Enabling access to Linked Media with SPARQL-MM

Kurz, Thomas; Schlegel, Kai; Kosch, Harald

doi:10.1145/2740908.2742914

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…Other proposed extensions of SPARQL target specific domains or types of applications, including tSPARQL [Hartig 2009], which allows for specifying and processing trust annotations in terms of which results can be trusted and why; SciS-PARQL [Andrejev and Risch 2012], which provides primitives to deal with numeric arrays of (scientific) information; SPARQL-MM [Kurz et al 2015], which proposes user-defined functions to help when querying meta-data about multimedia artefacts; GeoSPARQL [Perry and Herring 2012;Battle and Kolas 2012], stSPARQL [Koubarakis and Kyzirakos 2010] and SPARQL-ST [Perry et al 2011], which propose extensions to support spatial and temporal queries; EP-SPARQL [Anicic et al 2011], C-SPARQL [Barbieri et al 2010] and Streaming SPARQL [Bolles et al 2008], which deal with processing dynamic information and support, offering event processing, reasoning and querying over windows of streaming data, and so forth.…”

Section: A4 Further Extensionsmentioning

confidence: 99%

Foundations of Modern Query Languages for Graph Databases

et al. 2017

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We survey foundational features underlying modern graph query languages. We first discuss two popular graph data models: edge-labelled graphs, where nodes are connected by directed, labelled edges; and property graphs, where nodes and edges can further have attributes. Next we discuss the two most fundamental graph querying functionalities: graph patterns and navigational expressions. We start with graph patterns, in which a graph-structured query is matched against the data. Thereafter we discuss navigational expressions, in which patterns can be matched recursively against the graph to navigate paths of arbitrary length; we give an overview of what kinds of expressions have been proposed, and how they can be combined with graph patterns. We also discuss several semantics under which queries using the previous features can be evaluated, what effects the selection of features and semantics has on complexity, and offer examples of such features in three modern languages that are used to query graphs: SPARQL, Cypher and Gremlin. We conclude by discussing the importance of formalisation for graph query languages; a summary of what is known about SPARQL, Cypher and Gremlin in terms of expressivity and complexity; and an outline of possible future directions for the area.

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Section: A4 Further Extensionsmentioning

confidence: 99%

Foundations of Modern Query Languages for Graph Databases

et al. 2017

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“…The value of connecting media to its semantic metadata is limited to the provided access methods specialised for media assets and fragments. Hence, the proposed model conforms with the recently issued SPARQL-MM specification [15]. SPARQL-MM extends SPARQL, the standard query language for the Semantic Web with media specific concepts and functions to unify the access to media results.…”

Section: Model Queryingmentioning

confidence: 83%

A platform for contextual multimedia data

Schlegel

Berndl

Kosch

et al. 2015

Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business

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Whereas the former Web mostly consisted of information represented in textual documents, nowadays the Web includes a huge number of multimedia documents like videos, photos, and audio. This enormous increase in volume in the private, and above all in the industry sector, makes it more and more di cult to find relevant information. Besides the pure management of multimedia documents, finding hidden semantics and interconnections of heterogeneous cross-media content is a crucial task and stays mostly untouched. To overcome this tendency we see the need for a generic cross-media analysis platform, ranging from extracting relevant features from media objects over representing and publishing extraction results to integrated querying of aggregated findings. In this paper we propose the underlying foundation for a common and contextual multimedia platform in terms of an unified model for publishing multimedia analysis results. The proposed model is based on existing ontologies, adapted and extended to the cross-media environment. Besides the introduction of the already mentioned platform and model, this paper also briefly introduces specific use-case applications as well as possibilities to query the persisted data.

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“…Xu et al [67] present Video Structural Description (VSD) technology for discovering semantic concepts in the video with no CEP focus. SPARQL-MM [38] defines events in terms of spatial(point, line, shape) and temporal(instant, interval) thing for linked video. Works like Object Relation Network (ORN) [16] and CogVis [22] propose a guide ontology to recognize the scene in an image while VEKG is used for modeling videos.…”

Section: Multimedia Event Representationmentioning

confidence: 99%

Knowledge Graph Driven Approach to Represent Video Streams for Spatiotemporal Event Pattern Matching in Complex Event Processing

Yadav¹,

Salwala²,

Curry³

2020

Preprint

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Complex Event Processing (CEP) is an event processing paradigm to perform real-time analytics over streaming data and match high-level event patterns. Presently, CEP is limited to process structured data stream. Video streams are complicated due to their unstructured data model and limit CEP systems to perform matching over them. This work introduces a graph-based structure for continuous evolving video streams, which enables the CEP system to query complex video event patterns. We propose the Video Event Knowledge Graph (VEKG), a graph driven representation of video data. VEKG models video objects as nodes and their relationship interaction as edges over time and space. It creates a semantic knowledge representation of video data derived from the detection of high-level semantic concepts from the video using an ensemble of deep learning models. A CEP-based state optimization -VEKG-Time Aggregated Graph (VEKG-TAG) is proposed over VEKG representation for faster event detection. VEKG-TAG is a spatiotemporal graph aggregation method that provides a summarized view of the VEKG graph over a given time length. We defined a set of nine event pattern rules for two domains (Activity Recognition and Traffic Management), which act as a query and applied over VEKG graphs to discover complex event patterns. To show the efficacy of our approach, we performed extensive experiments over 801 video clips across 10 datasets. The proposed VEKG approach was compared with other state-of-the-art methods and was able to detect complex event patterns over videos with F-Score ranging from 0.44 to 0.90. In the given experiments, the optimized VEKG-TAG was able to reduce 99% and 93% of VEKG nodes and

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Enabling access to Linked Media with SPARQL-MM

Cited by 10 publications

References 19 publications

Foundations of Modern Query Languages for Graph Databases

Foundations of Modern Query Languages for Graph Databases

A platform for contextual multimedia data

Knowledge Graph Driven Approach to Represent Video Streams for Spatiotemporal Event Pattern Matching in Complex Event Processing

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