KIM – a semantic platform for information extraction and retrieval
By Borislav Popov, Atanas Kiryakov, Damyan Ognyanoff, Dimitar Manov, Angel Kirilov. Natural Language Engineering, 2004
335 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/fe2b08135fbfe3d3a8846f5b328afb8a0ab1567d
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
- Pablo Castells
- Miriam Fernández
- David Vallet
- Philipp Cimiano
- Dejing Dou
- Daya C. Wimalasuriya
- Fabio Ciravegna
- Denilson Barbosa
In Information Systems
- Miriam Fernández
- David Vallet
- Pablo Castells
- Philipp Cimiano
- Dejing Dou
- Daya C. Wimalasuriya
- Fabio Ciravegna
- Siegfried Handschuh
Citations in the corpus
- An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval (2007, methodology): “[17], [20], [28].”
- Ontology-based information extraction: An introduction and a survey of current approaches (2010, background): “This process is also known as the semantic annotation of web pages [23, 26, 27].”
- Robust named entity disambiguation with random walks (2018, background): “ca many applications, such as: (1) expanding or correcting KBs with facts of entities extracted from text [16]; (2) semantic search [27], the emerging paradigm of Web search that combines Information Retrieval approaches over document corpora with KB-style query answering and reasoning; and (3) in N”
- Semantic annotation for knowledge management: Requirements and a survey of the state of the art (2006, methodology): “The development of KIM is set to continue in collaboration with DERI Galway and the GATE research team under the banner of SWAN 9 , a Semantic Web Annotator.”
- Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic (2012, neutral): “Such OBIE systems include PANKOW [9], [10]), SOBA [11], OntoSyphon [12], Vulcain [13], and KIM [14].”
- Getting More Out of Biomedical Documents with GATE's Full Lifecycle Open Source Text Analytics (2013, background): “…N full-text-based, with boolean and proximity operators [44]; N annotation-based, with an underlying graph representation encoding structured information about text ranges [45]; N ontology-based, with hierarchical conceptual schemas plus concept instance sets from documents and databases [46].”