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Register a new userRanking Triples using Entity Links in a Large Web Crawl - The Chicory Triple Scorer at WSDM Cup 2017
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Frank Dorssers
Publication date
2017
fields
Informatics Engineering
and research's language is
English
Authors
Frank Dorssers
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This paper describes the participation of team Chicory in the Triple Ranking Challenge of the WSDM Cup 2017. Our approach deploys a large collection of entity tagged web data to estimate the correctness of the relevance relation expressed by the triples, in combination with a baseline approach using Wikipedia abstracts following [1]. Relevance estimations are drawn from ClueWeb12 annotated by Googles entity linker, available publicly as the FACC1 dataset. Our implementation is automatically generated from a so-called search strategy that specifies declaratively how the input data are combined into a final ranking of triples.
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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 Kendalls tau.
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 ranging from 0 to 7, with 7 being the most relevant and 0 being the least relevant. The task focuses on two such relations: profession and nationality. We built a system which could automatically predict the relevance scores for unseen triples. Our model is primarily a supervised machine learning based one in which we use well-designed features which are used to a make a Logistic Ordinal Regression based classification model. The proposed system achieves an overall accuracy score of 0.73 and Kendalls tau score of 0.36.
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 information about individuals and relations we supplement the data provided as part of the WSDM 2017 Triple Score contest with Wikidata and DBpedia, two large-scale knowledge graphs (KG). Our hypothesis is that any type relation, i.e., a specific profession like actor or scientist, can be described by the set of typical activities of people known to have that type relation. For example, actors are known to star in movies, and scientists are known for their academic affiliations. In a KG, this information is to be found on a properly defined subset of the second-degree neighbors of the type relation. This form of local information can be used as part of a learning algorithm to predict relevance scores for new, unseen triples. When scoring profession and nationality triples our experiments based on this approach result in an accuracy equal to 73% and 78%, respectively. These performance metrics are roughly equivalent or only slightly below the state of the art prior to the present contest. This suggests that our approach can be effective for evaluating facts, despite the skewness in the number of facts per individual mined from KGs.
With the continuous increase of data daily published in knowledge bases across the Web, one of the main issues is regarding information relevance. In most knowledge bases, a triple (i.e., a statement composed by subject, predicate, and object) can be only true or false. However, triples can be assigned a score to have information sorted by relevance. In this work, we describe the participation of the Catsear team in the Triple Scoring Challenge at the WSDM Cup 2017. The Catsear approach scores triples by combining the answers coming from three different sources using a linear regression classifier. We show how our approach achieved an Accuracy2 value of 79.58% and the overall 4th place.
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 Kendalls Tau - 0.35.
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