scholarly journals Knowledge graph mining for realty domain using dependency parsing and QAT models

2021 ◽  
Vol 193 ◽  
pp. 32-41
Author(s):  
Alexander Zamiralov ◽  
Timur Sohin ◽  
Nikolay Butakov
Keyword(s):  
Graph Mining ◽  
Knowledge Graph ◽  
Dependency Parsing

scholarly journals Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding

2019 ◽  
Vol 1 (3) ◽  
pp. 238-270 ◽  
Author(s):  
Lei Ji ◽  
Yujing Wang ◽  
Botian Shi ◽  
Dawei Zhang ◽  
Zhongyuan Wang ◽  
...  
Keyword(s):  
Graph Mining ◽  
Knowledge Graph ◽  
Web Pages ◽  
Search Query ◽  
Short Text ◽  
Text Understanding ◽  
Concept Space ◽  
Semantic Concepts ◽  
Table Understanding ◽  
The Web

Knowlege is important for text-related applications. In this paper, we introduce Microsoft Concept Graph, a knowledge graph engine that provides concept tagging APIs to facilitate the understanding of human languages. Microsoft Concept Graph is built upon Probase, a universal probabilistic taxonomy consisting of instances and concepts mined from the Web. We start by introducing the construction of the knowledge graph through iterative semantic extraction and taxonomy construction procedures, which extract 2.7 million concepts from 1.68 billion Web pages. We then use conceptualization models to represent text in the concept space to empower text-related applications, such as topic search, query recommendation, Web table understanding and Ads relevance. Since the release in 2016, Microsoft Concept Graph has received more than 100,000 pageviews, 2 million API calls and 3,000 registered downloads from 50,000 visitors over 64 countries.

scholarly journals DOMAIN SPECIFIC KEY FEATURE EXTRACTION USING KNOWLEDGE GRAPH MINING

2020 ◽  
Vol 15 ◽  
pp. 1-22
Author(s):  
Mohit Kumar Barai ◽  
◽  
Subhasis Sanyal ◽  
Keyword(s):  
Feature Extraction ◽  
Language Processing ◽  
Graph Mining ◽  
Text Processing ◽  
Weighted Graph ◽  
Knowledge Graph ◽  
Specific Data ◽  
Domain Specific ◽  
Extraction Algorithm ◽  
Processing Product

In the field of text mining, many novel feature extraction approaches have been propounded. The following research paper is based on a novel feature extraction algorithm. In this paper, to formulate this approach, a weighted graph mining has been used to ensure the effectiveness of the feature extraction and computational efficiency; only the most effective graphs representing the maximum number of triangles based on a predefined relational criterion have been considered. The proposed novel technique is an amalgamation of the relation between words surrounding an aspect of the product and the lexicon-based connection among those words, which creates a relational triangle. A maximum number of a triangle covering an element has been accounted as a prime feature. The proposed algorithm performs more than three times better than TF-IDF within a limited set of data in analysis based on domain-specific data. Keywords: feature extraction, natural language processing, product review, text processing, knowledge graph.

scholarly journals Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Emmanuel Bresso ◽  
Pierre Monnin ◽  
Cédric Bousquet ◽  
François-Elie Calvier ◽  
Ndeye-Coumba Ndiaye ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Drug Targets ◽  
Graph Mining ◽  
Molecular Mechanisms ◽  
Randomized Clinical Trials ◽  
Knowledge Graph ◽  
Drug Induced ◽  
Liver Injuries ◽  
Explainable Ai ◽  
Knowledge Graphs

Abstract Background Adverse drug reactions (ADRs) are statistically characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. This is true even for hepatic or skin toxicities, which are classically monitored during drug design. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs, such as their properties, interactions, or involvements in pathways. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established. Methods We propose in this paper to mine knowledge graphs for identifying biomolecular features that may enable automatically reproducing expert classifications that distinguish drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models, which explain the classification itself, but may also provide elements of explanation for molecular mechanisms behind ADRs. In summary, (1) we mine a knowledge graph for features; (2) we train classifiers at distinguishing, on the basis of extracted features, drugs associated or not with two commonly monitored ADRs: drug-induced liver injuries (DILI) and severe cutaneous adverse reactions (SCAR); (3) we isolate features that are both efficient in reproducing expert classifications and interpretable by experts (i.e., Gene Ontology terms, drug targets, or pathway names); and (4) we manually evaluate in a mini-study how they may be explanatory. Results Extracted features reproduce with a good fidelity classifications of drugs causative or not for DILI and SCAR (Accuracy = 0.74 and 0.81, respectively). Experts fully agreed that 73% and 38% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 90% and 77% of them. Conclusion Knowledge graphs provide sufficiently diverse features to enable simple and explainable models to distinguish between drugs that are causative or not for ADRs. In addition to explaining classifications, most discriminative features appear to be good candidates for investigating ADR mechanisms further.

scholarly journals Rule Reduction after Knowledge Graph Mining for Cyber Situational Awareness Analysis

2020 ◽  
Vol 176 ◽  
pp. 22-30
Author(s):  
Bin Liu ◽  
Xixi Zhu ◽  
Junfeng Wu ◽  
Li Yao
Keyword(s):  
Graph Mining ◽  
Situational Awareness ◽  
Knowledge Graph
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