scholarly journals K-BERT: Enabling Language Representation with Knowledge Graph

2020 ◽  
Vol 34 (03) ◽  
pp. 2901-2908 ◽  
Author(s):  
Weijie Liu ◽  
Peng Zhou ◽  
Zhe Zhao ◽  
Zhiruo Wang ◽  
Qi Ju ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Model Parameters ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Language Representation ◽  
Finance Law ◽  
Domain Specific Knowledge ◽  
The Impact ◽  
Knowledge Incorporation

Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-specific knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge. However, too much knowledge incorporation may divert the sentence from its correct meaning, which is called knowledge noise (KN) issue. To overcome KN, K-BERT introduces soft-position and visible matrix to limit the impact of knowledge. K-BERT can easily inject domain knowledge into the models by being equipped with a KG without pre-training by itself because it is capable of loading model parameters from the pre-trained BERT. Our investigation reveals promising results in twelve NLP tasks. Especially in domain-specific tasks (including finance, law, and medicine), K-BERT significantly outperforms BERT, which demonstrates that K-BERT is an excellent choice for solving the knowledge-driven problems that require experts.

scholarly journals Cross-Fertilizing Deep Web Analysis and Ontology Enrichment

2017 ◽  
Author(s):  
Marilena Oita ◽  
Antoine Amarilli ◽  
Pierre Senellart
Keyword(s):  
Domain Knowledge ◽  
Deep Web ◽  
Web Pages ◽  
Complete Understanding ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Web Crawlers ◽  
New Perspective ◽  
The Impact

Deep Web databases, whose content is presented as dynamically-generated Web pages hidden behind forms, have mostly been left unindexed by search engine crawlers. In order to automatically explore this mass of information, many current techniques assume the existence of domain knowledge, which is costly to create and maintain. In this article, we present a new perspective on form understanding and deep Web data acquisition that does not require any domain-specific knowledge. Unlike previous approaches, we do not perform the various steps in the process (e.g., form understanding, record identification, attribute labeling) independently but integrate them to achieve a more complete understanding of deep Web sources. Through information extraction techniques and using the form itself for validation, we reconcile input and output schemas in a labeled graph which is further aligned with a generic ontology. The impact of this alignment is threefold: first, the resulting semantic infrastructure associated with the form can assist Web crawlers when probing the form for content indexing; second, attributes of response pages are labeled by matching known ontology instances, and relations between attributes are uncovered; and third, we enrich the generic ontology with facts from the deep Web.

Research on feature-based opinion mining using topic maps

2016 ◽  
Vol 34 (3) ◽  
pp. 435-456 ◽  
Author(s):  
Lixin Xia ◽  
Zhongyi Wang ◽  
Chen Chen ◽  
Shanshan Zhai
Keyword(s):  
Domain Knowledge ◽  
Opinion Mining ◽  
Specific Knowledge ◽  
Content Type ◽  
Topic Maps ◽  
Domain Specific ◽  
Product Features ◽  
Domain Specific Knowledge ◽  
Feature Based ◽  
Integrate Domain

Purpose Opinion mining (OM), also known as “sentiment classification”, which aims to discover common patterns of user opinions from their textual statements automatically or semi-automatically, is not only useful for customers, but also for manufacturers. However, because of the complexity of natural language, there are still some problems, such as domain dependence of sentiment words, extraction of implicit features and others. The purpose of this paper is to propose an OM method based on topic maps to solve these problems. Design/methodology/approach Domain-specific knowledge is key to solve problems in feature-based OM. On the one hand, topic maps, as an ontology framework, are composed of topics, associations, occurrences and scopes, and can represent a class of knowledge representation schemes. On the other hand, compared with ontology, topic maps have many advantages. Thus, it is better to integrate domain-specific knowledge into OM based on topic maps. This method can make full use of the semantic relationships among feature words and sentiment words. Findings In feature-level OM, most of the existing research associate product features and opinions by their explicit co-occurrence, or use syntax parsing to judge the modification relationship between opinion words and product features within a review unit. They are mostly based on the structure of language units without considering domain knowledge. Only few methods based on ontology incorporate domain knowledge into feature-based OM, but they only use the “is-a” relation between concepts. Therefore, this paper proposes feature-based OM using topic maps. The experimental results revealed that this method can improve the accuracy of the OM. The findings of this study not only advance the state of OM research but also shed light on future research directions. Research limitations/implications To demonstrate the “feature-based OM using topic maps” applications, this work implements a prototype that helps users to find their new washing machines. Originality/value This paper presents a new method of feature-based OM using topic maps, which can integrate domain-specific knowledge into feature-based OM effectively. This method can improve the accuracy of the OM greatly. The proposed method can be applied across various application domains, such as e-commerce and e-government.

Workflows Científicos com Apoio de Bases de Conhecimento em Tempo Real

2020 ◽  
Author(s):  
Victor S. Bursztyn ◽  
Jonas Dias ◽  
Marta Mattoso
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Large Scale ◽  
Workflow Engine ◽  
Workflow Execution ◽  
Human In The Loop ◽  
Domain Specific ◽  
Political Sciences ◽  
Provenance Data ◽  
Domain Specific Knowledge

One major challenge in large-scale experiments is the analytical capacity to contrast ongoing results with domain knowledge. We approach this challenge by constructing a domain-specific knowledge base, which is queried during workflow execution. We introduce K-Chiron, an integrated solution that combines a state-of-the-art automatic knowledge base construction (KBC) system to Chiron, a well-established workflow engine. In this work we experiment in the context of Political Sciences to show how KBC may be used to improve human-in-the-loop (HIL) support in scientific experiments. While HIL in traditional domain expert supervision is done offline, in K-Chiron it is done online, i.e. at runtime. We achieve results in less laborious ways, to the point of enabling a breed of experiments that could be unfeasible with traditional HIL. Finally, we show how provenance data could be leveraged with KBC to enable further experimentation in more dynamic settings.

Cross-Disciplinary Collaboration

2020 ◽  
pp. 21-32
Author(s):  
Daphne Leong
Keyword(s):  
Intercultural Communication ◽  
Domain Knowledge ◽  
Mutual Influence ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Cross Domain ◽  
Domain Specific Knowledge ◽  
Epistemic Objects

This chapter describes the things and people that facilitate collaboration across disciplines: shared items, shared objectives, and shared agents. (These concepts draw from literature on collaboration in the sciences and from research on intercultural communication.) Shared items function differently from discipline to discipline, while being identifiable across disciplines. Shared objectives comprise activity objects, the prospective outcomes of collaboration, and epistemic objects, knowledge sought. Shared agents function within and across two or more disciplines. In this book, shared items are represented primarily by scores (and recordings), activity objects by the book’s chapters, epistemic objects by interpretations of pieces and of analysis-performance relations, and shared agents by scholar-performers or performer-scholars. Mechanisms and processes of collaboration are briefly described: strategies for collaborating when views diverge, and degrees of collaborative convergence (working in parallel, translating or mediating knowledge for mutual influence, transforming domain-specific knowledge into new cross-domain knowledge).

Con2KG-A Large-scale Domain-Specific Knowledge Graph

2019 ◽  
Author(s):  
Nidhi Goyal ◽  
Niharika Sachdeva ◽  
Vijay Choudhary ◽  
Rijula Kar ◽  
Ponnurangam Kumaraguru ◽  
...  
Keyword(s):  
Large Scale ◽  
Knowledge Graph ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge

Creation and interaction with large-scale domain-specific knowledge bases

2017 ◽  
Vol 10 (12) ◽  
pp. 1965-1968 ◽  
Author(s):  
S. Bharadwaj ◽  
L. Chiticariu ◽  
M. Danilevsky ◽  
S. Dhingra ◽  
S. Divekar ◽  
...  
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge

The impact of metamemory and domain-specific knowledge on memory performance

1998 ◽  
Vol 13 (1) ◽  
pp. 91-103 ◽  
Author(s):  
Wolfgang Schneider ◽  
Matthias Schlagmüller ◽  
Mechtild Visé
Keyword(s):  
Memory Performance ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
The Impact

scholarly journals Workflows Científicos com Apoio de Bases de Conhecimento em Tempo Real

2018 ◽  
Author(s):  
Victor S. Bursztyn ◽  
Jonas Dias ◽  
Marta Mattoso
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Large Scale ◽  
Workflow Engine ◽  
Workflow Execution ◽  
Human In The Loop ◽  
Domain Specific ◽  
Political Sciences ◽  
Provenance Data ◽  
Domain Specific Knowledge

One major challenge in large-scale experiments is the analytical capacity to contrast ongoing results with domain knowledge. We approach this challenge by constructing a domain-specific knowledge base, which is queried during workflow execution. We introduce K-Chiron, an integrated solution that combines a state-of-the-art automatic knowledge base construction (KBC) system to Chiron, a well-established workflow engine. In this work we experiment in the context of Political Sciences to show how KBC may be used to improve human-in-the-loop (HIL) support in scientific experiments. While HIL in traditional domain expert supervision is done offline, in K-Chiron it is done online, i.e. at runtime. We achieve results in less laborious ways, to the point of enabling a breed of experiments that could be unfeasible with traditional HIL. Finally, we show how provenance data could be leveraged with KBC to enable further experimentation in more dynamic settings.  

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