Towards the self-annotating web

By Philipp Cimiano, Siegfried Handschuh, Steffen Staab. The Web Conference, 2004

439 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/bead0ccccd0ab1da5237ca7ea80b425745e94083

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

1. Roberto Navigli
2. Simone Paolo Ponzetto
3. Fei Wu
4. Daniel S. Weld
5. Dejing Dou
6. Peter Mika
7. Daya C. Wimalasuriya
8. Philipp Cimiano

In Information Systems

1. Peter Mika
2. Yong Yu
3. Philipp Cimiano
4. Steffen Staab
5. Xianqi Wu
6. Lei Zhang
7. Fei Wu
8. Roberto Navigli

Citations in the corpus

• Semantic annotation for knowledge management: Requirements and a survey of the state of the art (2006, background): “PANKOW uses a range of relatively rare, but informative, syntactic patterns to mark-up candidate phrases in Web pages without having to manually produce an initial set of marked-up Web pages and go through a supervised learning step.”
• BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network (2012, methodology): “These include proposals to extract semantic information from dictionaries [21,38,115, inter alia], approaches using lexico-syntactic patterns [49,23,43,96], heuristic methods based on lexical and semantic regularities [47], taxonomy-based ontologization [96,103,122].”
• Exploring social annotations for the semantic web (2006, methodology): “Usually, the semantic annotations are made manually using a toolkit such as Protege or CREAM [26, 31] or semi-automatically through user interaction with a disambiguation algorithm [18, 4, 5, 6].”
• Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis (2005, methodology): “In particular, we explore the following three information measures (see Cimiano, S.Staab, & Tane, 2003; Cimiano et al., 2004b): Furthermore, f(n;v arg ) is the total number of occurrences of a term n as argument arg of a verb v , f(v arg ) is the number of occurrences of verb v with such an…”
• Ontology-based information extraction: An introduction and a survey of current approaches (2010, methodology): “Such systems include SOBA [43], KIM [27] the implementation by Li and Bontcheva [8], the implementation by Saggion et al. [33] and PANKOW [23].”
• Automatically refining the wikipedia infobox ontology (2008, methodology): “This is first proposed by Marti Hearst to acquire hypony ms from large text corpora [19], and later followed by many succ essful systems, such as KnowItAll [16] and PANKOW [8, 9].”
• Measuring semantic similarity between words using web search engines (2007, methodology): “Semantic similarity measures have been used in Semantic Web related applications such as automatic annotation of Web pages [7], community mining [23, 19], and keyword extraction for inter-entity relation representation [26].”
• Ontologies are us: A unified model of social networks and semantics (2005, methodology): “In some sense, this is the opposite of mining general knowledge from search engines as in the work of Cimiano et al. or Etzioni et al. [ 16 , 17].”
• POLYPHONET: an advanced social network extraction system from the web (2006, background): “That system, Pattern-based ANnotation through Knowledge On the Web (PANKOW), assigns a named entity into several linguistic patterns that convey semantic meanings [9, 10].”