AIRM-based, fine-grained semantic filtering of notices to airmen

Burgstaller, Felix; Steiner, Dieter; Schrefl, Michael; Gringinger, Eduard; Wilson, Scott; Stricht, Sam Van der

doi:10.1109/icnsurv.2015.7121222

Cited by 12 publications

(4 citation statements)

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“…Automated rule-based filtering and prioritization of DNOTAMs reduces information overload in pilot briefings [79]. Pilots formulate an interest specification [14], which comprises spatial, temporal, and aircraft interests [15]. The SemNOTAM system then matches DNOTAMs against the interest specification and assigns an importance, e.g., flight critical or operational restriction, to each DNOTAM.…”

Section: Use Case 1: Pilot Briefingsmentioning

confidence: 99%

Knowledge Graph OLAP

Schuetz

Bozzato

Neumayr

et al. 2021

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A knowledge graph (KG) represents real-world entities and their relationships. The represented knowledge is often context-dependent, leading to the construction of contextualized KGs. The multidimensional and hierarchical nature of context invites comparison with the OLAP cube model from multidimensional data analysis. Traditional systems for online analytical processing (OLAP) employ multidimensional models to represent numeric values for further analysis using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of the traditional OLAP query operations for the purposes of performing analysis over KGs. In particular, we decompose the roll-up operation from traditional OLAP into a merge and an abstraction operation. The merge operation corresponds to the selection of knowledge from different contexts whereas abstraction replaces entities with more general entities. The result of such a query is a more abstract, high-level view – a management summary – of the knowledge.

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Section: Use Case 1: Pilot Briefingsmentioning

confidence: 99%

Knowledge Graph OLAP

Schuetz

Bozzato

Neumayr

et al. 2021

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“…Potential application areas, besides business rules as shown in the examples, are rule-based systems for information tailoring [8], web data extraction [15], and knowledge graph management [3], where rule modules encode knowledge for tasks such as data extraction, transformation, cleansing, and filtering and need to be adapted to different settings.…”

Section: Potential Application Areasmentioning

confidence: 99%

“…In data-and knowledge-intensive systems it is good practice to separate explicit knowledge, elicited from domain experts and translated into declarative expressions by rule developers, from application code developed by application developers. Rule-based knowledge representation and reasoning build the core of systems for business rule engines [26], web data extraction [15], data wrangling [19], knowledge graph management [3], and information tailoring [8]. With the increasing number and complexity of rules, their maintenance and their adaptation to different settings and contexts become key challenges of rule developers.…”

Section: Introductionmentioning

confidence: 99%

Rule Module Inheritance with Modification Restrictions

Burgstaller

Neumayr

Sallinger

et al. 2018

Lecture Notes in Computer Science

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Adapting rule sets to different settings, yet avoiding uncontrolled proliferation of variations, is a key challenge of rule management. One fundamental concept to foster reuse and simplify adaptation is inheritance. Building on rule modules, i.e., rule sets with input and output schema, we formally define inheritance of rule modules by incremental modification in single inheritance hierarchies. To avoid uncontrolled proliferation of modifications, we introduce formal modification restrictions which flexibly regulate the degree to which a child module may be modified in comparison to its parent. As concrete rule language, we employ Datalog ± which can be regarded a common logical core of many rule languages. We evaluate the approach by a proof-of-concept prototype.

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“…SWIM documentation and web pages can reference term definitions in the CV using web Universal Resource Identifiers (URIs). [30,50,51] Notices to Airmen (NOTAMs) are advisories to pilots concerning establishment of, or changes to flight procedures, airport facilities, flight services, or other potential operational hazards. As part of the flight planning and pre-flight briefing process, pilots must review all of the NOTAMs pertinent to their filed route of flight.…”

Section: Faa Enterprise Information Management [40]mentioning

confidence: 99%

Ontologies for aviation data management

Keller

2016

2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)

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Managing complex aviation data can be a significant challenge for any enterprise-whether a government agency, airline, airframe manufacturer, or aviation service provider. To handle this challenge, data models are typically developed to characterize and manage the data generated, used, and stored by a given enterprise. Unfortunately, different data providers employ qualitatively different data models, and this gives rise to problems exchanging data across organizational boundaries. Over the past decade, these problems have motivated data producers and consumers to look toward standardized data exchange models to address data interoperability. In this paper we examine some of these standardized data exchange models and compare them with a new type of data model based on ontologies. Ontology models have emerged in recent years from a confluence of research in the artificial intelligence, semantic web, and information science communities. This paper introduces ontology models, provides several use cases for ontologies relevant to aviation data management, and summarizes state-ofthe-art aviation prototype applications that utilize ontologies.

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AIRM-based, fine-grained semantic filtering of notices to airmen

Cited by 12 publications

References 1 publication

Knowledge Graph OLAP

Knowledge Graph OLAP

Rule Module Inheritance with Modification Restrictions

Ontologies for aviation data management

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