An Entity Relatedness Test Dataset

Herrera, Jose; Casanova, Marco A.; Nunes, Bernardo Pereira; Leme, Luiz André P. Paes; Lopes, Giseli Rabello

doi:10.1007/978-3-319-68204-4_20

Cited by 8 publications

(33 citation statements)

References 9 publications

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“…In [Talavera Herrera 2017], some experiments were executed to evaluate a family of path search strategies (Table 1) against a benchmark [Herrera et al 2017] from the music and movies domains, and a baseline, RECAP [Pirrò 2015]. For each domain, the benchmark contains 20 pairs of entities, each with a ranked list with 50 relationship paths based on information about their entities found in IMDb and last.fm, and on information about their properties, computed from DBpedia.…”

Section: Discussionmentioning

confidence: 99%

“…All experiments were carried out over DBpedia through the OpenLink Virtuoso SPARQL protocol endpoint. We selected 10 entity pairs from the Entity Relatedness Test Dataset [Herrera et al 2017], 5 pairs for each domain (music and movies), shown in Table 2.…”

Section: Discussionmentioning

confidence: 99%

“…The authors compared and measured the effectiveness of different search strategies through user experiments. [Herrera et al 2017] introduced a benchmark to evaluate path ranking measures.…”

Section: Related Workmentioning

confidence: 99%

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CoEPinKB: A Framework to Understand the Connectivity of Entity Pairs in Knowledge Bases

Jiménez

Leme²,

Casanova

2021

Anais Do XLVIII Seminário Integrado De Software E Hardware (SEMISH 2021)

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A knowledge base, expressed using the Resource Description Framework (RDF), can be viewed as a graph whose nodes represent entities and whose edges denote relationships. The entity relatedness problem refers to the problem of discovering and understanding how two entities are related, directly or indirectly, that is, how they are connected by paths in a knowledge base. Strategies designed to solve the entity relatedness problem typically adopt an entity similarity measure to reduce the path search space and a path ranking measure to order and filter the list of paths returned. This paper presents a framework, called CoEPinKB, that supports the empirical evaluation of such strategies. The proposed framework allows combining entity similarity and path ranking measures to generate different path search strategies. The main goals of this paper are to describe the framework and present a performance evaluation of nine different path search strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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CoEPinKB: A Framework to Understand the Connectivity of Entity Pairs in Knowledge Bases

Jiménez

Leme²,

Casanova

2021

Anais Do XLVIII Seminário Integrado De Software E Hardware (SEMISH 2021)

Self Cite

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“…Datasets. We perform experiments on two widely used datasets for evaluating entity relatedness, KORE [13] and ERT [12], and extract the datasets containing associative Wikipedia articles and Web pages from search engines. To generate D q of each query, we extract the associative texts of all query entities in the two datasets from search engines and Wikipedia.…”

Section: Experiments Settingsmentioning

confidence: 99%

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

Yue

Liang

et al. 2021

Database Systems for Advanced Applications

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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.

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“…Additionally, the dataset creation process described in chapter 6 resembles the method used in (Herrera et al 2017) in that both studies exploit linked data graph structures. Conclusions and Future Work…”

Section: 4summarymentioning

confidence: 99%

Exploring RDF Knowledge Bases Through Serendipity Patterns

EICHLER¹

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Serendipity is defined as the discovery of a thing when one is not searching for it. In other words, serendipity means the discovery of information that provides valuable insights by unveiling unanticipated knowledge. The topic is receiving increased attention in the literature, since the precision requirement may be justifiably relaxed in order to improve user satisfaction. A field that can benefit from serendipity is the Web of Data, an immense global data space where data is publicly available. As more and more data become available in this data space, searching and extracting relevant information becomes a challenging task. This thesis contributes to addressing this challenge in two ways. First, it presents a query orchestration process that introduces three strategies to inject serendipity patterns in the query process. The serendipity patterns are inspired by basic characteristics of serendipitous events, such as, analogy and disturbance, and can be used for augmenting the results with additional information, suggesting alternative queries or rebalancing the results. Second, it introduces a benchmark dataset that can be used to compare different approaches for locating serendipitous content. The strategy adopted for constructing the dataset consists of dividing the dataset into partitions based on a global feature and linking entities from different partitions according to the number of paths they share.

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An Entity Relatedness Test Dataset

Cited by 8 publications

References 9 publications

CoEPinKB: A Framework to Understand the Connectivity of Entity Pairs in Knowledge Bases

CoEPinKB: A Framework to Understand the Connectivity of Entity Pairs in Knowledge Bases

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

Exploring RDF Knowledge Bases Through Serendipity Patterns

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