Fu, Bo, Polleres, Axel, Seneviratne, Oshani, Kagal, Lalana, Pan, Jeff Z., Tamma, Valentina, d’Amato, Claudia, Janowicz, Krzysztof, Fu, Bo, Polleres, Axel, Seneviratne, Oshani, Kagal, Lalana, Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands, High Performance Distributed Computing, Computer Systems, and Network Institute

Subjects
Scientific enterprise, Data labeling, FOS: Computer and information sciences, Sociology of scientific knowledge, Information retrieval, Computer science, business.industry, Computer Science - Artificial Intelligence, 010401 analytical chemistry, 020207 software engineering, 02 engineering and technology, Ontology (information science), 01 natural sciences, Pipeline (software), 0104 chemical sciences, Domain (software engineering), Artificial Intelligence (cs.AI), Knowledge base, 0202 electrical engineering, electronic engineering, information engineering, business
Abstract

Tables in scientific papers contain a wealth of valuable knowledge for the scientific enterprise. To help the many of us who frequently consult this type of knowledge, we present Tab2Know, a new end-to-end system to build a Knowledge Base (KB) from tables in scientific papers. Tab2Know addresses the challenge of automatically interpreting the tables in papers and of disambiguating the entities that they contain. To solve these problems, we propose a pipeline that employs both statistical-based classifiers and logic-based reasoning. First, our pipeline applies weakly supervised classifiers to recognize the type of tables and columns, with the help of a data labeling system and an ontology specifically designed for our purpose. Then, logic-based reasoning is used to link equivalent entities (via sameAs links) in different tables. An empirical evaluation of our approach using a corpus of papers in the Computer Science domain has returned satisfactory performance. This suggests that ours is a promising step to create a large-scale KB of scientific knowledge., 17 pages, 4 figures, conference: The Semantic Web -- ISWC 2020

Published
2020

241. Wikinegata

Author

Arnaout, Hiba, primary, Razniewski, Simon, additional, Weikum, Gerhard, additional, and Pan, Jeff Z., additional

Published
2021
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246. BANDAR: Benchmarking Snippet Generation Algorithms for (RDF) Dataset Search

Author

Wang, Xiaxia, Cheng, Gong, Pan, Jeff Z., Kharlamov, Evgeny, and Qu, Yuzhong

Abstract

The large volume of open data on the Web is expected to be reused and create value. Finding the right data to reuse is a non-trivial task addressed by the recent dataset search systems, which retrieve datasets relevant to a keyword query. An important component of such systems is snippet generation, extracting data from a retrieved dataset to exemplify its content and explain its relevance to the query. Snippet generation algorithms have emerged but were mainly evaluated by user studies. More efficient and reproducible evaluation methods are needed. To meet this challenge, in this article, we present a set of quality metrics for assessing the usefulness of a snippet from different perspectives, and we select and aggregate them into quality profiles for different stages of a dataset search process. Furthermore, we create a benchmark from thousands of collected real-world data needs and datasets, on which we apply the presented quality metrics and profiles to evaluate snippets generated by two existing algorithms and three adapted algorithms. The results, which are reproducible as they are automatically computed without human interaction, show the pros and cons of the tested algorithms and highlight directions for future research. The benchmark data is publicly available.

Published
2023
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247. Zero-Shot and Few-Shot Learning With Knowledge Graphs: A Comprehensive Survey

Author

Chen, Jiaoyan, Geng, Yuxia, Chen, Zhuo, Pan, Jeff Z., He, Yuan, Zhang, Wen, Horrocks, Ian, and Chen, Huajun

Abstract

Machine learning (ML), especially deep neural networks, has achieved great success, but many of them often rely on a number of labeled samples for supervision. As sufficient labeled training data are not always ready due to, e.g., continuously emerging prediction targets and costly sample annotation in real-world applications, ML with sample shortage is now being widely investigated. Among all these studies, many prefer to utilize auxiliary information including those in the form of knowledge graph (KG) to reduce the reliance on labeled samples. In this survey, we have comprehensively reviewed over 90 articles about KG-aware research for two major sample shortage settings—zero-shot learning (ZSL) where some classes to be predicted have no labeled samples and few-shot learning (FSL) where some classes to be predicted have only a small number of labeled samples that are available. We first introduce KGs used in ZSL and FSL as well as their construction methods and then systematically categorize and summarize KG-aware ZSL and FSL methods, dividing them into different paradigms, such as the mapping-based, the data augmentation, the propagation-based, and the optimization-based. We next present different applications, including not only KG augmented prediction tasks such as image classification, question answering, text classification, and knowledge extraction but also KG completion tasks and some typical evaluation resources for each task. We eventually discuss some challenges and open problems from different perspectives.

Published
2023
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