Comparing Query Similarity Measures for Collaborative Web Search

2008 ◽  
Author(s):  
Pavel Krömer ◽  
Václav Snášel ◽  
Jan Platoš
Keyword(s):  
Web Search ◽  
Similarity Measures ◽  
Query Similarity

Semantic Similarity Measures in the Biomedical Domain by Leveraging a Web Search Engine

2013 ◽  
Vol 17 (4) ◽  
pp. 853-861 ◽  
Author(s):  
Sheau-Ling Hsieh ◽  
Wen-Yung Chang ◽  
Chi-Huang Chen ◽  
Yung-Ching Weng
Keyword(s):  
Semantic Similarity ◽  
Search Engine ◽  
Web Search ◽  
Similarity Measures ◽  
Biomedical Domain ◽  
Web Search Engine

scholarly journals RoleSim*: Scaling axiomatic role-based similarity ranking on large graphs

2021 ◽  
Author(s):  
Weiren Yu ◽  
Sima Iranmanesh ◽  
Aparajita Haldar ◽  
Maoyin Zhang ◽  
Hakan Ferhatosmanoglu
Keyword(s):  
Web Search ◽  
Similarity Measures ◽  
Computational Time ◽  
Pairwise Similarity ◽  
Large Graphs ◽  
Triangular Inequality ◽  
Graph Theoretic ◽  
Role Based ◽  
Automorphic Equivalence ◽  
Similarity Information

AbstractRoleSim and SimRank are among the popular graph-theoretic similarity measures with many applications in, e.g., web search, collaborative filtering, and sociometry. While RoleSim addresses the automorphic (role) equivalence of pairwise similarity which SimRank lacks, it ignores the neighboring similarity information out of the automorphically equivalent set. Consequently, two pairs of nodes, which are not automorphically equivalent by nature, cannot be well distinguished by RoleSim if the averages of their neighboring similarities over the automorphically equivalent set are the same. To alleviate this problem: 1) We propose a novel similarity model, namely RoleSim*, which accurately evaluates pairwise role similarities in a more comprehensive manner. RoleSim* not only guarantees the automorphic equivalence that SimRank lacks, but also takes into account the neighboring similarity information outside the automorphically equivalent sets that are overlooked by RoleSim. 2) We prove the existence and uniqueness of the RoleSim* solution, and show its three axiomatic properties (i.e., symmetry, boundedness, and non-increasing monotonicity). 3) We provide a concise bound for iteratively computing RoleSim* formula, and estimate the number of iterations required to attain a desired accuracy. 4) We induce a distance metric based on RoleSim* similarity, and show that the RoleSim* metric fulfills the triangular inequality, which implies the sum-transitivity of its similarity scores. 5) We present a threshold-based RoleSim* model that reduces the computational time further with provable accuracy guarantee. 6) We propose a single-source RoleSim* model, which scales well for sizable graphs. 7) We also devise methods to scale RoleSim* based search by incorporating its triangular inequality property with partitioning techniques. Our experimental results on real datasets demonstrate that RoleSim* achieves higher accuracy than its competitors while scaling well on sizable graphs with billions of edges.

scholarly journals Automatic Acquisition of Similarity between Entities by Using Web Search Engine

2012 ◽  
pp. 293-296
Author(s):  
C. Aiswarya ◽  
R. Lakshmi ◽  
R. kotteswari
Keyword(s):  
Web Mining ◽  
Clustering Algorithm ◽  
Web Search ◽  
Similarity Measures ◽  
Relation Extraction ◽  
Web Based ◽  
Metadata Extraction ◽  
User Query ◽  
Benchmark Datasets ◽  
Automatic Acquisition

Web mining is the application of data mining technology to discover patterns from the web. The various tasks on web such as relation extraction, community mining, document clustering and automatic metadata extraction. A previously proposed web-based semantic similarity measures on three benchmark datasets showing high correlation with human rating. One of the main problems in information retrieval is to retrieve a set of documents that is semantically related to given user query. We propose an automatic acquisition method to estimate semantic relation between two words by using pattern extraction algorithm and sequential clustering algorithm.

scholarly journals Search Engine-inspired Ranking Algorithm for Trading Networks

2018 ◽  
Vol 9 (3) ◽  
pp. 812
Author(s):  
Andri Mirzal
Keyword(s):  
United Nations ◽  
Web Search ◽  
Similarity Measures ◽  
Ranking Algorithm ◽  
Link Structure ◽  
Ranking Algorithms ◽  
International Trading ◽  
Trading Network ◽  
Trading Networks

<p>Ranking algorithms based on link structure of the network are well-known methods in web search engines to improve the quality of the searches. The most famous ones are PageRank and HITS. PageRank uses probability of random surfers to visit a page as the score of that page, and HITS instead of produces one score, proposes using two scores, authority and hub scores, where the authority scores describe the degree of popularity of pages and hub scores describe the quality of hyperlinks on pages. In this paper, we show the differences between WWW network and trading network, and use these differences to create a ranking algorithm for trading networks. We test our proposed method with international trading data from United Nations. The similarity measures between vectors of proposed algorithm and vector of standard measure give promising results.</p>

scholarly journals On the Usefulness of SQL-Query-Similarity Measures to Find User Interests

2020 ◽  
Vol 32 (10) ◽  
pp. 1982-1999
Author(s):  
Natalia Arzamasova ◽  
Klemens Bohm ◽  
Bertrand Goldman ◽  
Christian Saaler ◽  
Martin Schaler
Keyword(s):  
Similarity Measures ◽  
User Interests ◽  
Query Similarity ◽  
Sql Query

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.

Sign in / Sign up

Export Citation Format

Share Document