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Register a new userExtracting Business Process Models from Natural Language Texts
استخلاص مخططات إجراءات العمل انطلاقا من النصوص
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نقترح في هذه الأطروحة منهجية لتوليد مخططات الإجراءات من النصوص تحاكي عملية الترجمة الآلية, حيث تعتمد المنهجية المقترحة أسلوب الترجمة بالنقل على أساس دلالي, حيث ستتم في المرحلة الأولى معالجة النص لاستخلاص المفاهيم الأساسية فيه و العلاقات الدلالية بين هذه المفاهيم بما يشكل شبكة مفاهيم concept map للنص .
In our work, we chose to follow semantic transfer based approach. Our approach consists of two main phases. The first phase, Natural Language Analysis phase, aims to analyze the text and extract the required knowledge from it. In addition to the syntactic analysis results, one of the main outputs for this phase is a concept map which summarize the concepts of the related domain and the relationships between these concepts.
References used
Abney, S. (1996). Partial parsing via finite-state cascades. Natural Language Engineering, 2(4), 337-344
Achour, C. B. (1998). Guiding scenario authoring. In In: 8th European-Japanese Conference on Information Modelling and Knowledge Bases
Blumberg, R., & Atre, S. (2003). The problem with unstructured data. Dm Review, 13(42-49), 62
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Various methods have been developed to measure the location of physical objects
on a landscape with high positional accuracy. A new method that has been gaining
popularity is the Airborne Light Detection and Ranging (LiDAR). LiDAR works by
scan
ning a landscape (the combination of ground, buildings, vegetation, etc.,) by
multiple passes. In each scan (pass), pulses of laser light are emitted from an
airborne platform and their return time is measured, thus enabling the range from
the point of emission to the landscape to be determined. The product of airborne
laser scanning is a cloud of points located in a 3D space. ALS is capable of
delivering clouds of very dense and accurate points that represent the landscape in a
relatively short time. However, in spite of the ability to measure objects with high
positional accuracy, the automatic detection and interpretation of individual objects
in landscapes remains a challenge. An example of such a challenge is the
classification of the cloud points produced by ALS. The classification of LiDAR
cloud points consists first of all of assigning the points as either object points or
bare ground ones. The points labeled object points are then further classified as
either buildings or vegetation. As a measurement technique, LiDAR is highly
promising, research has been conducted here to automate the detection of bare
ground, buildings and vegetation in LiDAR cloud points.
In this Research, we describe a new automated scheme that utilizes the so-called
“Edge Topology based Iterative Segmentation” (ETIS) model to classify the
LiDAR points as ground and objects points. First ground seed points based on
edges topology are to be selected and then the initial DTM is to be constructed, the
second step is an iterative densification of the DTM using a cloud point
segmentation method based on local slope parameter. General ground point
filtering parameters have been used was achieved in this method, instead of scene-
wise optimization of the parameters, in a way that many groups of benchmark
datasets have been without changing the thresholds values. Data provided by the
International Society for Photogrammetry and Remote Sensing (ISPRS)
commission, have been used to compare the performance of ETIS. The new method
is also tested against the 16 other publicized filtering methods. The results indicat
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