The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Roberto Carlos Dos Santos Pacheco - One of the best experts on this subject based on the ideXlab platform.
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Relation discovery from web data for competency management
Web Intelligence and Agent Systems: An International Journal, 2007Co-Authors: Alexandre Leopoldo Goncalves, Victoria S. Uren, Roberto Carlos Dos Santos Pacheco, Enrico Motta, Marc Eisenstadt, Dawei SongAbstract:In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (Community Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.
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Community Relation Discovery by Named Entities
2007 International Conference on Machine Learning and Cybernetics, 2007Co-Authors: Jianhan Zhu, Alexandre Leopoldo Goncalves, Roberto Carlos Dos Santos Pacheco, Enrico Motta, Dawei Song, Victoria Uren, Stefan RügerAbstract:Discovering who works with whom, on which projects and with which customers is a key task in knowledge management. Although most organizations keep models of organizational structures, these models do not necessarily accurately reflect the reality on the ground. In this paper we present a text mining method called CORDER which first recognizes named entities (NEs) of various types from Web pages, and then discovers Relations from a target NE to other NEs which co-occur with it. We evaluated the method on our departmental Website. We used the CORDER method to first find related NEs of four types (organizations, people, projects, and research areas) from Web pages on the Website and then rank them according to their co-occurrence with each of the people in our department. 20 representative people were selected and each of them was presented with ranked lists of each type of NE. Each person specified whether these NEs were related to him/her and changed or confirmed their rankings. Our results indicate that the method can find the NEs with which these people are closely related and provide accurate rankings.
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Web Intelligence - Mining Web Data for Competency Management
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 2005Co-Authors: Alexandre Leopoldo Goncalves, Victoria S. Uren, Enrico Motta, Roberto Carlos Dos Santos PachecoAbstract:We present CORDER (Community Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.
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K-CAP - CORDER: Community Relation discovery by named entity recognition
Proceedings of the 3rd international conference on Knowledge capture - K-CAP '05, 2005Co-Authors: Jianhan Zhu, Alexandre Leopoldo Goncalves, Enrico Motta, Victoria Uren, Roberto Carlos Dos Santos PachecoAbstract:We present a text mining method called CORDER [4] which discovers social networks from an organization's documents. CORDER finds Relations between a target named entity and other named entities which occur with it.
Alexandre Leopoldo Goncalves - One of the best experts on this subject based on the ideXlab platform.
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Relation discovery from web data for competency management
Web Intelligence and Agent Systems: An International Journal, 2007Co-Authors: Alexandre Leopoldo Goncalves, Victoria S. Uren, Roberto Carlos Dos Santos Pacheco, Enrico Motta, Marc Eisenstadt, Dawei SongAbstract:In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (Community Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.
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Community Relation Discovery by Named Entities
2007 International Conference on Machine Learning and Cybernetics, 2007Co-Authors: Jianhan Zhu, Alexandre Leopoldo Goncalves, Roberto Carlos Dos Santos Pacheco, Enrico Motta, Dawei Song, Victoria Uren, Stefan RügerAbstract:Discovering who works with whom, on which projects and with which customers is a key task in knowledge management. Although most organizations keep models of organizational structures, these models do not necessarily accurately reflect the reality on the ground. In this paper we present a text mining method called CORDER which first recognizes named entities (NEs) of various types from Web pages, and then discovers Relations from a target NE to other NEs which co-occur with it. We evaluated the method on our departmental Website. We used the CORDER method to first find related NEs of four types (organizations, people, projects, and research areas) from Web pages on the Website and then rank them according to their co-occurrence with each of the people in our department. 20 representative people were selected and each of them was presented with ranked lists of each type of NE. Each person specified whether these NEs were related to him/her and changed or confirmed their rankings. Our results indicate that the method can find the NEs with which these people are closely related and provide accurate rankings.
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Web Intelligence - Mining Web Data for Competency Management
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 2005Co-Authors: Alexandre Leopoldo Goncalves, Victoria S. Uren, Enrico Motta, Roberto Carlos Dos Santos PachecoAbstract:We present CORDER (Community Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.
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K-CAP - CORDER: Community Relation discovery by named entity recognition
Proceedings of the 3rd international conference on Knowledge capture - K-CAP '05, 2005Co-Authors: Jianhan Zhu, Alexandre Leopoldo Goncalves, Enrico Motta, Victoria Uren, Roberto Carlos Dos Santos PachecoAbstract:We present a text mining method called CORDER [4] which discovers social networks from an organization's documents. CORDER finds Relations between a target named entity and other named entities which occur with it.
Enrico Motta - One of the best experts on this subject based on the ideXlab platform.
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Relation discovery from web data for competency management
Web Intelligence and Agent Systems: An International Journal, 2007Co-Authors: Alexandre Leopoldo Goncalves, Victoria S. Uren, Roberto Carlos Dos Santos Pacheco, Enrico Motta, Marc Eisenstadt, Dawei SongAbstract:In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (Community Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.
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Community Relation Discovery by Named Entities
2007 International Conference on Machine Learning and Cybernetics, 2007Co-Authors: Jianhan Zhu, Alexandre Leopoldo Goncalves, Roberto Carlos Dos Santos Pacheco, Enrico Motta, Dawei Song, Victoria Uren, Stefan RügerAbstract:Discovering who works with whom, on which projects and with which customers is a key task in knowledge management. Although most organizations keep models of organizational structures, these models do not necessarily accurately reflect the reality on the ground. In this paper we present a text mining method called CORDER which first recognizes named entities (NEs) of various types from Web pages, and then discovers Relations from a target NE to other NEs which co-occur with it. We evaluated the method on our departmental Website. We used the CORDER method to first find related NEs of four types (organizations, people, projects, and research areas) from Web pages on the Website and then rank them according to their co-occurrence with each of the people in our department. 20 representative people were selected and each of them was presented with ranked lists of each type of NE. Each person specified whether these NEs were related to him/her and changed or confirmed their rankings. Our results indicate that the method can find the NEs with which these people are closely related and provide accurate rankings.
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Web Intelligence - Mining Web Data for Competency Management
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 2005Co-Authors: Alexandre Leopoldo Goncalves, Victoria S. Uren, Enrico Motta, Roberto Carlos Dos Santos PachecoAbstract:We present CORDER (Community Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.
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K-CAP - CORDER: Community Relation discovery by named entity recognition
Proceedings of the 3rd international conference on Knowledge capture - K-CAP '05, 2005Co-Authors: Jianhan Zhu, Alexandre Leopoldo Goncalves, Enrico Motta, Victoria Uren, Roberto Carlos Dos Santos PachecoAbstract:We present a text mining method called CORDER [4] which discovers social networks from an organization's documents. CORDER finds Relations between a target named entity and other named entities which occur with it.
Khorshed Alam - One of the best experts on this subject based on the ideXlab platform.
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Corporate minerals and Community development dilemma in the Surat Resource Region, Australia: implications for resource development planning
Environmental Science & Policy, 2017Co-Authors: Michael Odei Erdiaw-kwasie, Khorshed AlamAbstract:Abstract While the idea that agricultural farmlands and natural resources exploration can co-exist is rhetorically sound, emerged and rising realities question this claim. Past studies, particularly ones taking a corporate–Community Relation stance, have largely explored these emerging realities. This paper contributes an alternate perspective to the debate by presenting a procedural viewpoint on the subject in the light of empirical highlights. The Surat Resource Region in Queensland, Australia, which is noted for its rich agricultural farmlands and natural resources endowment, is considered an appropriate case region for the study. Both quantitative and qualitative empirical findings show that empowerment, cultural adhocracy, and value-led partnership are the missing procedural elements that need to be enforced and incorporated into resource development planning strategies. The study offers a strategy framework for integrative resource development planning research, whose policy and practical application are promising. Study findings aim to increase the robustness of resource development strategies through enhanced understanding of the planning and management processes.
Victoria S. Uren - One of the best experts on this subject based on the ideXlab platform.
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Relation discovery from web data for competency management
Web Intelligence and Agent Systems: An International Journal, 2007Co-Authors: Alexandre Leopoldo Goncalves, Victoria S. Uren, Roberto Carlos Dos Santos Pacheco, Enrico Motta, Marc Eisenstadt, Dawei SongAbstract:In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (Community Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.
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Web Intelligence - Mining Web Data for Competency Management
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 2005Co-Authors: Alexandre Leopoldo Goncalves, Victoria S. Uren, Enrico Motta, Roberto Carlos Dos Santos PachecoAbstract:We present CORDER (Community Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.