Fast Algorithms for Semantic Association Search and Pattern Mining

Cheng, Gong; Liu, Daxin; Qu, Yuzhong

doi:10.1109/tkde.2019.2942031

Cited by 11 publications

(13 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The compactness of an ERG can be measured by its diameter. For a set of input entities, Cheng et al [2019] presented an algorithm named OptimSearch which efficiently searches a KG and finds ERGs that connect all the input entities and do not exceed a predefined diameter bound. In brief, Op-timSearch performs pruned searches starting from each input entity and merges paths having a common end vertex into an ERG.…”

Section: Problem Formulationmentioning

confidence: 99%

“…A Knowledge Graph (KG) is a graph where vertices are entities interconnected with relations and annotated with types and attributes [Arenas et al, 2016]. Increasingly many KGs have been developed for various domains and applications [Kharlamov et al, 2017a;Kharlamov et al, 2017b]. An important application is document enrichment where words or phrases in a document are linked to entities in a given background KG, and then the KG is leveraged to help readers better comprehend the document with entity types [Tonon et al, 2016] and direct relations between pairs of entities [Gunaratna et al, 2017].…”

Section: Introductionmentioning

confidence: 99%

“…As illustrated in Fig. 1, an ERG in a KG connects three companies mentioned in a news article and usefully shows how they are indirectly related via an intermediate company [Huang et al, 2019].…”

Section: Introductionmentioning

confidence: 99%

“…Challenges. There exist algorithms for searching and ranking ERGs to connect a given set of entities [Tong and Faloutsos, 2006;Kasneci et al, 2009a;Kasneci et al, 2009b;Chen et al, 2011;Cheng et al, 2016;Cheng et al, 2019], but directly using them for document * Contact Author enrichment faces two challenges. (1) To properly enrich a document, an ERG should be representative-covering all or the salient entities mentioned in the document.…”

Section: Introductionmentioning

confidence: 99%

“…In the first step, we achieve a trade-off between representativeness and compactness by computing the most salient subset of entities mentioned in the document such that compact ERGs connecting these entities are guaranteed to exist in the KG. Then we easily find all such compact ERGs by performing an existing search algorithm [Cheng et al, 2019]. In the second step, we rank these ERGs by their relevance and return the top-ranked result.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Enriching Documents with Compact, Representative, Relevant Knowledge Graphs

Huang

Cheng

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

A prominent application of knowledge graph (KG) is document enrichment. Existing methods identify mentions of entities in a background KG and enrich documents with entity types and direct relations. We compute an entity relation subgraph (ERG) that can more expressively represent indirect relations among a set of mentioned entities. To find compact, representative, and relevant ERGs for effective enrichment, we propose an efficient best-first search algorithm to solve a new combinatorial optimization problem that achieves a trade-off between representativeness and compactness, and then we exploit ontological knowledge to rank ERGs by entity-based document-KG and intra-KG relevance. Extensive experiments and user studies show the promising performance of our approach.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enriching Documents with Compact, Representative, Relevant Knowledge Graphs

Huang

Cheng

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Graphs

Cheng

Zhang

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Ranking Associative Entities in Knowledge Graph by Graphical Modeling of Frequent Patterns

Yue

Liang

et al. 2021

Database Systems for Advanced Applications

View full text Add to dashboard Cite

Ranking associative entities in Knowledge Graph (KG) is critical for entity-oriented tasks like entity recommendation and associative inference. Existing methods benefit from explicit linkages in KG w.r.t. exactly two query entities via the closely appearing co-occurrences. Given a query including one or more entities in KG, it is necessary to obtain the implicit associative entities and uncover the strength of associations from data. To this end, we leverage KG with Web resources and propose an approach to ranking associative entities based on frequent pattern mining and graph embedding. First, we construct an entity dependency graph from the frequent patterns of entities generated from both KG and Web resources. Thus, the existence and strength of associations between entities could be depicted effectively in a holistic way. Second, we embed the dependency graph into a lower-dimensional space and consequently fulfill entity ranking on the embedding. Finally, we conduct an extensive experimental study on real-life datasets, and verify the effectiveness of our proposed approach compared to competitive baselines.

show abstract

Fast Algorithms for Semantic Association Search and Pattern Mining

Cited by 11 publications

References 32 publications

Enriching Documents with Compact, Representative, Relevant Knowledge Graphs

Enriching Documents with Compact, Representative, Relevant Knowledge Graphs

Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Graphs

Ranking Associative Entities in Knowledge Graph by Graphical Modeling of Frequent Patterns

Contact Info

Product

Resources

About