The Experts below are selected from a list of 35337 Experts worldwide ranked by ideXlab platform
Eric Nyberg - One of the best experts on this subject based on the ideXlab platform.
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leveraging Procedural Knowledge for task oriented search
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015Co-Authors: Zi Yang, Eric NybergAbstract:Many search engine users attempt to satisfy an information need by issuing multiple queries, with the expectation that each result will contribute some portion of the required information. Previous research has shown that structured or semi-structured descriptive Knowledge bases (such as Wikipedia) can be used to improve search quality and experience for general or entity-centric queries. However, such resources do not have sufficient coverage of Procedural Knowledge, i.e. what actions should be performed and what factors should be considered to achieve some goal; such Procedural Knowledge is crucial when responding to task-oriented search queries. This paper provides a first attempt to bridge the gap between two evolving research areas: development of Procedural Knowledge bases (such as wikiHow) and task-oriented search. We investigate whether task-oriented search can benefit from existing Procedural Knowledge (search task suggestion) and whether automatic Procedural Knowledge construction can benefit from users' search activities (automatic Procedural Knowledge base construction). We propose to create a three-way parallel corpus of queries, query contexts, and task descriptions, and reduce both problems to sequence labeling tasks. We propose a set of textual features and structural features to identify key search phrases from task descriptions, and then adapt similar features to extract wikiHow-style Procedural Knowledge descriptions from search queries and relevant text snippets. We compare our proposed solution with baseline algorithms, commercial search engines, and the (manually-curated) wikiHow Procedural Knowledge; experimental results show an improvement of +0.28 to +0.41 in terms of Precision@8 and mean average precision (MAP).
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SIGIR - Leveraging Procedural Knowledge for Task-oriented Search
Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15, 2015Co-Authors: Zi Yang, Eric NybergAbstract:Many search engine users attempt to satisfy an information need by issuing multiple queries, with the expectation that each result will contribute some portion of the required information. Previous research has shown that structured or semi-structured descriptive Knowledge bases (such as Wikipedia) can be used to improve search quality and experience for general or entity-centric queries. However, such resources do not have sufficient coverage of Procedural Knowledge, i.e. what actions should be performed and what factors should be considered to achieve some goal; such Procedural Knowledge is crucial when responding to task-oriented search queries. This paper provides a first attempt to bridge the gap between two evolving research areas: development of Procedural Knowledge bases (such as wikiHow) and task-oriented search. We investigate whether task-oriented search can benefit from existing Procedural Knowledge (search task suggestion) and whether automatic Procedural Knowledge construction can benefit from users' search activities (automatic Procedural Knowledge base construction). We propose to create a three-way parallel corpus of queries, query contexts, and task descriptions, and reduce both problems to sequence labeling tasks. We propose a set of textual features and structural features to identify key search phrases from task descriptions, and then adapt similar features to extract wikiHow-style Procedural Knowledge descriptions from search queries and relevant text snippets. We compare our proposed solution with baseline algorithms, commercial search engines, and the (manually-curated) wikiHow Procedural Knowledge; experimental results show an improvement of +0.28 to +0.41 in terms of Precision@8 and mean average precision (MAP).
Zi Yang - One of the best experts on this subject based on the ideXlab platform.
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leveraging Procedural Knowledge for task oriented search
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015Co-Authors: Zi Yang, Eric NybergAbstract:Many search engine users attempt to satisfy an information need by issuing multiple queries, with the expectation that each result will contribute some portion of the required information. Previous research has shown that structured or semi-structured descriptive Knowledge bases (such as Wikipedia) can be used to improve search quality and experience for general or entity-centric queries. However, such resources do not have sufficient coverage of Procedural Knowledge, i.e. what actions should be performed and what factors should be considered to achieve some goal; such Procedural Knowledge is crucial when responding to task-oriented search queries. This paper provides a first attempt to bridge the gap between two evolving research areas: development of Procedural Knowledge bases (such as wikiHow) and task-oriented search. We investigate whether task-oriented search can benefit from existing Procedural Knowledge (search task suggestion) and whether automatic Procedural Knowledge construction can benefit from users' search activities (automatic Procedural Knowledge base construction). We propose to create a three-way parallel corpus of queries, query contexts, and task descriptions, and reduce both problems to sequence labeling tasks. We propose a set of textual features and structural features to identify key search phrases from task descriptions, and then adapt similar features to extract wikiHow-style Procedural Knowledge descriptions from search queries and relevant text snippets. We compare our proposed solution with baseline algorithms, commercial search engines, and the (manually-curated) wikiHow Procedural Knowledge; experimental results show an improvement of +0.28 to +0.41 in terms of Precision@8 and mean average precision (MAP).
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SIGIR - Leveraging Procedural Knowledge for Task-oriented Search
Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15, 2015Co-Authors: Zi Yang, Eric NybergAbstract:Many search engine users attempt to satisfy an information need by issuing multiple queries, with the expectation that each result will contribute some portion of the required information. Previous research has shown that structured or semi-structured descriptive Knowledge bases (such as Wikipedia) can be used to improve search quality and experience for general or entity-centric queries. However, such resources do not have sufficient coverage of Procedural Knowledge, i.e. what actions should be performed and what factors should be considered to achieve some goal; such Procedural Knowledge is crucial when responding to task-oriented search queries. This paper provides a first attempt to bridge the gap between two evolving research areas: development of Procedural Knowledge bases (such as wikiHow) and task-oriented search. We investigate whether task-oriented search can benefit from existing Procedural Knowledge (search task suggestion) and whether automatic Procedural Knowledge construction can benefit from users' search activities (automatic Procedural Knowledge base construction). We propose to create a three-way parallel corpus of queries, query contexts, and task descriptions, and reduce both problems to sequence labeling tasks. We propose a set of textual features and structural features to identify key search phrases from task descriptions, and then adapt similar features to extract wikiHow-style Procedural Knowledge descriptions from search queries and relevant text snippets. We compare our proposed solution with baseline algorithms, commercial search engines, and the (manually-curated) wikiHow Procedural Knowledge; experimental results show an improvement of +0.28 to +0.41 in terms of Precision@8 and mean average precision (MAP).
Michael Schneider - One of the best experts on this subject based on the ideXlab platform.
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Developing Conceptual and Procedural Knowledge of Mathematics
2014Co-Authors: Bethany Rittlejohnson, Michael SchneiderAbstract:Mathematical competence rests on developing Knowledge of concepts and of procedures (i.e. conceptual and Procedural Knowledge). Although there is some variability in how these constructs are defined and measured, there is general consensus that the relations between conceptual and Procedural Knowledge are often bi-directional and iterative. The chapter reviews recent studies on the relations between conceptual and Procedural Knowledge in mathematics and highlights examples of instructional methods for supporting both types of Knowledge. It concludes with important issues to address in future research, including gathering evidence for the validity of measures of conceptual and Procedural Knowledge and specifying more comprehensive models for how conceptual and Procedural Knowledge develop over time.
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the developmental relations between conceptual and Procedural Knowledge a multimethod approach
Developmental Psychology, 2010Co-Authors: Michael Schneider, Elsbeth SternAbstract:Interactions between conceptual and Procedural Knowledge influence the development of mathematical competencies. However, after decades of research, these interrelations are still under debate, and empirical results are inconclusive. The authors point out a source of these problems. Different kinds of Knowledge and competencies only show up intertwined in behavior, making it hard to measure them validly and independently of each other. A multimethod approach was used to investigate the extent of these problems. A total of 289 fifth and sixth graders' conceptual and Procedural Knowledge about decimal fractions was measured by 4 common hypothetical measures of each kind of Knowledge. Study 1 tested whether treatments affected the 2 groups of measures in consistent ways. Study 2 assessed, across 3 measurement points, whether conceptual and Procedural Knowledge could be modeled as latent factors underlying the measures. The results reveal substantial problems with the validities of the measures, which might have been present but gone undetected in previous studies. A solution to these problems is essential for theoretical and practical progress in the field. The potential of the multimethod approach for this enterprise is discussed.
Yurniwati Yurniwati - One of the best experts on this subject based on the ideXlab platform.
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GEOMETRIC CONCEPTUAL AND Procedural Knowledge OF PROSPECTIVE TEACHERS
2019Co-Authors: Yurniwati Yurniwati, Dudung Amir SolehAbstract:This research is part of a multi-year study that aims to identify and understand the difficulties of prospective elementary school teachers on conceptual and Procedural Knowledge in geometry domain. Forty prospective teachers were involved as respondents in this study. This study uses a qualitative approach and data collected through diagnostic tests and observations. This study found that the achievement of Procedural Knowledge is higher than conceptual Knowledge. The results of the study suggest that attention needs to be paid to the conceptual and Procedural Knowledge of prospective teachers. Because to become professional teachers, they must have expertise in both Knowledge.
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Improving the Conceptual and Procedural Knowledge of Prospective Teachers through Multisensory Approach: Experience from Indonesia
JRAMathEdu (Journal of Research and Advances in Mathematics Education), 2018Co-Authors: Yurniwati YurniwatiAbstract:Ab strac t. In mathematics, there is conceptual and Procedural Knowledge. Conceptual Knowledge is about ideas or mathematics understanding but Procedural Knowledge is about procedure to solve mathematics problems. Multisensory approach involve many senses like kinaesthetic, visual and auditory to gain Knowledge. This research aims to find information about how to apply multisensory approach to improve conceptual and Procedural Knowledge of prospective teacher in Jakarta State University. This action research study used Kemmis and Taggart model and implemented in two cycles. The data were collected through questionnaires and observation sheets. Then, the data was analyzed descriptively. The research results showed that the multisensory approach can enhance the conceptual and Procedural Knowledge of the prospective teachers. The Kinaesthetic approach was implemented in hands-on activity using concrete materials while the visual using images. The concrete materials and image provide different presentation but it helped to constructed concepts and abstraction. Furthermore, the auditory approach was developed along learning activities trough discussion to produce and clarify the ideas. Keywords : Conceptual Knowledge, Procedural Knowledge, Multisensory approach
Fabio Ciravegna - One of the best experts on this subject based on the ideXlab platform.
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LREC - Automatically Extracting Procedural Knowledge from Instructional Texts using Natural Language Processing
2012Co-Authors: Ziqi Zhang, Victoria Uren, Philip Webster, Andrea Varga, Fabio CiravegnaAbstract:Procedural Knowledge is the Knowledge required to perform certain tasks, and forms an important part of expertise. A major source of Procedural Knowledge is natural language instructions. While these readable instructions have been useful learning resources for human, they are not interpretable by machines. Automatically acquiring Procedural Knowledge in machine interpretable formats from instructions has become an increasingly popular research topic due to their potential applications in process automation. However, it has been insufficiently addressed. This paper presents an approach and an implemented system to assist users to automatically acquire Procedural Knowledge in structured forms from instructions. We introduce a generic semantic representation of procedures for analysing instructions, using which natural language techniques are applied to automatically extract structured procedures from instructions. The method is evaluated in three domains to justify the generality of the proposed semantic representation as well as the effectiveness of the implemented automatic system.
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automatically extracting Procedural Knowledge from instructional texts using natural language processing
Language Resources and Evaluation, 2012Co-Authors: Ziqi Zhang, Victoria Uren, Philip Webster, Andrea Varga, Fabio CiravegnaAbstract:Procedural Knowledge is the Knowledge required to perform certain tasks, and forms an important part of expertise. A major source of Procedural Knowledge is natural language instructions. While these readable instructions have been useful learning resources for human, they are not interpretable by machines. Automatically acquiring Procedural Knowledge in machine interpretable formats from instructions has become an increasingly popular research topic due to their potential applications in process automation. However, it has been insufficiently addressed. This paper presents an approach and an implemented system to assist users to automatically acquire Procedural Knowledge in structured forms from instructions. We introduce a generic semantic representation of procedures for analysing instructions, using which natural language techniques are applied to automatically extract structured procedures from instructions. The method is evaluated in three domains to justify the generality of the proposed semantic representation as well as the effectiveness of the implemented automatic system.
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KDIR - A comprehensive solution to Procedural Knowledge acquisition using information extraction
2010Co-Authors: Ziqi Zhang, Victoria Uren, Fabio CiravegnaAbstract:Procedural Knowledge is the Knowledge required to perform certain tasks. It forms an important part of expertise, and is crucial for learning new tasks. This paper summarises existing work on Procedural Knowledge acquisition, and identifies two major challenges that remain to be solved in this field; namely, automating the acquisition process to tackle bottleneck in the formalization of Procedural Knowledge, and enabling machine understanding and manipulation of Procedural Knowledge. It is believed that recent advances in information extraction techniques can be applied compose a comprehensive solution to address these challenges. We identify specific tasks required to achieve the goal, and present detailed analyses of new research challenges and opportunities. It is expected that these analyses will interest researchers of various Knowledge management tasks, particularly Knowledge acquisition and capture.
