Answering Definition Questions Using Web Knowledge Bases

2005 ◽  
pp. 498-506 ◽  
Author(s):  
Zhushuo Zhang ◽  
Yaqian Zhou ◽  
Xuanjing Huang ◽  
Lide Wu
Keyword(s):  
Knowledge Bases ◽  
Web Knowledge

Disambiguation and Filtering Methods in Using Web Knowledge for Coreference Resolution

2012 ◽  
pp. 185-201 ◽  
Author(s):  
Olga Uryupina ◽  
Massimo Poesio ◽  
Claudio Giuliano ◽  
Kateryna Tymoshenko
Keyword(s):  
Semantic Information ◽  
Knowledge Bases ◽  
Coreference Resolution ◽  
Number Of Solutions ◽  
Web Resources ◽  
Percentage Points ◽  
Resolution System ◽  
Semantic Compatibility ◽  
Additional Constraints ◽  
Web Knowledge

The authors investigate two publicly available Web knowledge bases, Wikipedia and Yago, in an attempt to leverage semantic information and increase the performance level of a state-of-the-art coreference resolution engine. They extract semantic compatibility and aliasing information from Wikipedia and Yago, and incorporate it into a coreference resolution system. The authors show that using such knowledge with no disambiguation and filtering does not bring any improvement over the baseline, mirroring the previous findings (Ponzetto & Poesio, 2009). They propose, therefore, a number of solutions to reduce the amount of noise coming from Web resources: using disambiguation tools for Wikipedia, pruning Yago to eliminate the most generic categories and imposing additional constraints on affected mentions. The evaluation experiments on the ACE-02 corpus show that the knowledge, extracted from Wikipedia and Yago, improves the system’s performance by 2-3 percentage points.

Multilingual Natural Language Interaction with Semantic Web Knowledge Bases and Linked Open Data

2014 ◽  
pp. 211-226 ◽  
Author(s):  
Mariana Damova ◽  
Dana Dannélls ◽  
Ramona Enache ◽  
Maria Mateva ◽  
Aarne Ranta
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Open Data ◽  
Knowledge Bases ◽  
Linked Open Data ◽  
Natural Language Interaction ◽  
Web Knowledge

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
Author(s):  
F. Buekens ◽  
G. De Moor ◽  
A. Waagmeester ◽  
W. Ceusters
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Natural Language Understanding ◽  
Knowledge Bases ◽  
Linguistic Knowledge ◽  
Medical Terminology ◽  
Language Understanding ◽  
Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

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