Machine Learning with and for Semantic Web Knowledge Graphs

Paulheim, Heiko

doi:10.1007/978-3-030-00338-8_5

Cited by 14 publications

(5 citation statements)

References 63 publications

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“…This may require the return of more traditional AI techniques where large amounts of facts are attempted to be captured through the elegance of simple rules and axioms (like a picture that can express more than a thousand words). Therefore, building up meaningful Tboxes on top of existing Knowledge Graphs may be an interesting avenue to investigate (see Töpper et al 2012;Socher et al 2013;Galárraga et al 2013Galárraga et al , 2015Paulheim 2018b).…”

Section: Discussionmentioning

confidence: 99%

Semantic Web Empowered E-Tourism

Angele¹,

Fensel²,

Huaman³

et al. 2020

Handbook of E-Tourism

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Smart speakers such as Alexa and later Google Home have introduced Artificial Intelligence (AI) into millions, soon to be billions of households, making AI an everyday experience. These new communication channels present a new challenge for successful e-Marketing and e-Commerce providers. Data, content, and services are becoming semantically annotated, allowing software agents, socalled bots, to search through the web and understand its content. Nowadays, users typically consult their bot to find, aggregate, and personalize information and to reserve, book, or buy products and services. As a consequence, it is becoming increasingly important for touristic providers of information, products, and services to be prominently visible in these new online channels to ensure their future economic maturity. In our chapter, we survey the methods and tools helping to achieve these goals. The core aim is the development and application of machine-processable (semantic) annotations of content, data, and services, as well as their aggregation in large Knowledge Graphs. It is only through these methods bots are able to answer a question in a knowledgeable way and organize a useful dialogue (Knowledge Graphs in Use A significantly extended and generalized version of this article will appear as D.

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Section: Discussionmentioning

confidence: 99%

Semantic Web Empowered E-Tourism

Angele¹,

Fensel²,

Huaman³

et al. 2020

Handbook of E-Tourism

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“…Very recent work has applied Trans-E graph embeddings to the problem of entity retrieval, and shown consistent but small improvements [15]. However, Trans-E graph embeddings are not a good choice if the graph has 1-to-many, transitive or symmetric relations, which is the case in knowledge graphs [1]. In our research, we also look into improving entity retrieval using graph embeddings, but use the Wikipedia2Vec representation to address these shortcomings.…”

Section: Using Embeddings For Entity Retrievalmentioning

confidence: 96%

Graph-Embedding Empowered Entity Retrieval

Gerritse

Hasibi

Vries

2020

Lecture Notes in Computer Science

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In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings. The paper shows that graph embeddings are useful for entity-oriented search tasks. We demonstrate empirically that encoding information from the knowledge graph into (graph) embeddings contributes to a higher increase in effectiveness of entity retrieval results than using plain word embeddings. We analyze the impact of the accuracy of the entity linker on the overall retrieval effectiveness. Our analysis further deploys the cluster hypothesis to explain the observed advantages of graph embeddings over the more widely used word embeddings, for user tasks involving ranking entities.

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“…A well‐known example of a LOD set is DBpedia, one of the most popular and widely used data sets on the semantic web. In Reference [25], author discusses how machine learning can be integrated into DBpedia to promote an intelligent information system, such as recommender systems. In recent years, many researches 26‐29 have focused on deep learning, a type of machine learning.…”

Section: Related Workmentioning

confidence: 99%

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Hsu

Lin

2020

Softw Pract Exp

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Summary Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.

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Machine Learning with and for Semantic Web Knowledge Graphs

Cited by 14 publications

References 63 publications

Semantic Web Empowered E-Tourism

Semantic Web Empowered E-Tourism

Graph-Embedding Empowered Entity Retrieval

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

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