Ontology-Based Information Extraction of Crop Diseases on Chinese Web Pages

Jiang, Bo; Zhu, Mengxia; Wang, Jiale

doi:10.4304/jcp.8.1.85-90

Cited by 8 publications

(8 citation statements)

References 9 publications

(6 reference statements)

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“…Ontology can be applied to extract information from texts and documents [9][10][11]. It can also be used to retrieve information in some other fields, such as e-commerce [12] and crop diseases [13].…”

Section: B Ontology For the Knowledge Basementioning

confidence: 99%

Discovering Relationships between Data Structures and Algorithms

Zhu¹,

Yu²,

Li³

2013

JSW

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There are numerous of program code resources on the web which are solutions to programming problems on online judges. These program code resources are not organized for students to learn data structures and algorithms although they contain much knowledge of data structures and algorithms. For this reason, we propose an approach to organize the program code resources together with the programming problems systematically in terms of algorithms and data structures. This approach is based on the discovery of associate relationships between data structures and algorithms by applying ontology techniques. 1073 program codes on the web which are solutions to 480 problems distributed on online judges were mined in our experiment to discover the relationships between the data structures and algorithms used in the program codes. With the discovered relationships, the program codes and the corresponding problems were organized into learning materials in terms of algorithms and data structures. We believe that it would be useful for students to learn the programming knowledge.

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Section: B Ontology For the Knowledge Basementioning

confidence: 99%

Discovering Relationships between Data Structures and Algorithms

Zhu¹,

Yu²,

Li³

2013

JSW

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“…In [5], the author proposed a video search crawler in distributed environment and discussed key techniques about video search and data collection. Previous works in [6] [7][8] [9] investigated the method of information extraction, some of researcher also explored extracting structured data from Ajax site, whose data was loaded by triggering JavaScript. All video sharing sites we studied used this dynamical load method.…”

Section: Introductionmentioning

confidence: 99%

A Video Sharing Site Oriented Data Collection System

Wang¹,

Chen²,

Wang³

2013

JSW

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Analysis of video data have significant meaning for studying the characteristics video sharing site and understanding its user's behavior and preference. In order to achieve this goal, we designed and implemented a high performance video sharing site oriented data collection system (VDCS), which overcomes the low efficiency problems that traditional crawler facing. High time efficiency video search strategy is proposed to extract structured video data and high space efficiency filter mechanism is explored for filtering videos. The experiments and analysis proves VDCS have much more practical significance in collecting video data compare with traditional crawler due to its video search strategy and filter mechanism. Through analysis of the datasets we built, we studied video sharing sites' characteristic which includes the upload trend of videos, views and comments and social networks among videos, this study enhanced our understanding of video sharing sites and provide a new measurement tool for analyzing of video data.

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“…Martina [11] proposed sentence-level event detection in news webpages and found predefined type events. Jiang [12] used open information extraction and proposed event ontology to find various events. David [13] thought important named entities, such as time, place, people, and organization, and the co-occurrence relationships between them were also used to find events.…”

Section: Research Backgroundmentioning

confidence: 99%

“…The eight dimensions are denoted as { agent, activity, { object }, time, { location }, { cause }, { purpose }, { manner }}. The event mention set is represented as EM = {em 11 , em 12 …”

Section: Event Mention and Eventmentioning

confidence: 99%

Event Detection in Multiple Webpages based on Comprehensive Dimension Matching and Co-occurrence Constraint

Xu¹,

Zhou²,

Wang³

2014

Appl. Math. Inf. Sci.

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Detecting various sentence-level events from multiple webpages can be important in finding knowledge. We propose an event detection method based on comprehensive dimension matching and co-occurrence constraint. First, we detect events from a single webpage by clustering co-reference sentence-level event mentions. These events are considered as co-occurrence events in every single webpage. Second, similar events from multiple webpages are clustered. The dimension matching method is used to aggregate event mentions. Different matchers measure different dimensions, and an extended evidence theory is proposed to allocate dynamic weight and combine dimension measurement results. We propose an event co-occurrence constraint to reduce match times and quantity of candidate matches events in the multiple webpages event-detection process to improve event cluster efficiency. The experiment results demonstrate that this method can detect various events and noticeably reduce the quantity of co-reference events.

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Ontology-Based Information Extraction of Crop Diseases on Chinese Web Pages

Cited by 8 publications

References 9 publications

Discovering Relationships between Data Structures and Algorithms

Discovering Relationships between Data Structures and Algorithms

A Video Sharing Site Oriented Data Collection System

Event Detection in Multiple Webpages based on Comprehensive Dimension Matching and Co-occurrence Constraint

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