Designing a Query Neural Network
Lade...
Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2020
Typ der Arbeit
Master
Studiengang
Sammlung
Typ
11 - Studentische Arbeit
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
Hochschule für Wirtschaft FHNW
Verlagsort / Veranstaltungsort
Olten
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
This paper presents the Query Neural Network (QNN). The QNN embeds a set of rules in an Artificial Neural Network (ANN) which answers queries in a backward chaining style. This work can be classified in the research field of 'Integration of Machine Learning and Reasoning'. This field has shown the advantages when combining hand-built-classifier and empirical learning. Furhter, a current line of research in this area is the study of the integration of goal-directed reasoning with backward chaining into an ANN (D'Avila Garcez et al., 2019). While there are already some tools that implement goal directed reasoning, none of them can do this in propositional logic, which also can handle negations and hard rules. Moreover, the QNN tries to close this research gap. The Design Science Research methodology was chosen as an appropriate strategy to design, implement, and evaluate the QNN. First, a concept of the QNN was created. Afterwards, it was implemented within a python program . To evaluate the QNN a sample data from FHNW from the application process for the master's degree in business information systems were used to test if the QNN meets its testing criterias. It was evaluated if the goal-directed reasoning works accurately and the QNN meets its requirements. The evaluation results showed that the QNN meets its requirements.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Begutachtung
Open Access-Status
Lizenz
Zitation
Vogel, C. (2020). Designing a Query Neural Network [Hochschule für Wirtschaft FHNW]. https://irf.fhnw.ch/handle/11654/40380