@conference {654,
	title = {What{\textquoteright}s in this paper? Combining Rhetorical Entities with Linked Open Data for Semantic Literature Querying},
	booktitle = {Semantics, Analytics, Visualisation: Enhancing Scholarly Data (SAVE-SD 2015)},
	year = {2015},
	note = {Received "Best Paper Award" at SAVE-SD 2015},
	month = {05/2015},
	pages = {1023{\textendash}1028},
	publisher = {ACM},
	organization = {ACM},
	type = {Full Paper},
	address = {Florence, Italy},
	abstract = {Finding research literature pertaining to a task at hand is one of the essential tasks that scientists face on daily basis. Standard information retrieval techniques allow to quickly obtain a vast number of potentially relevant documents. Unfortunately, the search results then require significant effort for manual inspection, where we would rather select relevant publications based on more fine-grained, semantically rich queries involving a publication{\textquoteright}s contributions, methods, or application domains.
We argue that a novel combination of three distinct methods can significantly advance this vision: (i) Natural Language Processing (NLP) for Rhetorical Entity (RE) detection; (ii) Named Entity (NE) recognition based on the Linked Open Data (LOD) cloud; and (iii) automatic generation of RDF triples for both NEs and REs using semantic web ontologies to interconnect them.  Combined in a single workflow, these techniques allow us to automatically construct a knowledge base that facilitates numerous advanced use cases for managing scientific documents.
},
	keywords = {Linked Open Data, natural language processing, RDF, Semantic Publishing, Semantic Web},
	doi = {10.1145/2740908.2742022},
	url = {http://www.www2015.it/documents/proceedings/companion/p1023.pdf},
	author = {Bahar Sateli and Ren{\'e} Witte}
}
