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On the Need to Bootstrap Ontology Learning with Extraction Grammar Learning [chapter]

Georgios Paliouras
2005 Lecture Notes in Computer Science  
, information extraction, grammar induction and ontology enrichment is presented.  ...  The paper concludes with a number of interesting issues that need to be addressed in order to realize the advocated bootstrapping process.  ...  Acknowledgments This paper includes ideas and work that are not solely of the author. A number of current and past SKEL members have been involved in the presented work.  ... 
doi:10.1007/11524564_8 fatcat:444chquckvck7n6mluzi4gaje4

Inductive Logic Programming in an Agent System forOntological Relation Extraction

M. D. S. Seneviratne, D. N. Ranasinghe
2011 International Journal of Machine Learning and Computing  
In the multi agent system one agent makes use of Inductive Logic Programming for the rule learning process while another agent is expected to use the learnt rules to identify new relations as well as extract  ...  The learning capability of agents is exploited to train an agent to learn extraction rules from the syntactic structure of natural language sentences.  ...  Karunananda of Faculty of Information Technology, University of Moratuwa for his valuable contribution.  ... 
doi:10.7763/ijmlc.2011.v1.51 fatcat:gryji7aupfezdcdyknh6wnbpfi

Ontology research and development. Part 1 - a review of ontology generation

Ying Ding, Schubert Foo
2002 Journal of information science  
However, relation extraction is a very complex and difficult issue to resolve and it has turned out to be the main impediment to ontology learning and applicability.  ...  Through this survey, we have identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data  ...  Then, a domain-specific and a general corpus of texts were used to remove concepts that were not domain-specific through some heuristic rules.  ... 
doi:10.1177/016555150202800204 fatcat:4m54xekncrcgtjqrxnhnngpib4

Ontology research and development. Part 1: A review of ontology generation

Y. Ding, S. Foo
2002 Journal of Information Science  
However, relation extraction is a very complex and difficult issue to resolve and it has turned out to be the main impediment to ontology learning and applicability.  ...  Through this survey, we have identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data  ...  Then, a domain-specific and a general corpus of texts were used to remove concepts that were not domain-specific through some heuristic rules.  ... 
doi:10.1177/0165551024234020 fatcat:rmhtay6t75d2vm2xbpu3wbiixe

Mining Web Sites Using Wrapper Induction, Named Entities, and Post-processing [chapter]

Georgios Sigletos, Georgios Paliouras, Constantine D. Spyropoulos, Michalis Hatzopoulos
2004 Lecture Notes in Computer Science  
We introduce the idea of post-processing the extraction results for resolving ambiguous facts and improve the overall extraction performance.  ...  This paper presents a novel method for extracting information from collections of Web pages across different sites.  ...  Learning fact transition probabilities In many extraction domains, some facts appear in an almost fixed order within each page.  ... 
doi:10.1007/978-3-540-30123-3_6 fatcat:rbggyoy2g5eonczb7aprgdxury

10.1162/153244304322972685

2000 Applied Physics Letters  
Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document.  ...  We present an algorithm, RAPIER, that uses pairs of sample documents and filled templates to induce pattern-match rules that directly extract fillers for the slots in the template.  ...  Thanks to the anonymous reviewers for comments on earlier drafts of this paper.  ... 
doi:10.1162/153244304322972685 fatcat:f2mbjnvhwra7latl2olzokvtmm

Use of Agent Technology in Relation Extraction for Ontology Construction

M. D. S. Seneviratne, N. R. Ranasinghe
2012 Journal of clean energy technologies  
In the multi agent system one agent makes use of Inductive Logic Programming for the rule learning process while another agent is expected to use the learnt rules to identify new relations as well as extract  ...  The learning capability of agents is exploited to train an agent to learn extraction rules from the syntactic structure of natural language sentences.  ...  Karunananda of Faculty of Information Technology, University of Moratuwa for his valuable contribution.  ... 
doi:10.7763/ijcte.2012.v4.600 fatcat:3wfwuvz2erd3rnotisxeskpt2e

Adaptive Information Extraction: Core Technologies for Information Agents [chapter]

Nicholas Kushmerick, Bernd Thomas
2003 Lecture Notes in Computer Science  
This chapter focuses on the use of machine learning to enable adaptive information extraction systems that automatically learn extraction rules from training data in order to scale with the number of sources  ...  We therefore focus specifically on information extraction, rather than tangential (albeit important) issues, such as how agents can discover relevant sources or verify the authenticity of the retrieved  ...  The result is an accurate extraction algorithm that is competitive with other state-ofthe-art approaches in a variety of free-text domains, and superior in many.  ... 
doi:10.1007/3-540-36561-3_4 fatcat:peutiprqsnd2re3nuycsvwquxu

Interactive Inductive Learning: Application in Domain of Education

Ilze Birzniece, Marite Kirikova
2011 Scientific Journal of Riga Technical University Computer Sciences  
Interactive Inductive Learning: Application in Domain of Education globalization presents an opportunity to obtain education from several education providers by using different study exchange programmes  ...  This possibility, in turn, creates the need to compare available study courses in foreign institutions to courses on the curriculum of the institution which issues the degree.  ...  ACKNOWLEDGEMENTS This work has been supported by the European Social Fund within the project "Support for the implementation of doctoral studies at Riga Technical University".  ... 
doi:10.2478/v10143-011-0022-5 fatcat:j46xfsh5efektgn6dldj3shjpa

Automatic rule learning exploiting morphological features for named entity recognition in Turkish

Serhan Tatar, Ilyas Cicekli
2011 Journal of information science  
In this paper, we describe an automatic rule learning method that exploits different features of the input text to identify the named entities located in the natural language texts.  ...  Named entity recognition (NER) is one of the basic tasks in automatic extraction of information from natural language texts.  ...  In future work, we are planning to improve the developed rule learning method by introducing correction rules before applying it to the new IE tasks.  ... 
doi:10.1177/0165551511398573 fatcat:jsppv6n6wvegnjvxqj4a6t7pte

Active Learning with Strong and Weak Views: A Case Study on Wrapper Induction

Ion Muslea, Steven Minton, Craig A. Knoblock
2003 International Joint Conference on Artificial Intelligence  
In a case study on 33 wrapper induction tasks, our algorithm requires significantly fewer labeled examples than existing state-of-the-art approaches.  ...  Aggressive Co-Testing uses the weak views both for detecting the most informative examples in the domain and for improving the accuracy of the predictions.  ...  C-0197 and F30602-00-1-0504, in part by the Air Force Office of Scientific Research under grant numbers F49620-01 -1 -0053 and F49620-02-1 -0270, in part by the United States Air Force under contract number  ... 
dblp:conf/ijcai/MusleaMK03 fatcat:37pd4v6i2fe7lit2llguiyvn4q

Inductive programming meets the real world

Sumit Gulwani, José Hernández-Orallo, Emanuel Kitzelmann, Stephen H. Muggleton, Ute Schmid, Benjamin Zorn
2015 Communications of the ACM  
. • Learning from few examples is possible because users and systems share the same background knowledge. • Search is guided by domain-specific languages and the use of higher-order knowledge.  ...  Inductive Programming has a long research tradition and recent developments demonstrate it can liberate users from many tasks of this kind.  ...  Addressing the issue of robustness to such noise may be best done in a domain-specific manner.  ... 
doi:10.1145/2736282 fatcat:oods62zxrnfejeql6d3efmx52a

Intelligent Text Processing Techniques for Textual-Profile Gene Characterization [chapter]

Floriana Esposito, Marenglen Biba, Stefano Ferilli
2010 Lecture Notes in Computer Science  
Extracting prior knowledge from text-based genomic information sources is essential in order to reduce the list of potential candidate genes to be then further analyzed in laboratory.  ...  keyword extraction methods and exploitation of lexical knowledge bases for semantic text processing.  ...  First, to the best of our knowledge, machine learning in the form of rule-induction has not been used before for text-based gene prioritization or clustering.  ... 
doi:10.1007/978-3-642-14571-1_3 fatcat:mh565fttxzhsbca33hv3fcxtwu

Active Learning with Multiple Views

I. Muslea, S. Minton, C. A. Knoblock
2006 The Journal of Artificial Intelligence Research  
We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept.  ...  Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain.  ...  NBCHD030010, and in part by the Air Force Office of Scientific Research under grant number FA9550-04-1-0105.  ... 
doi:10.1613/jair.2005 fatcat:b735il7yhraubnejf5bpua3uh4

Building acceptable classification models for financial engineering applications

David Martens
2008 SIGKDD Explorations  
Active learning implies the focus on apparent problem areas, which for rule induction techniques are in the regions close to the SVM decision boundary where most of the noise is found.  ...  With these lessons learnt, a new methodology is developed for SVM rule extraction: active learning based approach (ALBA) [6] .  ...  Active learning implies the focus on apparent problem areas, which for rule induction techniques are in the regions close to the SVM decision boundary where most of the noise is found.  ... 
doi:10.1145/1540276.1540285 fatcat:j2uuk6oz7jabxnwtyk5fash23q
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