The Experts below are selected from a list of 2907 Experts worldwide ranked by ideXlab platform
Lindley Darden - One of the best experts on this subject based on the ideXlab platform.
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Strategies for Discovering Mechanisms: Schema Instantiation, Modular Subassembly, Forward/Backward Chaining
Philosophy of Science, 2002Co-Authors: Lindley DardenAbstract:Abstract Discovery proceeds in stages of construction, evaluation, and revision. Each of these stages is constrained by what is known or conjectured about what is being discovered. A new characterization of mechanism aids in specifying what is to be discovered when a mechanism is sought. Guidance in discovering mechanisms may be provided by the reasoning strategies of schema instantiation, modular subassembly, and forward/Backward Chaining. Examples are found in mechanisms in molecular biology, biochemistry, immunology, and evolutionary biology.
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strategies for discovering mechanisms schema instantiation modular subassembly forward Backward Chaining
Philosophy of Science, 2002Co-Authors: Lindley DardenAbstract:Abstract Discovery proceeds in stages of construction, evaluation, and revision. Each of these stages is constrained by what is known or conjectured about what is being discovered. A new characterization of mechanism aids in specifying what is to be discovered when a mechanism is sought. Guidance in discovering mechanisms may be provided by the reasoning strategies of schema instantiation, modular subassembly, and forward/Backward Chaining. Examples are found in mechanisms in molecular biology, biochemistry, immunology, and evolutionary biology.
Samsudin Samsudin - One of the best experts on this subject based on the ideXlab platform.
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sistem pakar diagnosa dampak penggunaan softlens menggunakan metode Backward Chaining
Journal of Biomedical Informatics, 2015Co-Authors: Nurmala Mukhtar, Samsudin SamsudinAbstract:Softlens adalah sejenis lensa yang dibuat dari bahan yang bersifat “lunakâ€, yaitu silicon hydrogen. Penggunaan softlens dalam jangka waktu lama dapat berpotensi menyebabkan iritasi mata, mata merah dan infeksi. Untuk itu diperlukan sebuah sistem pakar untuk membantu mendiagnosa dampak penggunaan softlens. Pembangunan sistem pakar diagnosa dampak penggunaan softlens ini menggunakan metode Backward Chaining atau runut balik. Metode runut balik bekerja dengan cara menentukan penyakit yang diderita oleh pengguna softlens kemudian akan dijabarkan sebab-sebab penyakit tersebut. Dari hasil penelitian dapat disimpulkan bahwa sistem pakar ini mempermudah pengguna soflens untuk melakukan diagnosa dampak penggunaan softlens berdasarkan gejala yang dialami, dan mengetahui cara penanggulangannya.
Rosmala Dwi - One of the best experts on this subject based on the ideXlab platform.
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metode Backward Chaining untuk diagnosa penyebab stroke pada pasien penderita
IEEE Intelligent Systems, 2018Co-Authors: Rosmala DwiAbstract:Backward Chaining merupakan metode yang digunakan di inference engiene untuk mendiagnosa 9 penyakit yang berpotensi menjadi penyebab stroke dengan melengkapi rule-rule yang akan digunakan didalam pembuatan pohon keputusan. Pohon keputusan ini akan ditelusuri dengan tehnik breadth-first search dimana node pada gejala penyakit yang ditunjukan oleh penderita akan dihubungkan dengan ars (busur) dengan menelusuri seluruh node yang ada sehingga didapatkan kesimpulan yang diinginkan. Data yang digunakan untuk membangun basis pengetahuan adalah data yang dikumpulkan dengan melakuan observasi langsung diklinik rehabilitasi pemulihan para penderita stroke , melakukan wawancara dengan beberapa orang dokter penyakit dalam dan penyakit saraf. Selain itu juga penulis menyiapkan angket yang diberikan kepada para penderita stroke untuk dapat mereka isi. Dengan penggunaan metode ini diharapkan dapat membatu dalam melakukan analisa untuk mendiagnosa yang berupa informasi penyakit penyebab penyakit stroke sehingga dapat segera dilakukan pencegahan dan pengobatannya
Hui Shi - One of the best experts on this subject based on the ideXlab platform.
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Evaluating an optimized Backward Chaining ontology reasoning system with innovative custom rules
Information Discovery and Delivery, 2018Co-Authors: Hui Shi, Dazhi Chong, Gongjun YanAbstract:Purpose Semantic Web is an extension of the World Wide Web by tagging content with “meaning”. In general, question answering systems based on semantic Web face a number of difficult issues. This paper aims to design an experimental environment with custom rules and scalable data sets and evaluate the performance of a proposed optimized Backward Chaining ontology reasoning system. This study also compares the experimental results with other ontology reasoning systems to show the performance and scalability of this ontology reasoning system. Design/methodology/approach The authors proposed a semantic question answering system. This system has been built using ontological knowledge base including optimized Backward Chaining ontology reasoning system and custom rules. With custom rules, the proposed semantic question answering system will be able to answer questions that contain qualitative descriptors such as “groundbreaking” resesarch and “tenurable at university x”. Scalability has been one of the difficult issues faced by an optimized Backward Chaining ontology reasoning system and semantic question answering system. To evaluate the proposed ontology reasoning system, first, the authors design a number of innovative custom rule sets and corresponding query sets. The innovative custom rule sets and query sets will contribute to the future research on evaluating ontology reasoning systems as well. Then they design an experimental environment including ontologies and scalable data sets and metrics. Furthermore, they evaluate the performance of the proposed optimized Backward Chaining reasoning system on supporting custom rules. The evaluation results have been compared with other ontology reasoning systems as well. Findings The proposed innovative custom rules and query sets can be effectively employed for evaluating ontology reasoning systems. The evaluation results show that the scalability of the proposed Backward Chaining ontology reasoning system is better than in-memory reasoning systems. The proposed semantic question answering system can be integrated in sematic Web applications to solve scalability issues. For light weight applications, such as mobile applications, in-memory reasoning systems will be a better choice. Originality/value This paper fulfils an identified need for a study on evaluating an ontology reasoning system on supporting custom rules with and without external storage.
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Backward Chaining ontology reasoning systems with custom rules
The Web Conference, 2016Co-Authors: Hui Shi, Kurt Maly, Dazhi Chong, Gongjun YanAbstract:In the semantic web, content is tagged with "meaning" or "semantics" to facilitate machine processing and web searching. In general, question answering systems that are built on top of reasoning and inference face a number of difficult issues. In this paper, we analyze scalability issues faced by a question answering system used by a knowledge base with science information that has been harvested from the web. Using this system, we will be able to answer questions that contain qualitative descriptors such as "groundbreaking", "top researcher", and "tenurable at university x". This question answering system has been built using ontologies, reasoning systems and custom based rules for the reasoning system. Furthermore, we evaluated the performance of our optimized Backward Chaining engine on supporting custom rules and designed the experimental environment including scalable datasets, rule sets, query sets and metrics and compared the experimental results with other in-memory ontology reasoning systems. The results show that our developed Backward Chaining ontology reasoning system has better scalability than in-memory reasoning systems.
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WWW (Companion Volume) - Backward Chaining Ontology Reasoning Systems with Custom Rules
Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion, 2016Co-Authors: Hui Shi, Kurt Maly, Dazhi Chong, Gongjun YanAbstract:In the semantic web, content is tagged with "meaning" or "semantics" to facilitate machine processing and web searching. In general, question answering systems that are built on top of reasoning and inference face a number of difficult issues. In this paper, we analyze scalability issues faced by a question answering system used by a knowledge base with science information that has been harvested from the web. Using this system, we will be able to answer questions that contain qualitative descriptors such as "groundbreaking", "top researcher", and "tenurable at university x". This question answering system has been built using ontologies, reasoning systems and custom based rules for the reasoning system. Furthermore, we evaluated the performance of our optimized Backward Chaining engine on supporting custom rules and designed the experimental environment including scalable datasets, rule sets, query sets and metrics and compared the experimental results with other in-memory ontology reasoning systems. The results show that our developed Backward Chaining ontology reasoning system has better scalability than in-memory reasoning systems.
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optimized Backward Chaining reasoning system for a semantic web
International Conference on Web Intelligence Mining and Semantics, 2014Co-Authors: Hui Shi, Kurt Maly, Steven J ZeilAbstract:In this paper we consider knowledge bases that organize information using ontologies. Specifically, we investigate reasoning over a semantic web where the underlying knowledgebase covers linked data about science research that are being harvested from the Web and are supplemented and edited by community members. In the semantic web over which we want to reason, frequent changes occur in the underlying knowledge base, and less frequent changes occur in the underlying ontology or the rule set that governs the reasoning. Queries may be composed of mixtures of clauses answerable directly by access to the knowledge base or indirectly via reasoning applied to that base. Two common methods of reasoning over a knowledge base using first order logic are forward Chaining and Backward Chaining. Forward Chaining is suitable for frequent computation of answers with data that are relatively static whereas Backward Chaining is suitable when frequent changes occur in the underlying knowledge base. We introduce new optimization techniques to the Backward-Chaining algorithm. We show that these techniques together with the query-optimization reported on earlier, will allow us to actually outperform forward-Chaining reasoners in scenarios where the knowledge base is subject to frequent change.
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WIMS - Optimized Backward Chaining Reasoning System for a Semantic Web
Proceedings of the 4th International Conference on Web Intelligence Mining and Semantics (WIMS14) - WIMS '14, 2014Co-Authors: Hui Shi, Kurt Maly, Steven J ZeilAbstract:In this paper we consider knowledge bases that organize information using ontologies. Specifically, we investigate reasoning over a semantic web where the underlying knowledgebase covers linked data about science research that are being harvested from the Web and are supplemented and edited by community members. In the semantic web over which we want to reason, frequent changes occur in the underlying knowledge base, and less frequent changes occur in the underlying ontology or the rule set that governs the reasoning. Queries may be composed of mixtures of clauses answerable directly by access to the knowledge base or indirectly via reasoning applied to that base. Two common methods of reasoning over a knowledge base using first order logic are forward Chaining and Backward Chaining. Forward Chaining is suitable for frequent computation of answers with data that are relatively static whereas Backward Chaining is suitable when frequent changes occur in the underlying knowledge base. We introduce new optimization techniques to the Backward-Chaining algorithm. We show that these techniques together with the query-optimization reported on earlier, will allow us to actually outperform forward-Chaining reasoners in scenarios where the knowledge base is subject to frequent change.
Nurmala Mukhtar - One of the best experts on this subject based on the ideXlab platform.
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sistem pakar diagnosa dampak penggunaan softlens menggunakan metode Backward Chaining
Journal of Biomedical Informatics, 2015Co-Authors: Nurmala Mukhtar, Samsudin SamsudinAbstract:Softlens adalah sejenis lensa yang dibuat dari bahan yang bersifat “lunakâ€, yaitu silicon hydrogen. Penggunaan softlens dalam jangka waktu lama dapat berpotensi menyebabkan iritasi mata, mata merah dan infeksi. Untuk itu diperlukan sebuah sistem pakar untuk membantu mendiagnosa dampak penggunaan softlens. Pembangunan sistem pakar diagnosa dampak penggunaan softlens ini menggunakan metode Backward Chaining atau runut balik. Metode runut balik bekerja dengan cara menentukan penyakit yang diderita oleh pengguna softlens kemudian akan dijabarkan sebab-sebab penyakit tersebut. Dari hasil penelitian dapat disimpulkan bahwa sistem pakar ini mempermudah pengguna soflens untuk melakukan diagnosa dampak penggunaan softlens berdasarkan gejala yang dialami, dan mengetahui cara penanggulangannya.
