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Too Much Information: Can AI Cope with Modern Knowledge Graphs?

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Formal Concept Analysis (ICFCA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11511))

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Abstract

Knowledge graphs play an important role in artificial intelligence (AI) applications – especially in personal assistants, question answering, and semantic search – and public knowledge bases like Wikidata are widely used in industry and research. However, modern AI includes many different techniques, including machine learning, data mining, natural language processing, which are often not able to use knowledge graphs in their full size and complexity. Feature engineering, sampling, and simplification are needed, and commonly achieved with custom preprocessing code. In this position paper, we argue that a more principled integrated approach to this task is possible using declarative methods from knowledge representation and reasoning. In particular, we suggest that modern rule-based systems are a promising platform for computing customised views on knowledge graphs, and for integrating the results of other AI methods back into the overall knowledge model.

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Notes

  1. 1.

    The company’s Google Knowledge Graph is the origin of the term.

  2. 2.

    Crawling and extracting such content is a difficult task, and a worthy research area in itself, yet the main work of knowledge gathering remains that of the human author.

  3. 3.

    SQL supports recursive views that resemble the expressivity of linear Datalog, but the standard forbids the use of duplicate elimination (DISTINCT) in their construction, making them quite useless for breadth-first search on graphs that may contain cycles.

  4. 4.

    https://grafana.wikimedia.org/dashboard/db/wikidata-query-service.

  5. 5.

    Data complexity characterises the worst-case asymptotic complexity of the reasoning problem for a fixed logical theory (i.e., MARPL rule set) with respect to the size of the input data (KG).

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Acknowledgements

This work is partly supported by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in project number 389792660 (TRR 248, Center for Perspicuous Systems), CRC 912 (Highly Adaptive Energy-Efficient Computing, HAEC), and Emmy Noether grant KR 4381/1-1.

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  1. TU Dresden, Dresden, Germany

    Markus Krötzsch

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Correspondence to Markus Krötzsch .

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  1. Babeș-Bolyai University, Cluj-Napoca, Romania

    Diana Cristea

  2. University of Strasbourg, ENGEES, Strasbourg, France

    Florence Le Ber

  3. Frankfurt University of Applied Sciences, Frankfurt, Germany

    Baris Sertkaya

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Krötzsch, M. (2019). Too Much Information: Can AI Cope with Modern Knowledge Graphs?. In: Cristea, D., Le Ber, F., Sertkaya, B. (eds) Formal Concept Analysis. ICFCA 2019. Lecture Notes in Computer Science(), vol 11511. Springer, Cham. https://doi.org/10.1007/978-3-030-21462-3_2

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