Umutcan Serles (formerly Umutcan Şimşek) is a Semantic Web and Knowledge Graph researcher in STI Innsbruck. This page contains some highlights of his professional life. For more details, please see the CV. For a full list of publications, please see the Google Scholar Profile. This page is still under construction. I also have a Wikidata page.
PhD in Computer Science, 2016-2021
University of Innsbruck
MSc in Computer Engineering, 2013-2015
Ege University, Izmir, Turkey
MSc in Computer Science, 2013-2015
Technische Hochschule Mittelhessen, Giessen, Germany
BSc in Computer Engineering, 2009-2013
Ege University, Izmir, Turkey
Web API Annotations with Schema.org Actions. The specification developed part of my PhD research.
Mapping of existing content model to schema.org and creating an extension for missing types and properties in tourism domain.
Knowledge Graph-enabled conversational agents, University of Innsbruck and other partners, Private funding.
Schema.org is a widely adopted vocabulary for semantic annotation of web resources. However, its generic nature makes it complicated for publishers to pick the right types and properties for a specific domain. In this paper, we propose an approach, a domain specification process that generates domain-specific patterns by applying operators implemented in SHACL syntax to the schema.org vocabulary. These patterns can support annotation generation and verification processes for specific domains. We provide tooling for the generation of such patterns and evaluate the usability of both domain-specific patterns and the tools with use cases in the tourism domain.
This book describes methods and tools that empower information providers to build and maintain knowledge graphs, including those for manual, semi-automatic, and automatic construction; implementation; and validation and verification of semantic annotations and their integration into knowledge graphs. It also presents lifecycle-based approaches for semi-automatic and automatic curation of these graphs, such as approaches for assessment, error correction, and enrichment of knowledge graphs with other static and dynamic resources. Chapter 1 defines knowledge graphs, focusing on the impact of various approaches rather than mathematical precision. Chapter 2 details how knowledge graphs are built, implemented, maintained, and deployed. Chapter 3 then introduces relevant application layers that can be built on top of such knowledge graphs, and explains how inference can be used to define views on such graphs, making it a useful resource for open and service-oriented dialog systems. Chapter 4 discusses applications of knowledge graph technologies for e-tourism and use cases for other verticals. Lastly, Chapter 5 provides a summary and sketches directions for future work. The additional appendix introduces an abstract syntax and semantics for domain specifications that are used to adapt schema. org to specific domains and tasks. To illustrate the practical use of the approaches presented, the book discusses several pilots with a focus on conversational interfaces, describing how to exploit knowledge graphs for e-marketing and e-commerce. It is intended for advanced professionals and researchers requiring a brief introduction …
The schema.org initiative led by the four major search engines curates a vocabulary for describing web content. The number of semantic annotations on the web are increasing, mostly due to the industrial incentives provided by those search engines. The annotations are not only consumed by search engines, but also by other automated agents like intelligent personal assistants (IPAs). However, only annotating data is not enough for automated agents to reach their full potential. Web APIs should also be annotated for automating service consumption, so the IPAs can complete tasks like booking a hotel room or buying a ticket for an event on the fly. Although there has been a vast amount of effort in the semantic web services field, the approaches did not gain too much adoption outside of academia, mainly due to lack of concrete incentives and steep learning curves. In this paper, we suggest a lightweight, bottom-up approach based on schema.org actions to annotate Web APIs. We analyse schema.org vocabulary in the scope of lightweight semantic web services literature and propose extensions where necessary. We demonstrate our work by annotating existing Web APIs of accommodation service providers. Additionally, we briefly demonstrate how these APIs can be used dynamically, for example, by a dialogue system.
Goal-oriented dialogue systems typically communicate with a backend (eg database, Web API) to complete certain tasks to reach a goal. The intents that a dialogue system can recognize are mostly included to the system by the developer statically. For an open dialogue system that can work on more than a small set of well curated data and APIs, this manual intent creation will not scalable. In this paper, we introduce a straightforward methodology for intent creation based on semantic annotation of data and services on the web. With this method, the Natural Language Understanding (NLU) module of a goal-oriented dialogue system can adapt to newly introduced APIs without requiring heavy developer involvement. We were able to extract intents and necessary slots to be filled from this http URL annotations. We were also able to create a set of initial training sentences for classifying user utterances into the generated intents. We demonstrate our approach on the NLU module of a state-of-the art dialogue system development framework.
The introduction of the schema.org vocabulary was a big step towards making websites machine read- and understandable. Due to schema.org’s RDF-like nature storing annotations in a graph database is easy and efficient. In this paper the authors show how they gather touristic data in the Austrian region of Tirol and provide this data publicly in a knowledge graph. The definition of subsets of the vocabulary is followed by providing means to map data sources efficiently to schema.org and then store the annotated content into the graph. To showcase the consumption of the touristic data four scenarios are described which use the knowledge graph for real life applications and data analysis.