scholarly journals Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks

2017 ◽  
Author(s):  
Jizhou Huang ◽  
Wei Zhang ◽  
Shiqi Zhao ◽  
Shiqiang Ding ◽  
Haifeng Wang
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Training Data ◽  
Data Annotation ◽  
Web Search Engine ◽  
Clickthrough Data ◽  
Ranking Model ◽  
Knowledge Graphs ◽  
Entity Relationships

Providing a plausible explanation for the relationship between two related entities is an important task in some applications of knowledge graphs, such as in search engines. However, most existing methods require a large number of manually labeled training data, which cannot be applied in large-scale knowledge graphs due to the expensive data annotation. In addition, these methods typically rely on costly handcrafted features. In this paper, we propose an effective pairwise ranking model by leveraging clickthrough data of a Web search engine to address these two problems. We first construct large-scale training data by leveraging the query-title pairs derived from clickthrough data of a Web search engine. Then, we build a pairwise ranking model which employs a convolutional neural network to automatically learn relevant features. The proposed model can be easily trained with backpropagation to perform the ranking task. The experiments show that our method significantly outperforms several strong baselines.

scholarly journals Test-framework: performance profiling and testing web search engine on non factoid queries

2019 ◽  
Vol 14 (3) ◽  
pp. 1373
Author(s):  
Althaf Ali A ◽  
Mahammad Shafi R
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Expectation Maximization Algorithm ◽  
Training Data ◽  
Test Cases ◽  
Retrieval Method ◽  
Performance Profiling ◽  
Query Result ◽  
Prediction Technique

<p>Performance profiling and testing is one of the interesting topics in the big data management and Cloud Computing. In testing, we use test cases composed to different type of queries to evaluate the performance aspects of the information retrieval system for large scale information collection. This test scenarioperforms the evaluation ofretrieval accuracy for all kind of ambiguity and non factoid queries with result set as Training data. This stands difficult to evaluate the retrieval method in order to schedule or optimize the Recommendation and prediction technique of the IR method to the Real time queries. The Queries is considered as requirement specification which has to supply to search engine or web information provider applications for information or web page retrieval. In this paper, we propose a novel technique named as “Test Retrieval Framework“a performance profiling and testing of the web search engines on the information retrieved towards non factoid queries. In this technique, we apply expectation maximization algorithm as an iterative method to find maximum likelihood estimate.We discuss on the important aspects in this work based on Recommendation models integrating domain and web usage, Query optimization for navigational and Transactional queries, Query Result records.The Experimental results demonstrates the proposed technique outperforms of state of arts approaches in terms of set based measures like Precision, Recall and F measure and rank based measures like Mean Average Precision and Cumulative Gain.</p>

scholarly journals Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 156
Author(s):  
Abdullah Bin Shams ◽  
Ehsanul Hoque Apu ◽  
Ashiqur Rahman ◽  
Md. Mohsin Sarker Raihan ◽  
Nazeeba Siddika ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Real Time ◽  
Search Engine ◽  
Language Processing ◽  
Web Search ◽  
Training Data ◽  
Small Data ◽  
Web Search Engine ◽  
User Query

Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues.

Reprint of: The anatomy of a large-scale hypertextual web search engine

2012 ◽  
Vol 56 (18) ◽  
pp. 3825-3833 ◽  
Author(s):  
Sergey Brin ◽  
Lawrence Page
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Web Search Engine

The anatomy of a large-scale hypertextual Web search engine

1998 ◽  
Vol 30 (1-7) ◽  
pp. 107-117 ◽  
Author(s):  
Sergey Brin ◽  
Lawrence Page
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Web Search Engine

scholarly journals Improving Entity Recommendation with Search Log and Multi-Task Learning

2018 ◽  
Author(s):  
Jizhou Huang ◽  
Wei Zhang ◽  
Yaming Sun ◽  
Haifeng Wang ◽  
Ting Liu
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Context Information ◽  
Context Aware ◽  
Time Step ◽  
Current Time ◽  
Task Learning ◽  
Web Search Engine ◽  
Search Logs

Entity recommendation, providing search users with an improved experience by assisting them in finding related entities for a given query, has become an indispensable feature of today's Web search engine. Existing studies typically only consider the query issued at the current time step while ignoring the in-session preceding queries. Thus, they typically fail to handle the ambiguous queries such as "apple" because the model could not understand which apple (company or fruit) is talked about. In this work, we believe that the in-session contexts convey valuable evidences that could facilitate the semantic modeling of queries, and take that into consideration for entity recommendation. Furthermore, in order to better model the semantics of queries, we learn the model in a multi-task learning setting where the query representation is shared across entity recommendation and context-aware ranking. We evaluate our approach using large-scale, real-world search logs of a widely used commercial Web search engine. The experimental results show that incorporating context information significantly improves entity recommendation, and learning the model in a multi-task learning setting could bring further improvements.

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