Ontology Learning and Population: Bridging the Gap between Text and Knowledge
Ontology Learning is up to now dominated by techniques which use text as input. There are only few methods which use a different data source. The techniques which use highly structured data as input have the disadvantage that such data sources are rare. On the other side, there are enormous amoun...
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| Những tác giả chính: | , |
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
| Được phát hành: |
IOS Press
2013
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| Những chủ đề: | |
| Truy cập trực tuyến: | http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/35195 |
| Các nhãn: |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| Tóm tắt: | Ontology Learning is up to now dominated by techniques which use
text as input. There are only few methods which use a different data source. The
techniques which use highly structured data as input have the disadvantage that
such data sources are rare. On the other side, there are enormous amounts of Web
content present today.
We present the XTREEM (Xhtml TREE Mining) methods which enable Ontology
Learning from Web Documents. Those methods rely on the semi-structure of
Web Documents. The added value of Web document markup is exploited by the
XTREEM methods. We show methods for the acquisition of terms, synonyms and
semantic relations.
The XTREEM techniques are based on the structure of Web documents; they
are domain and language independent. There is no need for NLP software nor for
training. They do not rely on domain or document collection specific resources or
background knowledge, such as patterns, rules or other heuristics; nor do they rely
on manually assembling a document collection. |
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