Industry-scale Knowledge Graphs: Lessons and Challenges

Noy, Natalya F.; Gao, Yuqing; Jain, Anshu; Narayanan, Anant; Patterson, Alan; Taylor, Jessica

doi:10.1145/3329781.3332266

Cited by 197 publications

(146 citation statements)

References 3 publications

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“…Knowledge graphs are closely associated with the Semantic Web, introduced in 2001 [14], which is an extended form of the Web where information is made more structured and meaningful. Knowledge graphs are recently gaining more attention from the industry [15], e.g., Google, Facebook, and Microsoft, thanks to their compact representation for factual (product) knowledge. Aside from industry, knowledge graphs can be leveraged for other domains, such as geography, government, life sciences, and many more [16].…”

Section: Knowledge Graphs and Wikidatamentioning

confidence: 99%

COVIWD: COVID-19 Wikidata Dashboard

Darari

2021

Jurnal Ilmu Komputer dan Informasi

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COVID-19 (short for coronavirus disease 2019) is an emerging infectious disease that has had a tremendous impact on our daily lives. Globally, there have been over 95 million cases of COVID-19 and 2 million deaths across 191 countries and regions. The rapid spread and severity of COVID-19 call for a monitoring dashboard that can be developed quickly in an adaptable manner. Wikidata is a free, collaborative knowledge graph, collecting structured data about various themes, including that of COVID-19. We present COVIWD, a COVID-19 Wikidata dashboard, which provides a one-stop information/visualization service for topics related to COVID-19, ranging from symptoms and risk factors to comparison of cases and deaths among countries. The dashboard is one of the first that leverages open knowledge graph technologies, namely, RDF (for data modeling) and SPARQL (for querying), to give a live, concise snapshot of the COVID-19 pandemic. The use of both RDF and SPARQL enables rapid and flexible application development. COVIWD is available at http://coviwd.org.

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Section: Knowledge Graphs and Wikidatamentioning

confidence: 99%

COVIWD: COVID-19 Wikidata Dashboard

Darari

2021

Jurnal Ilmu Komputer dan Informasi

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“…This data is often stored in various databases or file systems while information can be retrieved using query languages and information retrieval techniques. During the past decade, several companies have started taking up knowledge graphs (KG) [10], as a way to represent heterogeneous data and make it useful for a large variety of applications [14]. To make said data accessible, various querying languages like SPARQL and Cypher have been developed.…”

Section: Introductionmentioning

confidence: 99%

Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification

Bakel

Aleksiev

Daza

et al. 2021

Lecture Notes in Computer Science

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Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.Structured querying on such incomplete graphs will result in incomplete sets of answers, even if the correct entities exist in the graph, since one or more edges needed to match the pattern are missing. To overcome this problem, several algorithms for approximate structured query answering have been proposed. Inspired by modern Information Retrieval metrics, these algorithms produce a ranking of all entities in the graph, and their performance is further evaluated based on how high in this ranking the correct answers appear.In this work we take a critical look at this way of evaluation. We argue that performing a ranking-based evaluation is not sufficient to assess methods for complex query answering. To solve this, we introduce Message Passing Query Boxes (MPQB), which takes binary classification metrics back into use and shows the effect this has on the recently proposed query embedding method MPQE.

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“…It will be more scalable if any number of query entities are allowed and multiple Web resources could be introduced. By using linked Web resources, explicit associations could be found easily to enhance Web applications like search engines [21]. However, implicit associations between entities show usefulness in many domains including national security and biomedical research [5].…”

Section: Introductionmentioning

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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Industry-scale Knowledge Graphs: Lessons and Challenges

Cited by 197 publications

References 3 publications

COVIWD: COVID-19 Wikidata Dashboard

COVIWD: COVID-19 Wikidata Dashboard

Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification

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

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