Ontology Learning for the Semantic Web

By Alexander Maedche, Steffen Staab. 2002

2,282 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, background): “Ontology learning generally (Maedche and Staab, 2001), and ontology learning from text speciﬁcally is a hot topic (Cimiano and V¨olker, 2005).”
Joint Video and Text Parsing for Understanding Events and Answering Queries (2013, methodology): “This ontology can be manually constructed using an ontology editor (e.g., [41], [42], [43]), adapted from an existing ontology (e.g., [44], [45]), or automatically or semi-automatically learned from data (e.g., [46], [47], [48]).”
Learning to map between ontologies on the semantic web (2002, background): “…n ota d e T d e h e e n s e d hGh n h e t n e e d h n tad h n nan t dis e n h t d h Tam e n h d t t ibd n ean t d t m m e mtn t d ktoed t t n oin t e nm6 2 t oo d b m Dat o h 2 tb D o k eeoed a a e n h n o hih n h t n e e d h nWn m n d e s d h e n d n hen d h t h d x m dem t d etd hah n sam m n…”
Ontology Learning and Its Application to Automated Terminology Translation (2003, background): “where is a normalized entropy and α ∈ (0, 1 ). Only the complex terms with a DW value over a given threshold are retained.”
Knowledge graph refinement: A survey of approaches and evaluation methods (2016, neutral): “, Cities are Places) [13,64].”
AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems (2022, background): “• Ontology-based In general, ontology is “an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data” [56].”
Methods and tools for ontology evolution (2004, methodology): “Other data-mining methods may be applied [73], [77].”
Link mining: a survey (2005, result): “There has been some other work in this area, for example Madche and Staab [77] and Doan et al.”
Automatic fuzzy ontology generation for semantic Web (2006, background): “Typically, ontology can be generated from various data types such as textual data [10], [11], [12], [13], [14], [15], [16], [17], dictionary [18], [19], knowledge-based [20] semistructured schemata [21], [22], [23], and relational schemata [24], [25], [26], [27].”
A Protégé Plug-In for Ontology Extraction from Text Based on Linguistic Analysis (2004, background): “Springer-Verlag Berlin Heidelberg 2004 32 P. Buitelaar, D. Olejnik, and M. Sintek learning algorithms to find potentially interesting concepts and relations between them (see also Maedche, 2003).”
Ontology Matching: A Machine Learning Approach (2004, methodology): “Our work here provides techniques to help in the ontology construction process [22].”
A critical review of Knowledge-Based Engineering: An identification of research challenges (2012, background): “Further exploitation of data entities in KBE automation tools requires a ‘‘knowledge connection’’ where not only the syntactic content of the data but its common meaning is exchanged, an essential principle of the so-called ‘‘Semantic Web’’ [63].”
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites (2004, background): “Finally, in Maedche and Staab (2000, 2001), an architecture is presented to help ontology engineers in the difﬁcult task of creating an ontology.”