Matches in SemOpenAlex for { <https://semopenalex.org/work/W21497345> ?p ?o ?g. }
- W21497345 endingPage "93" @default.
- W21497345 startingPage "75" @default.
- W21497345 abstract "After the vision of the Semantic Web was broadcasted at the turn of the millennium, ontology became a synonym for the solution to many problems concerning the fact that computers do not understand human language: if there were an ontology and every document were marked up with it and we had agents that would understand the markup, then computers would finally be able to process our queries in a really sophisticated way. Some years later, the success of Google shows us that the vision has not come true, being hampered by the incredible amount of extra work required for the intellectual encoding of semantic mark-up – as compared to simply uploading an HTML page. To alleviate this acquisition bottleneck, the field of ontology learning has since emerged as an important sub-field of ontology engineering. It is widely accepted that ontologies can facilitate text understanding and automatic processing of textual resources. Moving from words to concepts not only mitigates data sparseness issues, but also promises appealing solutions to polysemy and homonymy by finding non-ambiguous concepts that may map to various realizations in – possibly ambiguous – words. Numerous applications using lexical-semantic databases like WordNet (Miller, 1990) and its non-English counterparts, e.g. EuroWordNet (Vossen, 1997) or CoreNet (Choi and Bae, 2004) demonstrate the utility of semantic resources for natural language processing. Learning semantic resources from text instead of manually creating them might be dangerous in terms of correctness, but has undeniable advantages: Creating resources for text processing from the texts to be processed will fit the semantic component neatly and directly to them, which will never be possible with general-purpose resources. Further, the cost per entry is greatly reduced, giving rise to much larger resources than an advocate of a manual approach could ever afford. On the other hand, none of the methods used today are good enough for creating semantic resources of any kind in a completely unsupervised fashion, albeit automatic methods can facilitate manual construction to a large extent. The term ontology is understood in a variety of ways and has been used in philosophy for many centuries. In contrast, the notion of ontology in the field of computer science is younger – but almost used as inconsistently, when it comes to the details of the definition. The intention of this essay is to give an overview of different methods that learn ontologies or ontology-like structures from unstructured text. Ontology learning from other sources, issues in description languages, ontology editors, ontology merging and ontology evolving transcend the scope of this article. Surveys on ontology learning from text and other sources can be found in Ding and Foo (2002) and Gomez-Perez" @default.
- W21497345 created "2016-06-24" @default.
- W21497345 creator A5021287757 @default.
- W21497345 date "2005-07-01" @default.
- W21497345 modified "2023-10-14" @default.
- W21497345 title "Ontology Learning from Text: A Survey of Methods" @default.
- W21497345 cites W1489800245 @default.
- W21497345 cites W1489949474 @default.
- W21497345 cites W1520232900 @default.
- W21497345 cites W1521908097 @default.
- W21497345 cites W1528321674 @default.
- W21497345 cites W1534261948 @default.
- W21497345 cites W1539384251 @default.
- W21497345 cites W1543515964 @default.
- W21497345 cites W1547207403 @default.
- W21497345 cites W1549479357 @default.
- W21497345 cites W1559783642 @default.
- W21497345 cites W1562494296 @default.
- W21497345 cites W1568698554 @default.
- W21497345 cites W1570274086 @default.
- W21497345 cites W1573981182 @default.
- W21497345 cites W1576784549 @default.
- W21497345 cites W1585353053 @default.
- W21497345 cites W1587330583 @default.
- W21497345 cites W1606238867 @default.
- W21497345 cites W1607723633 @default.
- W21497345 cites W1612003148 @default.
- W21497345 cites W1633771733 @default.
- W21497345 cites W163970217 @default.
- W21497345 cites W1868671693 @default.
- W21497345 cites W1930023685 @default.
- W21497345 cites W197270748 @default.
- W21497345 cites W1977182536 @default.
- W21497345 cites W2003180218 @default.
- W21497345 cites W2018203936 @default.
- W21497345 cites W2036373663 @default.
- W21497345 cites W2064546102 @default.
- W21497345 cites W2068737686 @default.
- W21497345 cites W2082846886 @default.
- W21497345 cites W2085729670 @default.
- W21497345 cites W2093381383 @default.
- W21497345 cites W2100377551 @default.
- W21497345 cites W2100653055 @default.
- W21497345 cites W2101087886 @default.
- W21497345 cites W2103931177 @default.
- W21497345 cites W2107658650 @default.
- W21497345 cites W2123084125 @default.
- W21497345 cites W2125464431 @default.
- W21497345 cites W2127314673 @default.
- W21497345 cites W2130337399 @default.
- W21497345 cites W2132024321 @default.
- W21497345 cites W2133108446 @default.
- W21497345 cites W2135922393 @default.
- W21497345 cites W2137079713 @default.
- W21497345 cites W2140259275 @default.
- W21497345 cites W2147152072 @default.
- W21497345 cites W2151375725 @default.
- W21497345 cites W2151846280 @default.
- W21497345 cites W2158027649 @default.
- W21497345 cites W2160053217 @default.
- W21497345 cites W2160587453 @default.
- W21497345 cites W2161669948 @default.
- W21497345 cites W2165612380 @default.
- W21497345 cites W218889330 @default.
- W21497345 cites W2401160776 @default.
- W21497345 cites W2500324896 @default.
- W21497345 cites W2760048271 @default.
- W21497345 cites W3134607232 @default.
- W21497345 cites W71789194 @default.
- W21497345 cites W84750871 @default.
- W21497345 cites W88073455 @default.
- W21497345 cites W93567766 @default.
- W21497345 doi "https://doi.org/10.21248/jlcl.20.2005.76" @default.
- W21497345 hasPublicationYear "2005" @default.
- W21497345 type Work @default.
- W21497345 sameAs 21497345 @default.
- W21497345 citedByCount "85" @default.
- W21497345 countsByYear W214973452012 @default.
- W21497345 countsByYear W214973452013 @default.
- W21497345 countsByYear W214973452014 @default.
- W21497345 countsByYear W214973452015 @default.
- W21497345 countsByYear W214973452016 @default.
- W21497345 countsByYear W214973452017 @default.
- W21497345 countsByYear W214973452018 @default.
- W21497345 countsByYear W214973452019 @default.
- W21497345 countsByYear W214973452020 @default.
- W21497345 countsByYear W214973452021 @default.
- W21497345 countsByYear W214973452023 @default.
- W21497345 crossrefType "journal-article" @default.
- W21497345 hasAuthorship W21497345A5021287757 @default.
- W21497345 hasBestOaLocation W214973451 @default.
- W21497345 hasConcept C111472728 @default.
- W21497345 hasConcept C136764020 @default.
- W21497345 hasConcept C138885662 @default.
- W21497345 hasConcept C204321447 @default.
- W21497345 hasConcept C23123220 @default.
- W21497345 hasConcept C25810664 @default.
- W21497345 hasConcept C41008148 @default.