دانلود مقاله ISI انگلیسی شماره 44425
ترجمه فارسی عنوان مقاله

تجزیه و تحلیل تاثیر متقابل معنایی مقایسه شده است

عنوان انگلیسی
Semantic compared cross impact analysis
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
44425 2014 7 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 41, Issue 7, 1 June 2014, Pages 3477–3483

ترجمه کلمات کلیدی
تجزیه و تحلیل تاثیر، نمایه سازی معناشناسی باقیمانده، استخراج متن، مقایسه اثر متقابل متقابل، معدن وب
کلمات کلیدی انگلیسی
Cross impact analysis; Latent semantic indexing; Text mining; Compared cross impact analysis; Web mining

چکیده انگلیسی

The aim of cross impact analysis (CIA) is to predict the impact of a first event on a second. For organization’s strategic planning, it is helpful to identify the impacts among organization’s internal events and to compare these impacts to the corresponding impacts of external events from organization’s competitors. For this, literature has introduced compared cross impact analysis (CCIA) that depicts advantages and disadvantages of the relationships between organization’s events to the relationships between competitors’ events. However, CCIA is restricted to the use of patent data as representative for competitors’ events and it applies a knowledge structure based text mining approach that does not allow considering semantic aspects from highly unstructured textual information. In contrast to related work, we propose an internet based environmental scanning procedure to identify textual patterns represent competitors’ events. To enable processing of this highly unstructured textual information, the proposed methodology uses latent semantic indexing (LSI) to calculate the compared cross impacts (CCI) for an organization. A latent semantic subspace is built that consists of semantic textual patterns. These patterns are selected that represent organization’s events. A web mining approach is used for crawling textual information from the internet based on keywords extracted from each selected pattern. This textual information is projected into the same latent semantic subspace. Based on the relationships between the semantic textual patterns in the subspace, CCI is calculated for different events of an organization. A case study shows that the proposed approach successfully calculates the CCI for technologies processed by a governmental organization. This enables decision makers to direct their investments more targeted.