A Review of Relational Machine Learning for Knowledge Graphs

Abstract Objectives Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. Methods New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus. Results Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph. Conclusion Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.

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Swift Logic for Big Data and Knowledge Graphs

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/1 ◽

2017 ◽

Cited By ~ 13

Author(s):

Luigi Bellomarini ◽

Georg Gottlob ◽

Andreas Pieris ◽

Emanuel Sallinger

Keyword(s):

Machine Learning ◽

Big Data ◽

Computational Complexity ◽

Management System ◽

Knowledge Graph ◽

Complex Reasoning ◽

Knowledge Graphs ◽

Reasoning Tasks ◽

Graph Management ◽

The Web

Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve efficient and scalable reasoning over Big Data with an acceptable computational complexity. Moreover, a KGMS needs interfaces to corporate databases, the web, and machine-learning and analytics packages. We present KRR formalisms and a system achieving these goals.

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RDFFrames: knowledge graph access for machine learning tools

The VLDB Journal ◽

10.1007/s00778-021-00690-5 ◽

2021 ◽

Author(s):

Aisha Mohamed ◽

Ghadeer Abuoda ◽

Abdurrahman Ghanem ◽

Zoi Kaoudi ◽

Ashraf Aboulnaga

Keyword(s):

Machine Learning ◽

Database Systems ◽

Database System ◽

Sparql Query ◽

Knowledge Graph ◽

Learning Tools ◽

Tabular Format ◽

Knowledge Graphs ◽

Rdf Database ◽

Learning Software

AbstractKnowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.

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KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

10.1101/2020.08.17.254839 ◽

2020 ◽

Author(s):

Justin Reese ◽

Deepak Unni ◽

Tiffany J. Callahan ◽

Luca Cappelletti ◽

Vida Ravanmehr ◽

...

Keyword(s):

Machine Learning ◽

User Interface ◽

Learning Task ◽

Research Community ◽

Biological Knowledge ◽

Biomedical Data ◽

Effective Response ◽

Learning Tasks ◽

Flexible Framework ◽

Knowledge Graphs

SUMMARYIntegrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.BIGGER PICTUREAn effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.

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Special Issue on Machine Learning and Knowledge Graphs

Future Generation Computer Systems ◽

10.1016/j.future.2021.11.022 ◽

2021 ◽

Author(s):

Mehwish Alam ◽

Anna Fensel ◽

Jorge Martinez-Gil ◽

Bernhard Moser ◽

Diego Reforgiato Recupero ◽

...

Keyword(s):

Machine Learning ◽

Special Issue ◽

Knowledge Graphs

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