2019 Highlights – Semantic Web group at ISWC 2019

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Our group contributed in several ways to ISWC 2019.
Olaf Hartig was PC co-chair and held, together with Ruben Taelman a tutorial on GraphQL. Information about the tutorial is available at https://www.ida.liu.se/research/semanticweb/events/GraphQLTutorialAtISWC2019.shtml.

Patrick Lambrix and Huanyu Li co-organized two tracks in the Ontology Alignment Evaluation Initiative (the Anatomy track and the Interactive track).

Further, the group presented two posters:
Keskisärkkä R, Li H, Cheng S, Carlsson N, Lambrix P, An Ontology for Ice Hockey,

and

Li H, Armiento R, Lambrix P, Extending Ontologies in the Nanotechnology Domain using Topic Models and Formal Topical Concept Analysis on Unstructured Text.

Conducting and reporting on empirical user studies in Semantic Web contexts

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Our colleague Catia Pesquita from Lisbon University is presenting a paper, co-authored by Valentina Ivanova and Patrick Lambrix of our group and Steffen Lohmann from Fraunhofer, at EKAW 2018 – 21st International Conference on Knowledge Engineering and Knowledge Management, on a framework to conduct and report on empirical user studies in Semantic Web contexts. As organizers of the VOILA workshops on Visualization and Interaction for Ontologies and Linked Data  at the recent ISWC conferences, we noticed that both the assessment of interactive Semantic Web approaches as well as the reporting on conducted user studies still suffer from weaknesses.
Empirical evaluations are essential to compare different approaches, demonstrate their benefits and reveal their drawbacks, and thus to facilitate further adoption of Semantic Web technologies. In our paper, we review empirical user studies of user interfaces, visualizations and interaction techniques recently published at several Semantic Web venues, assessing both the user studies themselves and their reporting. We then chart the design space of available methods for user studies in Semantic Web contexts. Finally, we propose a framework for their comprehensive reporting, taking into consideration user expertise, experimental setup, task design, experimental procedures and results analysis.

The original publication is at doi. A self-archived postprint version of the article is available at Linköping University Institutional Repository (DiVA).

Dagstuhl seminar on Knowledge Graphs

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Returning home from a very interesting Dagstuhl seminar on Knowledge Graphs, it is time to collect some thoughts. In the seminar we developed a shared understanding of the current state of the art in Knowledge Graphs and more importantly mapped out the road ahead. The format of the seminar consisted of 5-min pitches on relevant topics, and then followed up by group discussions, to be summarised and consolidated in an upcoming report. In the spirit of true societal (and research) progress a large part of the seminar was devoted to discussing grand challenges in our society, where in this case the focus was on those where we believe Knowledge Graphs can play a crucial role in addressing the challenges. In the upcoming report those will be discussed in depth, but examples of such challenges include interaction between humanity and machines, the kind of explainable and human-centred AI that is required in various societal domains, such as medicine, keeping up with knowledge evolution and rapidly changing information in our society, and addressing information interoperability at scale.

The feeling I in particular take with me from this seminar is that we have a unique opportunity to really facilitate interaction and integration of major results from different areas, and that Knowledge Graphs may be the key that finally makes this possible at scale.

However, taking a step back, one may first ask the question: What is a Knowledge Graph? And how does it relate to previous objects of study, such as Linked Data or Ontologies? Although this was discussed at length in the seminar, my personal viewpoint is that we do not really need a strict scientific definition. Potentially a descriptive one could be useful, but even just exemplifying what we mean when talking about Knowledge Graphs should be enough. To me a Knowledge Graph is about two things: knowledge that is represented in some graph-like format, preferably machine readable, and (can be) used as the source of knowledge/information/data in some application. This subsumes both ontologies, Linked Data, and all the various Knowledge Graphs proposed by large companies so far. Although Google were the ones to popularise the term a few years ago, it has been around also before that, and can even be traced back to ancient times (as some people pointed out in the seminar). However, that does not reduce the importance of the Google Knowledge Graph, both as a positive example and inspiration for others (i.e., Knowledge Graphs of “everything” can really work at scale), and as a popular explanation of the term, or could maybe even be seen as a revitalisation of the whole knowledge representation field.

So, how does it relate to existing fields then? Here we come back to my key take-away from the seminar – integration of research fields. I do not see Knowledge Graphs as a new field, nor as a renaming of some existing area, such as the Semantic Web or ontologies, but rather it is what emerges when you marry ontologies and Linked Data with property graphs and graph databases and the web. Or macine learning models with graph formats and methods for symbolic knowledge representation, e.g., to create explainable AI. Of course, that means that everything we learned so far in these individual fields is very valuable, e.g., ontology engineering, representation formats and standards etc., but it is when you marry that with results from other fields that 1+1 becomes 3, or even 10. So if you ask for the relation to ontologies, for instance, I would say that Knowlege Graphs is a generalisation, where any Semantic Web ontology can probably be considered to be a Knowledge Graph, but not every Knowledge Graph (probably just a few) will be an ontology.

 

Related to our own research in the Linköping University Semantic Web group, we do have some very valuable pieces of this puzzle to offer. In the knowledge representation area we have worked a lot on ontology engineering and ontology design patterns, and this is a valuable input also for creation of Knowledge Graphs. In particular the notion of design patterns I believe is very valuable also when creating generic Knowledge Graphs. Especially since patterns are not only intended as a technical development tool, but can also support understandability, interoperability, reuse, and act as a least common denominator when matching and integrating data and knowledge. Also recent work on ontology matching will be directly applicable to Knowledge Graph matching and integration, as well as the work on ontology evolution and stream reasoning and complex event processing, for managing highly dynamic data and knowledge. All of this is highly relevant when generalised from ontologies to general Knowledge Graphs, maybe even more relevant than for the specific case of ontologies.

Then of course a Knowledge Graph needs to be represented in some way, preferably using a machine readable format and in a language with some formal semantics. RDF is an obvious candidate for representing Knowledge Graphs on the web. However, so far the RDF community has been quite separated from the community around property graphs (and graph databases), in my opinion mainly due to the difficulties of directly representing property graphs in RDF. Also here the LiU group has something to offer, in the form of the proposals by Olaf Hartig on RDF and SPARQL extensions to bridge this gap (called RDF* and SPARQL*) as well as our research on graph data, and models for that, in general.

I hope this seminar will really become the starting point of something new. New research directions, and a more inclusive community (than maybe the Semantic Web community has been, in retrospect) around Knowledge Graps that embraces the need for integrating approaches from various other fields, embraces variety and complexity, and embraces dynamics.

LiU Semantic Web research at The Web Conference 2018

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Next week, the 27th edition of The Web Conference (earlier called International World Wide Web Conference – WWW) will take place in Lyon, France, and different results of Semantic Web research at LiU will be present at the conference.

First, there is a research paper in the main research track of the conference by our Olaf Hartig and Jorge Pérez from the Universidad de Chile. The title of the paper is “Semantics and Complexity of GraphQL.”

Additionally, Olaf will give an invited talk in the Web Stream Processing workshop at the conference. While the exact title of the talk is yet to be decided, the talk will present recent work about the RDF*/SPARQL* Approach to Statement-Level Metadata in RDF.

Find Olaf and talk to him if you also happen to be in the conference; he will be happy to tell you more about his research, as well as our other Semantic Web related research at LiU.

Valentina Ivanova defended her PhD thesis on Fostering User Involvement in Ontology Alignment and Alignment Evaluation

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On January 26, our PhD candidate Valentina Ivanova defended successfully her thesis with the title “Fostering User Involvement in Ontology Alignment and Alignment Evaluation.”
This thesis focuses on supporting users during the cognitively intensive ontology alignment process and makes several contributions.

First, front- and back-end system features that foster user involvement during the alignment process were identified and their support has been investigated in existing systems Then this was further narrowed down to investigate features in connection to manual validation while also considering the level of user expertise by assessing the impact of user errors on alignments’ quality. As developing and aligning ontologies is an error-prone task, there is also an investigation on the benefits of the integration of ontology alignment and debugging.
Further, interactive comparative exploration and evaluation of multiple alignments at different levels of detail was enabled by developing a dedicated visual environment—Alignment Cubes—which allows for alignments’ evaluation even in the absence of reference alignments.
Finally, inspired by the latest technological advances three promising directions for the application of large, high-resolution displays in the field were identified: improving the navigation in the ontologies and their alignments, supporting reasoning and collaboration between users.

Valentina’s work on the thesis was supervised by Patrick Lambrix, and co-supervised by Nahid Shahmehri. The opponent was Fabien Gandon from INRIA, France. The examination committee consisted of Oscar Corcho from Universidad Politecnica de Madrid, Mathieu d’ Aquin from the National University of Ireland Galway and Claes Lundström from Sectra and Linköping University. Mattias Arvola from Linköping University was backup examination committee member.

Find the thesis in the Diva portal.