Integrating and navigating engineering design decision-related knowledge using decision knowledge graph
Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not read...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hao, Jia [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: X-ray detectors in medical imaging - Spahn, Martin ELSEVIER, 2013, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:50 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.aei.2021.101366 |
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ELV055832385 |
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| 520 | |a Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. | ||
| 520 | |a Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. | ||
| 650 | 7 | |a Design |2 Elsevier | |
| 650 | 7 | |a Navigation |2 Elsevier | |
| 650 | 7 | |a Searching |2 Elsevier | |
| 650 | 7 | |a Decision support |2 Elsevier | |
| 650 | 7 | |a Knowledge graph |2 Elsevier | |
| 700 | 1 | |a Zhao, Lei |4 oth | |
| 700 | 1 | |a Milisavljevic-Syed, Jelena |4 oth | |
| 700 | 1 | |a Ming, Zhenjun |4 oth | |
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10.1016/j.aei.2021.101366 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001578.pica (DE-627)ELV055832385 (ELSEVIER)S1474-0346(21)00119-1 DE-627 ger DE-627 rakwb eng 530 VZ 610 VZ 44.90 bkl Hao, Jia verfasserin aut Integrating and navigating engineering design decision-related knowledge using decision knowledge graph 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. Design Elsevier Navigation Elsevier Searching Elsevier Decision support Elsevier Knowledge graph Elsevier Zhao, Lei oth Milisavljevic-Syed, Jelena oth Ming, Zhenjun oth Enthalten in Elsevier Science Spahn, Martin ELSEVIER X-ray detectors in medical imaging 2013 Amsterdam [u.a.] (DE-627)ELV016695070 volume:50 year:2021 pages:0 https://doi.org/10.1016/j.aei.2021.101366 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 GBV_ILN_164 GBV_ILN_300 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2180 44.90 Neurologie VZ AR 50 2021 0 |
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10.1016/j.aei.2021.101366 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001578.pica (DE-627)ELV055832385 (ELSEVIER)S1474-0346(21)00119-1 DE-627 ger DE-627 rakwb eng 530 VZ 610 VZ 44.90 bkl Hao, Jia verfasserin aut Integrating and navigating engineering design decision-related knowledge using decision knowledge graph 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. Design Elsevier Navigation Elsevier Searching Elsevier Decision support Elsevier Knowledge graph Elsevier Zhao, Lei oth Milisavljevic-Syed, Jelena oth Ming, Zhenjun oth Enthalten in Elsevier Science Spahn, Martin ELSEVIER X-ray detectors in medical imaging 2013 Amsterdam [u.a.] (DE-627)ELV016695070 volume:50 year:2021 pages:0 https://doi.org/10.1016/j.aei.2021.101366 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 GBV_ILN_164 GBV_ILN_300 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2180 44.90 Neurologie VZ AR 50 2021 0 |
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10.1016/j.aei.2021.101366 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001578.pica (DE-627)ELV055832385 (ELSEVIER)S1474-0346(21)00119-1 DE-627 ger DE-627 rakwb eng 530 VZ 610 VZ 44.90 bkl Hao, Jia verfasserin aut Integrating and navigating engineering design decision-related knowledge using decision knowledge graph 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. Design Elsevier Navigation Elsevier Searching Elsevier Decision support Elsevier Knowledge graph Elsevier Zhao, Lei oth Milisavljevic-Syed, Jelena oth Ming, Zhenjun oth Enthalten in Elsevier Science Spahn, Martin ELSEVIER X-ray detectors in medical imaging 2013 Amsterdam [u.a.] (DE-627)ELV016695070 volume:50 year:2021 pages:0 https://doi.org/10.1016/j.aei.2021.101366 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 GBV_ILN_164 GBV_ILN_300 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2180 44.90 Neurologie VZ AR 50 2021 0 |
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10.1016/j.aei.2021.101366 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001578.pica (DE-627)ELV055832385 (ELSEVIER)S1474-0346(21)00119-1 DE-627 ger DE-627 rakwb eng 530 VZ 610 VZ 44.90 bkl Hao, Jia verfasserin aut Integrating and navigating engineering design decision-related knowledge using decision knowledge graph 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. Design Elsevier Navigation Elsevier Searching Elsevier Decision support Elsevier Knowledge graph Elsevier Zhao, Lei oth Milisavljevic-Syed, Jelena oth Ming, Zhenjun oth Enthalten in Elsevier Science Spahn, Martin ELSEVIER X-ray detectors in medical imaging 2013 Amsterdam [u.a.] (DE-627)ELV016695070 volume:50 year:2021 pages:0 https://doi.org/10.1016/j.aei.2021.101366 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 GBV_ILN_164 GBV_ILN_300 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2180 44.90 Neurologie VZ AR 50 2021 0 |
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10.1016/j.aei.2021.101366 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001578.pica (DE-627)ELV055832385 (ELSEVIER)S1474-0346(21)00119-1 DE-627 ger DE-627 rakwb eng 530 VZ 610 VZ 44.90 bkl Hao, Jia verfasserin aut Integrating and navigating engineering design decision-related knowledge using decision knowledge graph 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. Design Elsevier Navigation Elsevier Searching Elsevier Decision support Elsevier Knowledge graph Elsevier Zhao, Lei oth Milisavljevic-Syed, Jelena oth Ming, Zhenjun oth Enthalten in Elsevier Science Spahn, Martin ELSEVIER X-ray detectors in medical imaging 2013 Amsterdam [u.a.] (DE-627)ELV016695070 volume:50 year:2021 pages:0 https://doi.org/10.1016/j.aei.2021.101366 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 GBV_ILN_164 GBV_ILN_300 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2180 44.90 Neurologie VZ AR 50 2021 0 |
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Integrating and navigating engineering design decision-related knowledge using decision knowledge graph |
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Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. |
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Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. |
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Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge. |
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