NOUS: Construction and Querying of Dynamic Knowledge Graphs

Choudhury, Sutanay; Agarwal, Khushbu; Purohit, Sumit; Zhang, Baichuan; Pirrung, Meg; Smith, Will; Thomas, Mathew

doi:10.1109/icde.2017.228

Cited by 35 publications

(18 citation statements)

References 11 publications

(10 reference statements)

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“…Here for the computational convenience, we set Re (A, D) as A 2 F + D 2 F . Such pairwise ranking loss objective is in the similar spirit to the Bayesian Personalized Ranking [8,29], which aims to predict the interaction between users and items in recommender system domain.…”

Section: Model Formulationmentioning

confidence: 99%

“…In both tables, the rows correspond to the name references and the columns (2 to 12) stand for various methods. e competing methods are grouped 8 We use the code from h ps://github.com/aditya-grover/node2vec 9 logically. e first group includes the baseline methods that we have designed such as random predictor (Rand) and methods using low-dimensional factorization of author-list for clustering.…”

Section: Experimental Setting and Implementationmentioning

confidence: 99%

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Name Disambiguation in Anonymized Graphs using Network Embedding

Zhang

Hasan

2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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104

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In real-world, our DNA is unique but many people share names.is phenomenon o en causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper a ribution of credit or blame in digital forensic. To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing solutions to this task substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable due to the risk of privacy violation. In this work, we propose a novel name disambiguation method. Our proposed method is non-intrusive of privacy because instead of using a ributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs. In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical agglomerative clustering algorithm. Our experimental results demonstrate that the proposed method is significantly be er than the existing name disambiguation methods working in a similar se ing.

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Section: Model Formulationmentioning

confidence: 99%

Section: Experimental Setting and Implementationmentioning

confidence: 99%

Name Disambiguation in Anonymized Graphs using Network Embedding

Zhang

Hasan

2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Self Cite

104

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show abstract

“…Only those nodes which are accessible because of the query, are explored by the graph database engine. Because every record is handled individually, it drastically boosts the query performance and helps in reducing resource cost of the query results [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Financial Knowledge Graph Based Financial Report Query System

et al. 2021

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Annual Financial Reports are the core in the Banking Sector to publish its financial statistics. Extracting useful information from these complex and lengthy reports involves manual process to resolve the financial queries, resulting in delays and ambiguity in investment decisions. One of the major reasons is the lack of any standardization in the format and vocabulary used in the reports. An automated system for resolution of intelligent financial queries is therefore difficult to design.Several works have been proposed to overcome these problems using Information Extraction; however, they do not address the semantic interoperability of the reports across different institutions. This work proposed an automated querying engine to answer the financial queries using Ontology based Information Extraction. For Semantic modeling of financial reports, a Financial Knowledge Graph, assisted by Financial Ontology, has been proposed. The nodes are populated with entities, while links are populated with relationships using Information Extraction applied on annual reports. Two benefits have been provided by this system to stakeholders through automation: decision making through queries and generation of custom financial stories. The work can further be extended to other domains including healthcare and academia where physical reports are used for communication.

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“…Knowledge graph can construct a huge network with concepts (nodes) and the relations (edges) among concepts. It can links scattered knowledge to form a massive knowledge network [10]. Knowledge graph are very useful in knowledge retrieval, questionanswering, knowledge recommendation and other applications.…”

Section: Introductionmentioning

confidence: 99%

KnowHealth: A Knowledge Graph based Question-Answer Platform for Elderly People

Hang²,

Chu

et al. 2019

Proceedings of the 12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019,

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Nowadays, population aging has become a prominent problem all over the world, which brings new challenge to manage multi-type and multi-relation health data from elderly. Existing approaches store those complex data in traditional relational database, which lack support of relation retrieval and is hard to answer elderly's semantic query. In this paper, we design and implement a knowledge graph based question-answer platform (KnowHealth) to manage those health data, which can help the elderly know their health condition better, Specifically, we propose an ontology definition which can describes the entities and relations in aged disease domain. Based on the ontology, we crawl and extract health related information from different input sources. Then, entities and relation extraction methods can be used to construct a knowledge graph. Finally, we completed a historical behavior driven question-answering platform to serve query for elderly. By analyzing and extending intention of questions, answer can be retrieved and reasoned more accurately.

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NOUS: Construction and Querying of Dynamic Knowledge Graphs

Cited by 35 publications

References 11 publications

Name Disambiguation in Anonymized Graphs using Network Embedding

Name Disambiguation in Anonymized Graphs using Network Embedding

Financial Knowledge Graph Based Financial Report Query System

KnowHealth: A Knowledge Graph based Question-Answer Platform for Elderly People

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