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Supervised Ranking of Triples for Type-Like Relations - The Cress Triple Scorer at the WSDM Cup 2017

2017-12-22 · Hasibi Faegheh NTNU Trondheim, Garigliotti Darío University of Stavanger, Zhang Shuo University of Stavanger, Balog Krisztian University of Stavanger

This paper describes our participation in the Triple Scoring task of WSDM Cup 2017, which aims at ranking triples from a knowledge base for two type-like relations: profession and nationality. We introduce a supervised ranking method along with the features we designed for this task. Our system has been top ranked with respect to average score difference and 2nd best in terms of Kendall's tau.

📄 PDF Abstract BibTeX arXiv:1712.08354

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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 th…

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

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

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

Cross-lingual Alignment of Knowledge Graph Triples with Sentences

2021-12-01 · ICON 2021 12 · Swayatta Daw, Shivprasad Sagare, Tushar Abhishek, Vikram Pudi 외

The pairing of natural language sentences with knowledge graph triples is essential for many downstream tasks like data-to-text generation, facts extraction from sentences (semantic parsing), knowledge graph completion, …

Data-to-Text Generation Knowledge Graph Completion NER Semantic Parsing+4