scholarly journals Using Wikipedia's Big Data for creation of Knowledge Bases

2021 ◽  
pp. 11-18
Author(s):  
Mohamed Minhaj
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Question Answering ◽  
Semantic Information ◽  
Knowledge Bases ◽  
Sources Of Information ◽  
Direct Interpretation ◽  
Efficient Retrieval ◽  
Retrieval Mechanism ◽  
And Storage

Wikipedia is among the most prominent and comprehensive sources of information available on the WWW. However, its unstructured form impedes direct interpretation by machines. Knowledge Base (KB) creation is a line of research that enables interpretation of Wikipedia's concealed knowledge by machines. In light of the efficacy of KBs for the storage and efficient retrieval of semantic information required for powering several IT applications such Question-Answering System, many large-scale knowledge bases have been developed. These KBs have employed different approaches for data curation and storage. The retrieval mechanism facilitated by these KBs is also different. Further, they differ in their depth and breadth of knowledge. This paper endeavours to explicate the process of KB creation using Wikipedia and compare the prominent KBs developed using the big data of Wikipedia.

scholarly journals Large-scale electrophysiology: Acquisition, compression, encryption, and storage of big data

2009 ◽  
Vol 180 (1) ◽  
pp. 185-192 ◽  
Author(s):  
Benjamin H. Brinkmann ◽  
Mark R. Bower ◽  
Keith A. Stengel ◽  
Gregory A. Worrell ◽  
Matt Stead
Keyword(s):  
Big Data ◽  
Large Scale ◽  
And Storage

scholarly journals Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8439
Author(s):  
Shukan Liu ◽  
Ruilin Xu ◽  
Li Duan ◽  
Mingjie Li ◽  
Yiming Liu
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Weighted Graph ◽  
Knowledge Bases ◽  
Language Models ◽  
Main Stream ◽  
Semantic Features ◽  
Open Domain ◽  
Original Graph

The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build a metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then, a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.

ASKNET: CREATING AND EVALUATING LARGE SCALE INTEGRATED SEMANTIC NETWORKS

2008 ◽  
Vol 02 (03) ◽  
pp. 343-364 ◽  
Author(s):  
BRIAN HARRINGTON ◽  
STEPHEN CLARK
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
Semantic Information ◽  
Semantic Network ◽  
Word Sense Disambiguation ◽  
Semantic Networks ◽  
Difficult Problem ◽  
Word Sense

Extracting semantic information from multiple natural language sources and combining that information into a single unified resource is an important and fundamental goal for natural language processing. Large scale resources of this kind can be useful for a wide variety of tasks including question answering, word sense disambiguation and knowledge discovery. A single resource representing the information in multiple documents can provide significantly more semantic information than is available from the documents considered independently. The ASKNet system utilises existing NLP tools and resources, together with spreading activation based techniques, to automatically extract semantic information from a large number of English texts, and combines that information into a large scale semantic network. The initial emphasis of the ASKNet system is on wide-coverage, robustness and speed of construction. In this paper we show how a network consisting of over 1.5 million nodes and 3.5 million edges, more than twice as large as any network currently available, can be created in less than 3 days. Evaluation of large-scale semantic networks is a difficult problem. In order to evaluate ASKNet we have developed a novel evaluation metric based on the notion of a network "core" and employed human evaluators to determine the precision of various components of that core. We have applied this evaluation to networks created from randomly chosen articles used by DUC (Document Understanding Conference). The results are highly promising: almost 80% precision in the semantic core of the networks.

scholarly journals Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents

2021 ◽  
Author(s):  
Shukan Liu ◽  
Ruilin Xu ◽  
Li Duan ◽  
Mingjie Li ◽  
Yiming Liu
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Weighted Graph ◽  
Knowledge Bases ◽  
Language Models ◽  
Main Stream ◽  
Semantic Features ◽  
Open Domain ◽  
Original Graph

The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.

scholarly journals Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning

2020 ◽  
Author(s):  
Yuncheng Hua ◽  
Yuan-Fang Li ◽  
Gholamreza Haffari ◽  
Guilin Qi ◽  
Wei Wu
Keyword(s):  
Knowledge Base ◽  
Large Scale ◽  
Question Answering ◽  
Knowledge Bases ◽  
Retrieval Model ◽  
Test Question ◽  
Weak Supervision ◽  
Meta Learning ◽  
Complex Knowledge ◽  
Complex Question

A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka the programmer-interpreter approach. Use similar training questions to the test question, meta-learning enables the programmer to adapt to unseen questions to tackle potential distributional biases quickly. However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive. In this paper, we present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision, i.e., the system’s performance with respect to the produced answers. To the best of our knowledge, this is the first attempt to train the retrieval model with the programmer jointly. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases. We have released our code at https://github.com/DevinJake/MARL.

scholarly journals Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models

2021 ◽  
Author(s):  
Yuanmeng Yan ◽  
Rumei Li ◽  
Sirui Wang ◽  
Hongzhi Zhang ◽  
Zan Daoguang ◽  
...  
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Knowledge Bases ◽  
Language Models ◽  
Relation Learning

scholarly journals The Knowledge Acquisition Bottleneck Problem in Multilingual Word Sense Disambiguation

2020 ◽  
Author(s):  
Tommaso Pasini
Keyword(s):  
Knowledge Acquisition ◽  
Language Processing ◽  
Large Scale ◽  
Semantic Information ◽  
Word Sense Disambiguation ◽  
Knowledge Bases ◽  
Word Sense ◽  
Future Directions ◽  
Sense Disambiguation ◽  
Made In

Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word in a given context. It lies at the base of Natural Language Processing as it provides semantic information for words. In the last decade, great strides have been made in this field and much effort has been devoted to mitigate the knowledge acquisition bottleneck problem, i.e., the problem of semantically annotating texts at a large scale and in different languages. This issue is ubiquitous in WSD as it hinders the creation of both multilingual knowledge bases and manually-curated training sets. In this work, we first introduce the reader to the task of WSD through a short historical digression and then take the stock of the advancements to alleviate the knowledge acquisition bottleneck problem. In that, we survey the literature on manual, semi-automatic and automatic approaches to create English and multilingual corpora tagged with sense annotations and present a clear overview over supervised models for WSD. Finally, we provide our view over the future directions that we foresee for the field.

scholarly journals The Value of Paraphrase for Knowledge Base Predicates

2020 ◽  
Vol 34 (05) ◽  
pp. 9346-9353
Author(s):  
Bingcong Xue ◽  
Sen Hu ◽  
Lei Zou ◽  
Jiashu Cheng
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
Knowledge Bases ◽  
Human Beings ◽  
High Quality ◽  
Language Generation

Paraphrase, i.e., differing textual realizations of the same meaning, has proven useful for many natural language processing (NLP) applications. Collecting paraphrase for predicates in knowledge bases (KBs) is the key to comprehend the RDF triples in KBs. Existing works have published some paraphrase datasets automatically extracted from large corpora, but have too many redundant pairs or don't cover enough predicates, which cannot be improved by computer only and need the help of human beings. This paper shows a full process of collecting large-scale and high-quality paraphrase dictionaries for predicates in knowledge bases, which takes advantage of existing datasets and combines the technologies of machine mining and crowdsourcing. Our dataset comprises 2284 distinct predicates in DBpedia and 31130 paraphrase pairs in total, the quality of which is a great leap over previous works. Then it is demonstrated that such good paraphrase dictionaries can do great help to natural language processing tasks such as question answering and language generation. We also publish our own dictionary for further research.

scholarly journals Improving Natural Language Inference Using External Knowledge in the Science Questions Domain

2019 ◽  
Vol 33 ◽  
pp. 7208-7215 ◽  
Author(s):  
Xiaoyan Wang ◽  
Pavan Kapanipathi ◽  
Ryan Musa ◽  
Mo Yu ◽  
Kartik Talamadupula ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Improve Performance ◽  
External Knowledge ◽  
Structured Knowledge ◽  
Significant Attention

Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention due to the release of large scale, challenging datasets. Present approaches to the problem largely focus on learning-based methods that use only textual information in order to classify whether a given premise entails, contradicts, or is neutral with respect to a given hypothesis. Surprisingly, the use of methods based on structured knowledge – a central topic in artificial intelligence – has not received much attention vis-a-vis the NLI problem. While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not been well explored. To address this, we present a combination of techniques that harness external knowledge to improve performance on the NLI problem in the science questions domain. We present the results of applying our techniques on text, graph, and text-and-graph based models; and discuss the implications of using external knowledge to solve the NLI problem. Our model achieves close to state-of-the-art performance for NLI on the SciTail science questions dataset.

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