112 research outputs found
Predicting wikipedia infobox type information using word embeddings on categories
Wikipedia has emerged as the largest multilingual, web based general reference work on the Internet. A huge amount of human resources have been invested in the creation and update of Wikipedia articles which are ideally complemented by so-called infobox templates defining the type of the underlying article. It has been observed that the Wikipedia infobox type information is often incomplete and inconsistent due to various reasons. However, the Wikipedia infobox type information plays a fundamental role for the RDF type information of Wikipedia based Knowledge Graphs such as DBpedia. This stimulates the need of always having the correct and complete infobox type information. In this work, we propose an approach to predict Wikipedia infobox types by using word embeddings on categories of Wikipedia articles, and analyze the impact of using minimal information from the Wikipedia articles in the prediction process
Entity Type Prediction in Knowledge Graphs using Embeddings
Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized
as the backbone of diverse applications in the field of data mining and
information retrieval. Hence, the completeness and correctness of the Knowledge
Graphs (KGs) are vital. Most of these KGs are mostly created either via an
automated information extraction from Wikipedia snapshots or information
accumulation provided by the users or using heuristics. However, it has been
observed that the type information of these KGs is often noisy, incomplete, and
incorrect. To deal with this problem a multi-label classification approach is
proposed in this work for entity typing using KG embeddings. We compare our
approach with the current state-of-the-art type prediction method and report on
experiments with the KGs
InfoSync: Information Synchronization across Multilingual Semi-structured Tables
Information Synchronization of semi-structured data across languages is
challenging. For instance, Wikipedia tables in one language should be
synchronized across languages. To address this problem, we introduce a new
dataset InfoSyncC and a two-step method for tabular synchronization. InfoSync
contains 100K entity-centric tables (Wikipedia Infoboxes) across 14 languages,
of which a subset (3.5K pairs) are manually annotated. The proposed method
includes 1) Information Alignment to map rows and 2) Information Update for
updating missing/outdated information for aligned tables across multilingual
tables. When evaluated on InfoSync, information alignment achieves an F1 score
of 87.91 (en non-en). To evaluate information updation, we perform
human-assisted Wikipedia edits on Infoboxes for 603 table pairs. Our approach
obtains an acceptance rate of 77.28% on Wikipedia, showing the effectiveness of
the proposed method.Comment: 22 pages, 7 figures, 20 tables, ACL 2023 (Toronto, Canada
Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models
In this paper, we propose a table and image generation task to verify how the
knowledge about entities acquired from natural language is retained in Vision &
Language (V & L) models. This task consists of two parts: the first is to
generate a table containing knowledge about an entity and its related image,
and the second is to generate an image from an entity with a caption and a
table containing related knowledge of the entity. In both tasks, the model must
know the entities used to perform the generation properly. We created the
Wikipedia Table and Image Generation (WikiTIG) dataset from about 200,000
infoboxes in English Wikipedia articles to perform the proposed tasks. We
evaluated the performance on the tasks with respect to the above research
question using the V & L model OFA, which has achieved state-of-the-art results
in multiple tasks. Experimental results show that OFA forgets part of its
entity knowledge by pre-training as a complement to improve the performance of
image related tasks.Comment: Accepted at ACL 202
Natural language generation from structured data
Generování přirozeného jazyka je jedna z nejtěžších úloh strojového učení. Jejím cílem je obvykle prezentovat informaci původně uloženou ve strukturované podobě. V této práci implementuji a zkoumám systém založený na principu jazykového modelu, který generuje věty v přirozeném jazyce z dat uložených v tabulce.Natural language generation is on of the hardest tasks of machine learning. Usually, the task is to convey some information stored in structured form. In this work, we implement and test a system based on a neural language model which attempts to generate natural language sentences from data contained in a table
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