The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data

Färber, Michael

doi:10.1007/978-3-030-30796-7_8

Cited by 94 publications

(77 citation statements)

References 23 publications

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“…In the future, the KG could be enriched with scholarly data (e.g. as in [ 3 ]) for covering also the research output of the institution. Connections with External Systems.…”

Section: Supporting and Ongoing Activitiesmentioning

confidence: 99%

How Can a University Take Its First Steps in Open Data?

Tzitzikas

Pitikakis

Giakoumis

et al. 2021

Metadata and Semantic Research

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Every university in Greece is obliged to comply with the national legal framework on open data. The rising question is how such a big and diverse organization could support open data from an administrative, legal and technical point of view, in a way that enables gradual improvement of the open data-related services. In this paper, we describe our experience, as University of Crete, for tackling these requirements. In particular, (a) we detail the steps of the process that we followed, (b) we show how an Open Data Catalog can be exploited also in the first steps of this process, (c) we describe the platform that we selected, how we organized the catalog and the metadata selection, (d) we describe extensions that were required, and (e) we discuss the current status, performance indicators, and possible next steps.

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“…In the future, the KG could be enriched with scholarly data (e.g. as in [ 3 ]) for covering also the research output of the institution. Connections with External Systems.…”

Section: Supporting and Ongoing Activitiesmentioning

confidence: 99%

How Can a University Take Its First Steps in Open Data?

Tzitzikas

Pitikakis

Giakoumis

et al. 2021

Metadata and Semantic Research

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“…MAKG * The Microsoft Academic Knowledge Graph (MAKG) [4] is an RDF translation of the Microsoft Academic Graph-a heterogeneous dataset of scientific publication records, citations, authors, institutions, journals, conferences, and fields of study. The original MAKG dataset contains 8 billion triples and includes links to datasets in the Linked Open Data Cloud.…”

Section: Lubm-8kmentioning

confidence: 99%

Streaming Partitioning of RDF Graphs for Datalog Reasoning

2021

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A cluster of servers is often used to reason over RDF graphs whose size exceeds the capacity of a single server. While many distributed approaches to reasoning have been proposed, the problem of data partitioning has received little attention thus far. In practice, data is usually partitioned by a variant of hashing, which is very simple, but it does not pay attention to data locality. Locality-aware partitioning approaches have been considered, but they usually process the entire dataset on a single server. In this paper, we present two new RDF partitioning strategies. Both are inspired by recent streaming graph partitioning algorithms [18,16], which partition a graph while keeping only a small subset of the graph in memory. We have evaluated our approaches empirically against hash and min-cut partitioning. Our results suggest that our approaches can significantly improve reasoning performance, but without unrealistic demands on the memory of the servers used for partitioning.

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“…In our case, the author's information had no strong influence on the model performance. This can be explained by the fact that we used a different database than Ebensu et al The author metadata in our MAG dataset is noisy to some extent, and author names may have been incomplete or duplicated [6]. Cleaning the MAG data and thus providing more accurate recommendations is considered…”

Section: Offline Evaluationmentioning

confidence: 99%

C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations

Färber

Zinecker

Cartus

et al. 2021

Companion Proceedings of the Web Conference 2021

Self Cite

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Finding suitable citations for scientific publications can be challenging and time-consuming. To this end, context-aware citation recommendation approaches that recommend publications as candidates for in-text citations have been developed. In this paper, we present C-Rex, a web-based demonstration system available at http://c-rex.org for context-aware citation recommendation based on the Neural Citation Network [5] and millions of publications from the Microsoft Academic Graph. Our system is one of the first online context-aware citation recommendation systems and the first to incorporate not only a deep learning recommendation approach, but also explanation components to help users better understand why papers were recommended. In our offline evaluation, our model performs similarly to the one presented in the original paper and can serve as a basic framework for further implementations. In our online evaluation, we found that the explanations of recommendations increased users' satisfaction.

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The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data

Cited by 94 publications

References 23 publications

How Can a University Take Its First Steps in Open Data?

How Can a University Take Its First Steps in Open Data?

Streaming Partitioning of RDF Graphs for Datalog Reasoning

C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations

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