A Tutorial on Leveraging Knowledge Graphs for Web Search

2016 ◽  
pp. 24-37
Author(s):  
Gianluca Demartini
Keyword(s):  
Web Search ◽  
Knowledge Graphs

scholarly journals CAREA: Cotraining Attribute and Relation Embeddings for Cross-Lingual Entity Alignment in Knowledge Graphs

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Baiyang Chen ◽  
Xiaoliang Chen ◽  
Peng Lu ◽  
Yajun Du
Keyword(s):  
Artificial Intelligence ◽  
Web Search ◽  
Threshold Value ◽  
Dynamic Threshold ◽  
Absolute Increase ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Cross Lingual ◽  
Application Fields ◽  
Entity Relationships

Knowledge graphs (KGs) are one of the most widely used techniques of knowledge organizations and have been extensively used in many application fields related to artificial intelligence, for example, web search and recommendations. Entity alignment provides a useful tool for how to integrate multilingual KGs automatically. However, most of the existing studies evaluated ignore the abundant information of entity attributes except for entity relationships. This paper sets out to investigate cross-lingual entity alignment and proposes an iterative cotraining approach (CAREA) to train a pair of independent models. The two models can extract the attribute and the relation features of multilingual KGs, respectively. In each iteration, the two models alternate to predict a new set of potentially aligned entity pairs. Besides, this method further filters through the dynamic threshold value to enhance the two models’ supervision. Experimental results on three real-world datasets demonstrate the effectiveness and superiority of the proposed method. The CAREA model improves the performance with at least an absolute increase of 3.9 % across all experiment datasets. The code is available at https://github.com/ChenBaiyang/CAREA.

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.

Suicide Prevention Through Online Gatekeeping Using Search Advertising Techniques

Crisis ◽  
2015 ◽  
Vol 36 (4) ◽  
pp. 267-273 ◽  
Author(s):  
Hajime Sueki ◽  
Jiro Ito
Keyword(s):  
Suicidal Ideation ◽  
Suicide Prevention ◽  
Help Seeking ◽  
Service Use ◽  
Web Search ◽  
Suicide Attempts ◽  
Consultation Service ◽  
Search Advertising ◽  
Internet Users ◽  
History Of

Abstract. Background: Nurturing gatekeepers is an effective suicide prevention strategy. Internet-based methods to screen those at high risk of suicide have been developed in recent years but have not been used for online gatekeeping. Aims: A preliminary study was conducted to examine the feasibility and effects of online gatekeeping. Method: Advertisements to promote e-mail psychological consultation service use among Internet users were placed on web pages identified by searches using suicide-related keywords. We replied to all emails received between July and December 2013 and analyzed their contents. Results: A total of 139 consultation service users were analyzed. The mean age was 23.8 years (SD = 9.7), and female users accounted for 80% of the sample. Suicidal ideation was present in 74.1%, and 12.2% had a history of suicide attempts. After consultation, positive changes in mood were observed in 10.8%, 16.5% showed intentions to seek help from new supporters, and 10.1% of all 139 users actually took help-seeking actions. Conclusion: Online gatekeeping to prevent suicide by placing advertisements on web search pages to promote consultation service use among Internet users with suicidal ideation may be feasible.

A Novel Personalized Web Search with Offline Capability

2012 ◽  
Vol 3 (5) ◽  
pp. 243-245
Author(s):  
Roy T P Roy T P ◽  
◽  
Ginnu George
Keyword(s):  
Web Search

Classification of means and methods of the Web semantic retrieval

2017 ◽  
pp. 030-050
Author(s):  
J.V. Rogushina ◽  
Keyword(s):  
Search Engines ◽  
Domain Knowledge ◽  
Information Needs ◽  
Web Search ◽  
User Interaction ◽  
Query Languages ◽  
Semantic Search ◽  
Semantic Retrieval ◽  
The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

Review of Improvement of Web Search Based on Web Log File

2012 ◽  
Vol 3 (2) ◽  
pp. 298-300 ◽  
Author(s):  
Soniya P. Chaudhari ◽  
Prof. Hitesh Gupta ◽  
S. J. Patil
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Web Search ◽  
Pattern Mining ◽  
Efficiency Improvement ◽  
Web Searching ◽  
Important Method ◽  
Web Log ◽  
Journal Paper ◽  
Log File

In this paper we review various research of journal paper as Web Searching efficiency improvement. Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network

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