Stage-oe-small.jpg

Inproceedings3525: Unterschied zwischen den Versionen

Aus Aifbportal
Wechseln zu:Navigation, Suche
Zeile 19: Zeile 19:
 
|Publisher=IEEE Computer Society Press
 
|Publisher=IEEE Computer Society Press
 
|Note=to appear
 
|Note=to appear
}}
 
{{Publikation Tool
 
|Tool=X-LiSA
 
 
}}
 
}}
 
{{Publikation Details
 
{{Publikation Details
 +
|Abstract=In recent years, the amount of entities in large knowledge bases has been increasing rapidly. Such entities can help to bridge unstructured text with structured knowledge and thus be beneficial for many entity-centric applications. The key issue is to link entity mentions in text with entities in knowledge bases, where the main challenge lies in mention ambiguity. Many methods have been proposed to tackle this problem. However, most of the methods assume certain characteristics of the input mentions and documents, e.g., only named entities are considered. In this paper, we propose a context-aware approach to collective entity disambiguation of the input mentions in text with different characteristics in a consistent manner. We extensively evaluate the performance of our approach over 9 datasets and compare it with 14 state-of-the-art methods. Experimental results show that our approach outperforms the existing methods in most cases.
 +
|Download=Wi2016.pdf,
 
|Projekt=XLiMe
 
|Projekt=XLiMe
 
|Forschungsgruppe=Web Science und Wissensmanagement
 
|Forschungsgruppe=Web Science und Wissensmanagement
 
}}
 
}}

Version vom 28. Oktober 2016, 00:31 Uhr


Context-Aware Entity Disambiguation in Text Using Markov Chains


Context-Aware Entity Disambiguation in Text Using Markov Chains



Published: 2016 Oktober

Buchtitel: IEEE/WIC/ACM International Conference on Web Intelligence (WI'16)
Verlag: IEEE Computer Society Press

Referierte VeröffentlichungNote: to appear

BibTeX

Kurzfassung
In recent years, the amount of entities in large knowledge bases has been increasing rapidly. Such entities can help to bridge unstructured text with structured knowledge and thus be beneficial for many entity-centric applications. The key issue is to link entity mentions in text with entities in knowledge bases, where the main challenge lies in mention ambiguity. Many methods have been proposed to tackle this problem. However, most of the methods assume certain characteristics of the input mentions and documents, e.g., only named entities are considered. In this paper, we propose a context-aware approach to collective entity disambiguation of the input mentions in text with different characteristics in a consistent manner. We extensively evaluate the performance of our approach over 9 datasets and compare it with 14 state-of-the-art methods. Experimental results show that our approach outperforms the existing methods in most cases.

Download: Media:Wi2016.pdf

Projekt

XLiMe



Forschungsgruppe

Web Science und Wissensmanagement


Forschungsgebiet