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Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries
[article]
2015
arXiv
pre-print
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Building such a large-scale multimodal KB presents a major challenge of scalability. ...
We cast a large-scale MRF into a KB representation, incorporating visual, textual and structured data, as well as their diverse relations. ...
A KB can often be viewed as a large-scale graph structure that connects different entities with their relations [38, 58] . ...
arXiv:1507.05670v2
fatcat:fjcydziz6zbknegnvwa6xy6cyu
Enhancing Knowledge Bases with Quantity Facts
2022
Proceedings of the ACM Web Conference 2022
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Prior work on extracting quantity facts from web contents focused on high precision for top-ranked outputs, but did not tackle the KB coverage issue. ...
Experiments with extractions from more than 13 million web documents demonstrate the benefits of our method. ...
CONCLUSION We have presented a method for augmenting knowledge bases with quantity facts extracted from text at large scale. ...
doi:10.1145/3485447.3511932
fatcat:hkyzkbg32zb6po7467kbfw5a7y
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction
[article]
2013
arXiv
pre-print
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This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. ...
We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities ...
Experiments We use the training and test data, evaluation framework and baselines from (Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) . ...
arXiv:1307.7973v1
fatcat:rwctwdnpqnbapjlvmqufgpc5ka
Interactive Knowledge Base Population
[article]
2015
arXiv
pre-print
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Most work on building knowledge bases has focused on collecting entities and facts from as large a collection of documents as possible. ...
Lastly pocket KBs sidestep engineering issues around supporting a large global KB. The tools needed to do AI research on large KBs like Freebase range from cumbersome to lacking. ...
result than building a global KB. ...
arXiv:1506.00301v1
fatcat:jtgxyijndbcvhfz7zr6jdz43j4
DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference
2012
International Workshop on Very Large Data Search
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We describe how we address the scalability challenges to achieve web-scale KBC and the lessons we have learned from building DeepDive. ...
We present an end-to-end (live) demonstration system called DeepDive that performs knowledge-base construction (KBC) from hundreds of millions of web pages. ...
INTRODUCTION Knowledge-base construction (KBC) is the process of populating a knowledge base (KB) with facts (or assertions) extracted from text. ...
dblp:conf/vlds/NiuZRS12
fatcat:okz2hruox5hg5e6vxhvmceoqoy
A Distributional Semantics Approach for Selective Reasoning on Commonsense Graph Knowledge Bases
[chapter]
2014
Lecture Notes in Computer Science
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Tasks such as question answering and semantic search are dependent on the ability of querying & reasoning over large-scale commonsense knowledge bases (KBs). ...
This paper proposes a selective graph navigation mechanism based on a distributional relational semantic model which can be applied to querying & reasoning over heterogeneous knowledge bases (KBs). ...
This research was supported in part by funding from the Science Foundation Ireland under Grant Number SFI/12/RC/2289 (Insight) and by the Irish Research Council. Joao C. ...
doi:10.1007/978-3-319-07983-7_3
fatcat:e4ebguwn5re7rijefhkza6pwqm
Knowledge bases in the age of big data analytics
2014
Proceedings of the VLDB Endowment
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This tutorial gives an overview on state-of-the-art methods for the automatic construction of large knowledge bases and harnessing them for data and text analytics. ...
It covers both big-data methods for building knowledge bases and knowledge bases being assets for big-data applications. The tutorial also points out challenges and research opportunities. ...
There is a large spectrum of methods to extract such facts from Web documents. ...
doi:10.14778/2733004.2733069
fatcat:kt3qjia2wbfctexlmyzs6cnqhq
Big Data Methods for Computational Linguistics
2012
IEEE Data Engineering Bulletin
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Specifically, we show how to build large dictionaries of names and paraphrases for entities and relations, and how these help to disambiguate entity mentions in texts. ...
We demonstrate a virtuous cycle in harvesting knowledge from large data and text collections and leveraging this knowledge in order to improve the annotation and interpretation of language in Web pages ...
Specifically, we show how to build large dictionaries of names and paraphrases for entities and relations, and how these help to disambiguate entity mentions in texts. ...
dblp:journals/debu/WeikumHNSSY12
fatcat:vmtel42ji5dfrebgn3ekt4lude
Entity ranking for descriptive queries
2014
2014 IEEE Spoken Language Technology Workshop (SLT)
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First, we propose a novel method of injecting textual information from web documents to the KB on a large scale. ...
Since the number of web documents can be large, we propose to use keyword extraction and summarization techniques for compactly representing entity-related information. ...
We build different profiles for each entity in the KB, which we regard those profiles as unstructured text. ...
doi:10.1109/slt.2014.7078574
dblp:conf/slt/HongPWH14
fatcat:3mqqk7qr7rg6lht7r77iebt7y4
Dual-enhanced Word Representations Based on Knowledge Base
2018
International Semantic Web Conference
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In this paper, we propose an approach for enhancing word representations twice based on large-scale knowledge bases. ...
They commonly refer to the statistics derived from a large text corpus. Meanwhile, it is proved that the larger the corpus is, the better the model performs in most tasks [2, 4] . ...
Firstly, we take the related words in knowledge base as the additional accurate context in comparison with the large corpus. Afterwards, inspired from Kiela et al. ...
dblp:conf/semweb/HeZZF18
fatcat:porhkmtogjefzdh3tjn2ey3sam
XLORE2: Large-scale Cross-lingual Knowledge Graph Construction and Application
2019
Data Intelligence
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But the number of aligned properties is quite small for such a large-scale KB. ...
To address this issue, XLORE has become the first large-scale cross-lingual KB with a balanced amount of Chinese-English knowledge [4] . ...
XLORE is the first large-scale cross-lingual KB with a balanced amount of Chinese-English knowledge. ...
doi:10.1162/dint_a_00003
dblp:journals/dint/JinLZHLZ19
fatcat:zdcq2gfyirc2ba4s6scxtytsna
Scalable Construction and Reasoning of Massive Knowledge Bases
2018
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts
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In today's information-based society, there is abundant knowledge out there carried in the form of natural language texts (e.g., news articles, social media posts, scientific publications), which spans ...
How to turn such massive and unstructured text data into structured, actionable knowledge for computational machines, and furthermore, how to teach machines learn to reason and complete the extracted knowledge ...
reasoning over large-scale knowledge bases. ...
doi:10.18653/v1/n18-6003
dblp:conf/naacl/RenPW18
fatcat:t57e7rwinjfbbgxzddcerishwi
Approximate and selective reasoning on knowledge graphs: A distributional semantics approach
2015
Data & Knowledge Engineering
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Tasks such as question answering and semantic search are dependent on the ability of querying and reasoning over large-scale commonsense knowledge bases (KBs). ...
This paper proposes a selective graph navigation mechanism based on a distributional relational semantic model which can be applied to querying and reasoning over heterogeneous knowledge bases (KBs). ...
unstructured text is used as a general-purpose large-scale commonsense KB, which complements the knowledge present at the relational KB. ...
doi:10.1016/j.datak.2015.06.010
fatcat:6umcpjnmezfyzpvc53a7nbcvii
A Korean Knowledge Extraction System for Enriching a KBox
2018
International Conference on Computational Linguistics
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The increased demand for structured knowledge has created considerable interest in knowledge extraction from natural language sentences. ...
Thus, it is widely used as an effective way to automatically create labeled data between a large-scale KB and a corpus. ...
However, because even large-scale KBs do not contain all the possible knowledge, the knowledge completion task remains crucial in the NLP field. ...
dblp:conf/coling/NamKKJHC18
fatcat:3hxzfo2tojfw3lvewovupepm3i
Knowledge Graphs Representation for Event-Related E-News Articles
2021
Machine Learning and Knowledge Extraction
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Thus, there is a striking concentration on constructing knowledge graphs by combining the background information related to the subjects in text documents. ...
E-newspaper readers are overloaded with massive texts on e-news articles, and they usually mislead the reader who reads and understands information. ...
Relationship Extraction Relation extraction is a key task of building large-scale KGs automatically by extracting unidentified relations from the source text. ...
doi:10.3390/make3040040
fatcat:hwspn2ma4vh5fn3o4oh3zgwj6m
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