Motif-Based Hyponym Relation Extraction from Wikipedia Hyperlinks

Wei, Bifan; Li, Jun; Ma, Jian; Zhang, Wei; Bao, Feng

doi:10.1109/tkde.2013.183

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…A conjunctive query on trees and relations can find sentences to satisfy specific semantic structure by relations (e.g. hyponym relations [37]) and corpora trees [41].…”

Section: Queries In Computational Linguisticsmentioning

confidence: 99%

Cross-Model Conjunctive Queries over Relation and Tree-Structured Data

Chen

Uotila

et al. 2022

Database Systems for Advanced Applications

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Conjunctive queries are the most basic and central class of database queries. With the continued growth of demands to manage and process the massive volume of different types of data, there is little research to study the conjunctive queries between relation and tree data. In this paper, we study of Cross-Model Conjunctive Queries (CMCQs) over relation and tree-structured data (XML and JSON). To efficiently process CMCQs with bounded intermediate results, we first encode tree nodes with position information. With tree node original label values and encoded position values, it allows our proposed algorithm CMJoin to join relations and tree data simultaneously, avoiding massive intermediate results. CMJoin achieves worst-case optimality in terms of the total result of label values and encoded position values. Experimental results demonstrate the efficiency and scalability of the proposed techniques to answer a CMCQ in terms of running time and intermediate result size.

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“…A conjunctive query on trees and relations can find sentences to satisfy specific semantic structure by relations (e.g. hyponym relations [37]) and corpora trees [41].…”

Section: Queries In Computational Linguisticsmentioning

confidence: 99%

Cross-Model Conjunctive Queries over Relation and Tree-Structured Data

Chen

Uotila

et al. 2022

Database Systems for Advanced Applications

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“…As shown in Fig. 8, Randgraf-4-2 has 2, 2, 2, 4, 2, and 6 ways to sample a 4-node CIS s including v in orbits 6,9,10,12,13, and 14 respectively. Each way happens with probability β…”

Section: Proof Of Theoremmentioning

confidence: 99%

“…In fact, the graphlet orbit degree signature has been successfully used for protein function prediction [2] and cancer gene identification [3] by identifying groups (or clusters) of topologically similar nodes in biological networks. In addition to biological networks, graphlet orbit degree is also used for link prediction [4] and node classification [5] in online social networks, and hyponym relation extraction from Wikipedia hyperlinks [6].…”

Section: Introductionmentioning

confidence: 99%

SNOD: a fast sampling method of exploring node orbit degrees for large graphs

et al. 2018

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Exploring small connected and induced subgraph patterns (CIS patterns, or graphlets) has recently attracted considerable attention. Despite recent efforts on computing the number of instances a specific graphlet appears in a large graph (i.e., the total number of CISes isomorphic to the graphlet), little attention has been paid to characterizing a node's graphlet degree, i.e., the number of CISes isomorphic to the graphlet that include the node, which is an important metric for analyzing complex networks such as social and biological networks. Similar to global graphlet counting, it is challenging to compute node graphlet degrees for a large graph due to the combinatorial nature of the problem. Unfortunately, previous methods of computing global graphlet counts are not suited to solve this problem. In this paper we propose sampling methods to estimate node graphlet degrees for undirected and directed graphs, and analyze the error of our estimates. To the best of our knowledge, we are the first to study this problem and give a fast scalable solution. We conduct experiments on a variety of real-word datasets that demonstrate that our methods accurately and efficiently estimate node graphlet degrees for graphs with millions of edges.

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“…ii) The accuracy of free-text taxonomies is usually lower than many Wikipedia-based taxonomies because it is difficult to extract knowledge completely from texts; iii) The task of taxonomy learning is still insufficiently studied a) in emerging and specific domains and b) for non-English or underresourced languages (Wei et al, 2014;Alfarone and Davis, 2015;.…”

Section: Introductionmentioning

confidence: 99%

A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances

Wang¹,

He²,

Zhou³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations. While a large number of taxonomies have been constructed from human-compiled resources (e.g., Wikipedia), learning taxonomies from text corpora has received a growing interest and is essential for longtailed and domain-specific knowledge acquisition. In this paper, we overview recent advances on taxonomy construction from free texts, reorganizing relevant subtasks into a complete framework. We also overview resources for evaluation and discuss challenges for future research.

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Motif-Based Hyponym Relation Extraction from Wikipedia Hyperlinks

Cited by 8 publications

References 20 publications

Cross-Model Conjunctive Queries over Relation and Tree-Structured Data

Cross-Model Conjunctive Queries over Relation and Tree-Structured Data

SNOD: a fast sampling method of exploring node orbit degrees for large graphs

A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances

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