The Experts below are selected from a list of 27048 Experts worldwide ranked by ideXlab platform
Paul Compton - One of the best experts on this subject based on the ideXlab platform.
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similarity function recommender service using Incremental user Knowledge acquisition
International Conference on Service Oriented Computing, 2011Co-Authors: Seung Hwan Ryu, Boualem Benatallah, Hyeyoung Paik, Yang Sok Kim, Paul ComptonAbstract:Similar entity search is the task of identifying entities that most closely resemble a given entity (e.g., a person, a document, or an image). Although many techniques for estimating similarity have been proposed in the past, little work has been done on the question of which of the presented techniques are most suitable for a given similarity analysis task. Knowing the right similarity function is important as the task is highly domain- and data-dependent. In this paper, we propose a recommender service that suggests which similarity functions (e.g., edit distance or jaccard similarity) should be used for measuring the similarity between two entities. We introduce the notion of “similarity function recommendation rule” that captures user Knowledge about similarity functions and their usage contexts. We also present an Incremental Knowledge acquisition technique for building and maintaining a set of similarity function recommendation rules.
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impact of quasi expertise on Knowledge acquisition in computer vision
Image and Vision Computing New Zealand, 2009Co-Authors: Avishkar Misra, Arcot Sowmya, Paul ComptonAbstract:Ripple Down Rules (RDR)'s Incremental Knowledge acquisition provides computer vision applications with the ability to gradually adapt to the domain and circumvent some of its learning challenges. RDR use Incremental exception-based theory revision and rely on the expert to provide the rule conditions. A computer vision expert whilst understanding their significance cannot always provide accurate rule conditions using numeric attributes. This work investigates the impact of the quasiexpertise of vision experts on the structure and performance of the acquired Knowledge base. The findings provide insights into the design of features and strategies to facilitate the use of quasiexpertise for Knowledge acquisition in computer vision.
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an Incremental Knowledge acquisition method for improving duplicate invoices detection
International Conference on Data Engineering, 2009Co-Authors: Paul Compton, Boualem Benatallah, Julien J P Vayssiere, Lucio Menzel, Hartmut VoglerAbstract:Duplicate records are a major problem and duplicate invoices are a specific example of this. The detection of duplicate invoices is a critical issue for business since duplicate invoices can result in a company paying more than once for goods or services ordered. Past experience has shown that generic duplicate record detection techniques are not very useful when applied to invoices: the rate of false positives can be so high that invoice clerks are discouraged from using the system. This is because such approaches do not take the business context into account, e.g. what types of good were ordered as well as the past relationship with that vendor. In this paper, we discuss applying Ripple Down Rules (RDR), an approach for Incremental and end-user-centred Knowledge acquisition, to the problem of classifying pairs of potential duplicate invoices. We describe how we built a prototype on top of the SAP ERP product and evaluated it on a real data set that had been previously independently audited for duplicates. The preliminary results have highlighted the significant potential of this approach for assisting invoicing clerks processing potential duplicate invoices. We have observed a drop in the rate of false positives from 92% down to 18.66% when compared to traditional approaches that do not take the business context into account. We suggest that Incremental development of domain specific Knowledge may have more general application to the problem of handling duplicate records.
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an approach for Incremental Knowledge acquisition from text
Expert Systems With Applications, 2003Co-Authors: Juana Maria Ruizsanchez, Rafael Valenciagarcia, Jesualdo Tomas Fernandezbreis, Rodrigo Martinezbejar, Paul ComptonAbstract:Abstract The approach presented in this work aims to simplify Knowledge Acquisition Processes by means of the extraction of some types of Knowledge directly from natural language texts. This approach uses both a morphologic analyzer and a user-centered, Incremental Knowledge methodology with the purpose of achieving a language independent system. The Knowledge acquired from text is represented by means of ontologies.
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formal concept analysis for domain specific document retrieval systems
Australian Joint Conference on Artificial Intelligence, 2001Co-Authors: Mihye Kim, Paul ComptonAbstract:Domain-specific information retrieval normally depends on general search engines, or systems which support browsing using handcrafted organisation of documents, but such systems are costly to build and maintain. An alternative approach for specialised domains is to build a retrieval system Incrementally and dynamically by allowing users to evolve their own organisation of documents and to assist them in ensuring improvement of the system's performance as it evolves. This paper describes a browsing mechanism for such a system based on the concept lattice of Formal Concept Analysis (FCA) in cooperation with Incremental Knowledge acquisition mechanisms. Our experience with a prototype suggests that a browsing scheme for a specific domain can be able to be collaboratively created and maintained by multiple users over time. It also appears that the concept lattice of FCA is a useful way of supporting the flexible open management of documents required by individuals, small communities or in specialised domains.
Wenbin Qian - One of the best experts on this subject based on the ideXlab platform.
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Knowledge Acquisition Approach Based on Incremental Objects From Data With Missing Values
IEEE Access, 2019Co-Authors: Wenbin QianAbstract:Knowledge acquisition is the process of extracting useful Knowledge from data sets to analyze data in areas of data mining and Knowledge discovery. Most current Knowledge acquisition work mainly focuses on static data. However, due to the dynamic characteristics of data, the objects grow at an unprecedented rate in real-world data sets. The Incremental objects with a dynamic environment significantly affect Knowledge updating. To maintain the effectiveness of Knowledge from the dynamic data, it is necessary to update the Knowledge timely. So far, there are relatively few studies on Knowledge acquisition for the data with missing feature values, i.e., incomplete data. To handle with this issue, an Incremental updating manner of the accuracy matrix and coverage matrix are first proposed on the basis of the computations of the tolerance classes in incomplete data, which plays an important role in the Knowledge acquisition process. Then, an Incremental Knowledge acquisition algorithm is proposed when some new objects added to the data with missing values. Finally, some numerical experiments are conducted to evaluate the efficiency of the proposed algorithm.
Rodrigo Martinezbejar - One of the best experts on this subject based on the ideXlab platform.
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ontology learning from biomedical natural language documents using umls
Expert Systems With Applications, 2011Co-Authors: Juana Maria Ruizmartinez, Rafael Valenciagarcia, Jesualdo Tomas Fernandezbreis, Francisco Garciasanchez, Rodrigo MartinezbejarAbstract:The generation of new Knowledge is continuous in biomedical domains, thus biomedical literature is becoming harder to understand. Ontologies provide vocabulary standardization, so they can be helpful to facilitate the understanding of biomedical texts. In this work, a methodology for building biomedical ontologies from texts is presented. This approach relies on natural language processing and Incremental Knowledge acquisition techniques to obtain the relevant concepts and relations to be included in an OWL ontology. Additionally, we provide an algorithm to connect the isolated concepts regions in the ontology using UMLS. We also discuss in this paper the experiment carried out to validate our approach and its positive results in terms of performance and scalability.
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an approach for Incremental Knowledge acquisition from text
Expert Systems With Applications, 2003Co-Authors: Juana Maria Ruizsanchez, Rafael Valenciagarcia, Jesualdo Tomas Fernandezbreis, Rodrigo Martinezbejar, Paul ComptonAbstract:Abstract The approach presented in this work aims to simplify Knowledge Acquisition Processes by means of the extraction of some types of Knowledge directly from natural language texts. This approach uses both a morphologic analyzer and a user-centered, Incremental Knowledge methodology with the purpose of achieving a language independent system. The Knowledge acquired from text is represented by means of ontologies.
Jesualdo Tomas Fernandezbreis - One of the best experts on this subject based on the ideXlab platform.
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ontology learning from biomedical natural language documents using umls
Expert Systems With Applications, 2011Co-Authors: Juana Maria Ruizmartinez, Rafael Valenciagarcia, Jesualdo Tomas Fernandezbreis, Francisco Garciasanchez, Rodrigo MartinezbejarAbstract:The generation of new Knowledge is continuous in biomedical domains, thus biomedical literature is becoming harder to understand. Ontologies provide vocabulary standardization, so they can be helpful to facilitate the understanding of biomedical texts. In this work, a methodology for building biomedical ontologies from texts is presented. This approach relies on natural language processing and Incremental Knowledge acquisition techniques to obtain the relevant concepts and relations to be included in an OWL ontology. Additionally, we provide an algorithm to connect the isolated concepts regions in the ontology using UMLS. We also discuss in this paper the experiment carried out to validate our approach and its positive results in terms of performance and scalability.
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an approach for Incremental Knowledge acquisition from text
Expert Systems With Applications, 2003Co-Authors: Juana Maria Ruizsanchez, Rafael Valenciagarcia, Jesualdo Tomas Fernandezbreis, Rodrigo Martinezbejar, Paul ComptonAbstract:Abstract The approach presented in this work aims to simplify Knowledge Acquisition Processes by means of the extraction of some types of Knowledge directly from natural language texts. This approach uses both a morphologic analyzer and a user-centered, Incremental Knowledge methodology with the purpose of achieving a language independent system. The Knowledge acquired from text is represented by means of ontologies.
Rafael Valenciagarcia - One of the best experts on this subject based on the ideXlab platform.
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ontology learning from biomedical natural language documents using umls
Expert Systems With Applications, 2011Co-Authors: Juana Maria Ruizmartinez, Rafael Valenciagarcia, Jesualdo Tomas Fernandezbreis, Francisco Garciasanchez, Rodrigo MartinezbejarAbstract:The generation of new Knowledge is continuous in biomedical domains, thus biomedical literature is becoming harder to understand. Ontologies provide vocabulary standardization, so they can be helpful to facilitate the understanding of biomedical texts. In this work, a methodology for building biomedical ontologies from texts is presented. This approach relies on natural language processing and Incremental Knowledge acquisition techniques to obtain the relevant concepts and relations to be included in an OWL ontology. Additionally, we provide an algorithm to connect the isolated concepts regions in the ontology using UMLS. We also discuss in this paper the experiment carried out to validate our approach and its positive results in terms of performance and scalability.
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an approach for Incremental Knowledge acquisition from text
Expert Systems With Applications, 2003Co-Authors: Juana Maria Ruizsanchez, Rafael Valenciagarcia, Jesualdo Tomas Fernandezbreis, Rodrigo Martinezbejar, Paul ComptonAbstract:Abstract The approach presented in this work aims to simplify Knowledge Acquisition Processes by means of the extraction of some types of Knowledge directly from natural language texts. This approach uses both a morphologic analyzer and a user-centered, Incremental Knowledge methodology with the purpose of achieving a language independent system. The Knowledge acquired from text is represented by means of ontologies.
