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Predicting Relevance Scores for Triples from Type-Like Relations using Neural Embedding - The Cabbage Triple Scorer at WSDM Cup 2017

2017-12-22 · Brumer Yael Ben-Gurion University of the Negev, Shapira Bracha Ben-Gurion University of the Negev, Rokach Lior Ben-Gurion University of the Negev, Barkan Oren Tel Aviv University

The WSDM Cup 2017 Triple scoring challenge is aimed at calculating and assigning relevance scores for triples from type-like relations. Such scores are a fundamental ingredient for ranking results in entity search. In this paper, we propose a method that uses neural embedding techniques to accurately calculate an entity score for a triple based on its nearest neighbor. We strive to develop a new latent semantic model with a deep structure that captures the semantic and syntactic relations between words. Our method has been ranked among the top performers with accuracy - 0.74, average score difference - 1.74, and average Kendall's Tau - 0.35.

📄 PDF Abstract BibTeX arXiv:1712.08359

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Relevance Scoring of Triples Using Ordinal Logistic Classification - The Celosia Triple Scorer at WSDM Cup 2017

2017-12-22 · Fatma Nausheen IIIT Hyderabad, Chinnakotla Manoj K. Microsoft, Shrivastava Manish IIIT Hyderabad

In this paper, we report our participation in the Task 2: Triple Scoring of WSDM Cup challenge 2017. In this task, we were provided with triples of "type-like" relations which were given human-annotated relevance scores …

Task 2

RelSifter: Scoring Triples from Type-like Relations - The Samphire Triple Scorer at WSDM Cup 2017

2017-12-22 · Shiralkar Prashant Indiana University Bloomington, Avram Mihai Indiana University Bloomington, Ciampaglia Giovanni Luca Indiana University Bloomington, Menczer Filippo Indiana University Bloomington 외

We present RelSifter, a supervised learning approach to the problem of assigning relevance scores to triples expressing type-like relations such as 'profession' and 'nationality.' To provide additional contextual informa…

Knowledge Graphs Relation

Integrating Knowledge from Latent and Explicit Features for Triple Scoring - Team Radicchio's Triple Scorer at WSDM Cup 2017

2017-12-22 · Chen Liang-Wei University of Illinois at Urbana-Champaign, Mangipudi Bhargav University of Illinois at Urbana-Champaign, Bandlamudi Jayachandu University of Illinois at Urbana-Champaign, Sehgal Richa University of Illinois at Urbana-Champaign 외

The objective of the triple scoring task in WSDM Cup 2017 is to compute relevance scores for knowledge-base triples of type-like relations. For example, consider Julius Caesar who has had various professions, including P…

Information Retrieval Retrieval

Proceedings of the WSDM Cup 2017: Vandalism Detection and Triple Scoring

2017-12-27 · Potthast Martin, Heindorf Stefan, Bast Hannah

The WSDM Cup 2017 was a data mining challenge held in conjunction with the 10th International Conference on Web Search and Data Mining (WSDM). It addressed key challenges of knowledge bases today: quality assurance and e…

Overview of the Triple Scoring Task at the WSDM Cup 2017

2017-12-21 · Bast Hannah, Buchhold Björn, Haussmann Elmar

This paper provides an overview of the triple scoring task at the WSDM Cup 2017, including a description of the task and the dataset, an overview of the participating teams and their results, and a brief account of the m…

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