User-centric pattern mining on knowledge graphs: An archaeological case study

Wilcke, W.X.; Boer, Victor de; Kleijn, M.T.M. de; Harmelen, Frank van; Scholten, H.J.

doi:10.1016/j.websem.2018.12.004

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…Similar approaches are used in bioinformatics for new drug prediction or disease response (Zhu et al 2022). Perhaps a similar workflow, using data from Open Context, tDAR, or the Archaeological Data Service could serve as a model here (a pipeline for working with knowledge graphs that uses as an example Dutch linked open-data protocols for archaeological materials is discussed in Wilcke et al 2019). Simple statements of knowledge can lead to entirely new perspectives.…”

Section: Discussionmentioning

confidence: 99%

Relationship Prediction in a Knowledge Graph Embedding Model of the Illicit Antiquities Trade

Graham,

Yates,

El-Roby

et al. 2023

Adv. archaeol. pract.

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The transnational networks of the illicit and illegal antiquities trade are hard to perceive. We suggest representing the trade as a knowledge graph with multiple kinds of relationships that can be transformed by a neural architecture into a “knowledge graph embedding model.” The result is that the vectorization of the knowledge represented in the graph can be queried for missing “knowledge” of the trade by virtue of the various entities’ proximity in the multidimensional embedding space. In this article, we build a knowledge graph about the antiquities trade using a semantic annotation tool, drawing on the series of articles in the Trafficking Culture Project's online encyclopedia. We then use the AmpliGraph package, a series of tools for supervised machine learning (Costabello et al. 2019) to turn the graph into a knowledge graph embedding model. We query the model to predict new hypotheses and to cluster actors in the trade. The model suggests connections between actors and institutions hitherto unsuspected and not otherwise present in the original knowledge graph. This approach could hold enormous potential for illuminating the hidden corners of the illicit antiquities trade. The same method could be applied to other kinds of archaeological knowledge.

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Section: Discussionmentioning

confidence: 99%

Relationship Prediction in a Knowledge Graph Embedding Model of the Illicit Antiquities Trade

Graham,

Yates,

El-Roby

et al. 2023

Adv. archaeol. pract.

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show abstract

“…In other domains this has been researched extensively, but in archaeology it is quite rare. One example of pattern mining is the work by Wilcke et al (2019), but they worked with hand-created XML (eXtensible Markup Language) data, not extracted entities. Their results are perhaps a bit lacking, but this might be partly due to the small amount of data.…”

Section: Named Entity Recognitionmentioning

confidence: 99%

Information Extraction and Machine Learning for Archaeological Texts

Brandsen

2023

Quantitative Archaeology and Archaeological Modelling

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Archaeologists are creating ever-increasing amounts of textual data. So much in fact, that manual reading and inspection has become practically impossible. By leveraging computational approaches, it is possible to extract relevant information from this big data, allowing for more efficient research and new analyses. In this chapter, methods and techniques to extract information from archaeological texts through Machine Learning are introduced and discussed, with a focus on practical examples. After reading the chapter, you should have a clear grasp on the possibilities of text mining in archaeology, the current state of research, and enough information to start your own text analyses.

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“…The Pattern-following Knowledge Graphs have been constructed depending on various patterns that are followed by the Knowledge Graphs. The Parser-based Knowledge Graphs have been built with different parsing techniques that are followed by the Knowledge Graphs [109][110][111].…”

Section: Rule-based Knowledge Graphmentioning

confidence: 99%

A Systematic Review on Knowledge Graphs Classiﬁcation and Their Various Usages

Mst. Mim Akter,

Rahoman

2023

Artificial Intelligence Evolution

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A Knowledge Graph is a directive graph where the nodes state the entities and the edges describe the relationships between the entities of data. It is also referred to as a Semantic Network. The massive amount of data generated every day can be transformed into knowledge via knowledge graphs for the effective use of these data. Knowing the classification of Knowledge Graphs is required to adapt to different requirements of Knowledge Graphs. Knowledge Graphs are primarily classified concerning their building techniques and their usages. In building techniques, it is considered how the Knowledge Graph is built. For example, the graph can be constructed as a triplet, quadruplet, etc., or created from structured data, e.g., database, or unstructured data, e.g., text, image, etc. On the other hand, Knowledge Graphs can be used for various purposes. For example, Knowledge Graphs can be used for Information Retrieval, Semantic Query, etc., or different types of data visualization. Nowadays, Knowledge Graph is one of the trending topics in the modern technology-dependent world. However, clear and specific discussions on the classifications of Knowledge Graphs and their various usages are less available. In this paper, we will describe the classification of knowledge graphs and their various usages in detail so that the readers can get a clear concept of this topic.

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User-centric pattern mining on knowledge graphs: An archaeological case study

Cited by 9 publications

References 21 publications

Relationship Prediction in a Knowledge Graph Embedding Model of the Illicit Antiquities Trade

Relationship Prediction in a Knowledge Graph Embedding Model of the Illicit Antiquities Trade

Information Extraction and Machine Learning for Archaeological Texts

A Systematic Review on Knowledge Graphs Classiﬁcation and Their Various Usages

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