Dagstuhl seminar on Knowledge Graphs

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.

Emanuele Della Valle (Politecnico di Milano) will talk at LiU about Stream Reasoning

On October 5 (Thursday), Emanuele Della Valle of Politecnico di Milano, Italy, will give a Semantic Web related talk at LiU. The title of his talk is:

Stream Reasoning: A Summary of Ten Years of Research and a Vision for the Next Decade

Abstract: Stream reasoning studies the application of inference techniques to data characterised by being highly dynamic. It can find application in several settings, from Smart Cities to Industry 4.0, from Internet of Things to Social Media analytics. This year stream reasoning turns ten, and this talk analyses its growth. In the first part, it traces the main results obtained so far, by presenting the most prominent studies. It starts by an overview of the most relevant studies developed in the context of semantic web, and then it extends the analysis to include contributions from adjacent areas, such as database and artificial intelligence. Looking at the past is useful to prepare for the future: the second part presents a set of open challenges and issues that stream reasoning will face in the next future.

Time and date: 9.00am, October 5, 2017

Location: Campus Valla, Building E, Room “Alan Turing”

Dagstuhl seminar on Federated Semantic Data Management

During the last week of June, I co-organized a Dagstuhl seminar on Federated Semantic Data Management together with Maria-Esther Vidal and Johann-Christoph Freytag. It was a very intense week with a packed schedule and almost no time to catch some breath (exactly like how a Dagstuhl seminar should be I guess 😉

To start with, we had scheduled a few short, survey-style talks on a number of topics related to the seminar. In particular, these talks covered:

While these talks were meant to establish a common understanding of key concepts and terminology, the major focus of the seminar was on discussions and working groups. To this end, we had invited a good mix of participants from the Semantic Web field, from Databases, as well as from application areas. Due to this mix, we ended up on several occasions and in different constellations discussing and reflecting in depth the fundamental assumptions and the core ideas of federated semantic data management. These general discussions and reflections kept re-emerging not only during the sessions, but also during the meals, the coffee breaks, and the evenings in Dagstuhl’s wine cellar. In my opinion, clearly articulating and repeatedly arguing about these assumptions and ideas was a long-needed discussion to be had in the community. After this week, I would guess that many of the participants have a much clearer understanding of what federated semantic data management can and should be, and I am certain that this understanding will be reflected in the reports that the working groups are preparing.

Speaking of working groups, the seminar was structured around four topics addressed by four separate working groups who came together occasionally to report on their progress and obtain feedback from the other groups. The topics were:

  • RDF and graph data models
  • Federated query processing
  • Access control and privacy
  • Use cases and applications

Each of the working groups is currently preparing a summary of their discussions and results. These summaries will become part of our Dagstuhl report (to be published some time in August if all goes well). In addition to this report, we are planning to document the discussions and the results of the seminar in a collection of more detailed publications.

What’s next? We have some ideas to keep the momentum and to advance the discussions around the seminar topics in a more continuous community process. Stay tuned.


Kim Ahlstrøm (Aalborg University) will talk in the LiU Semantic Web Seminars Series

KimAhlstromOn February 7 (Tuesday), Kim Ahlstrøm of Aalborg University will give a talk in our series of Semantic Web seminars. The title of his talk is:

Towards Answering Provenance-Enabled SPARQL Queries over RDF Data Cubes

Abstract: The SPARQL 1.1 standard has made it possible to formulate analytical queries in SPARQL. While some approaches have become available for processing analytical queries on RDF data cubes, little attention has been paid to answering provenance-enabled queries over such data. Yet, considering provenance is a prerequisite to being able to validate if a query result is trustworthy. The main challenge for existing triple stores is the way provenance can be encoded in standard triple stores based on context values (named graphs). In this talk, I will present shortcomings in existing techniques, and we propose an index to handle the high number of context values that provenance encoding typically entails. Our experimental results using the Star Schema Benchmark show the feasibility and scalability of our index and query evaluation strategies.

Time and date: 3.15pm, February 7, 2017

Location: Campus Valla, Building B, Room “Charles Babbage”

Marjan Alirezaie (Örebro University) will talk in the LiU Semantic Web Seminars Series

PhotoMarjanAlirezaieOn Tuesday next week, January 24, Marjan Alirezaie of Örebro University will give a talk in our series of Semantic Web seminars. The title of her talk is:

Recent Developments in Bridging the Semantic Gap Problem

Abstract: In this talk, I will present a summary of my PhD thesis which is about bridging the semantic gap between sensor data and ontological knowledge. The focus will be on the recent developments in heterogeneous knowledge integration as a solution for the semantic gap issue. I will also introduce the two research projects: Semantic Robot and E-care@home, with the goal of addressing the knowledge integration problem. In Semantic Robot, we are aiming to provide a semantic layer to a 3D topographic map and making the objects in the 3D map reasoning ready for different purposes such as querying and navigation. Likewise, in Ecare@home which is a Swedish interdisciplinary distributed research environment, our focus is on the development of methods that provide interpretation of the heterogeneous data coming from different types of sensors in conjunction with both medical and environmental knowledge in order to provide e-services for the elderly residing in their homes.

Time and date: 3.15pm, January 24, 2017

Location: Campus Valla, Building B, Room “Alan Turing”