We investigate post-OCR correction in a setting where we have access to different OCR views of the same document.
A few KGE techniques address interpretability, i. e., mapping the connectivity patterns of the relations (i. e., symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotations.
We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG).
Our empirical study was conducted on two well-known knowledge bases (i. e., Wikidata and Wikipedia).
Ranked #1 on Entity Linking on MSNBC
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG).
We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on state-of-the-art NED model for the Wikipedia knowledge base.
Ranked #2 on Entity Disambiguation on AIDA-CoNLL
We present a method to automatically identify financially relevant news using stock price movements and news headlines as input.
Named Entity Disambiguation (NED) systems perform well on news articles and other texts covering a specific time interval.
In this work, we discuss the importance of external knowledge for performing Named Entity Recognition (NER).
KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge.
Disambiguating named entities in naturallanguage text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO.
Ranked #12 on Entity Linking on AIDA-CoNLL