OntoExtractor: A Fuzzy-Based Approach in Clustering Semi-structured Data Sources and Metadata Generation

Cui, Zhan; Damiani, Ernesto; Leida, Marcello; Viviani, Marco

doi:10.1007/11552413_17

Cited by 4 publications

(6 citation statements)

References 2 publications

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“…This is performed based on the output fuzzy sets (Figure 6). The algorithm checks which interval the output falls into and generates the metadata value based on equations (7) and (8). According to our example, the generated output is "MediumDifficulty".…”

Section: Proposed Fuzzy Information Granulation and Fuzzy Inference Smentioning

confidence: 99%

“…Step 8: Based on the metadata value and the interval that m_value falls into, our algorithm calculates a metadata confidence score. Let that has the highest membership degree on the output fuzzy set, 1 x is the left boundary of the metadata interval and 2 x is the right boundary of the metadata interval according to equations (7) and (8 ). Then, confidence score is: , which is a low score since value= 16.66 is closer to the boundary.…”

Section: Proposed Fuzzy Information Granulation and Fuzzy Inference Smentioning

confidence: 99%

“…Successful examples of this are [34] [41]. On the other hand, similar approaches that took advantage of structured XML documents and fuzzy techniques for automatic ontology (taxonomy) generation are presented in [5] [6] and [8]. In our research, our focus is on cognitive metadata extraction nd not ontology generation.…”

Section: Cognitive/pedagogical Metadata Extractionmentioning

confidence: 99%

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Automatic Metadata Mining from Multilingual Enterprise Content

Şah

Wade

2012

SSRN Journal

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Abstract. Personalization is increasingly vital especially for enterprises to be able to reach their customers. The key challenge in supporting personalization is the need for rich metadata, such as metadata about structural relationships, subject/concept relations between documents and cognitive metadata about documents (e.g. difficulty of a document). Manual annotation of large knowledge bases with such rich metadata is not scalable. As well as, automatic mining of cognitive metadata is challenging since it is very difficult to understand underlying intellectual knowledge about document automatically. On the other hand, the Web content is increasing becoming multilingual since growing amount of data generated on the Web is nonEnglish. Current metadata extraction systems are generally based on English content and this requires to be revolutionized in order to adapt to the changing dynamics of the Web. To alleviate these problems, we introduce a novel automatic metadata extraction framework, which is based on a novel fuzzy based method for automatic cognitive metadata generation and uses different document parsing algorithms to extract rich metadata from multilingual enterprise content using the newly developed DocBook, Resource Type and Topic ontologies. Since the metadata generation process is based upon DocBook structured enterprise content, our framework is focused on enterprise documents and content which is loosely based on the DocBook type of formatting. DocBook is a common documentation formatting to formally produce corporate data and it is adopted by many enterprises. The proposed framework is illustrated and evaluated on English, German and French versions of the Symantec Norton 360 knowledge bases. The user study showed that the proposed fuzzy-based method generates reasonably accurate values with an average precision of 89.39% on the metadata values of document difficulty, document interactivity level and document interactivity type. The proposed fuzzy inference system achieves improved results compared to a rulebased reasoner for difficulty metadata extraction (~11% enhancement). In addition, user perceived metadata quality scores (mean of 5.57 out of 6) found to be high and automated metadata analysis showed that the extracted metadata is high quality and can be suitable for personalized information retrieval.

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Section: Proposed Fuzzy Information Granulation and Fuzzy Inference Smentioning

confidence: 99%

Section: Proposed Fuzzy Information Granulation and Fuzzy Inference Smentioning

confidence: 99%

Section: Cognitive/pedagogical Metadata Extractionmentioning

confidence: 99%

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Automatic Metadata Mining from Multilingual Enterprise Content

Şah

Wade

2012

SSRN Journal

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“…In most enterprise domains, fortunately documents are very structured and formatted by XML, which allow intellectual analysis of document semantics and reasoning using fuzzy inference. On the other hand, [12] [13] and [14] uses structured XML documents and fuzzy techniques for automatic ontology (taxonomy) generation. In our research, our focus is cognitive metadata extraction and not ontology generation.…”

Section: Related Work On Automatic Cognitive/pedagogical Metadata Extmentioning

confidence: 99%

Automatic mining of cognitive metadata using fuzzy inference

Şah

Wade

2011

Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia

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Personalized search and browsing is increasingly vital especially for enterprises to able to reach their customers. Key challenge in supporting personalization is the need for rich metadata such as cognitive metadata about documents. As we consider size of large knowledge bases, manual annotation is not scalable and feasible. On the other hand, automatic mining of cognitive metadata is challenging since it is very difficult to understand underlying intellectual knowledge about documents automatically. To alleviate this problem, we introduce a novel metadata extraction framework, which is based on fuzzy information granulation and fuzzy inference system for automatic cognitive metadata mining. The user evaluation study shows that our approach provides reasonable precision rates for difficulty, interactivity type, and interactivity level on the examined 100 documents. In addition, proposed fuzzy inference system achieves improved results compared to a rule-based reasoner for document difficulty metadata extraction (11% improvement).

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“…While not all available information items can be expected to be in XML format, we extensively rely on software wrappers [37], a well-understood technique for mapping foreign data-types to the XML data model. Therefore, without loss of generality, here we assume data items to be represented by XML instances.…”

Section: A Bottom-up Fuzzy Knowledge Extraction Enginementioning

confidence: 99%

Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata

Ceravolo

Damiani

Viviani

2007

IEEE Trans. Knowl. Data Eng.

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Abstract-We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run.

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OntoExtractor: A Fuzzy-Based Approach in Clustering Semi-structured Data Sources and Metadata Generation

Cited by 4 publications

References 2 publications

Automatic Metadata Mining from Multilingual Enterprise Content

Automatic Metadata Mining from Multilingual Enterprise Content

Automatic mining of cognitive metadata using fuzzy inference

Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata

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