Generating Synthetic Time-Series Data For Smart-Building Knowledge Graphs Using Generative Adversarial Networks

[This blog post is based on Jesse van Haaster‘s bachelor thesis Artificial Intelligence at VU]

Knowledge Graphs represent data as triples, connecting related data points. This form of representation is widely used for various applications, such as querying information and drawing inferences from data. For fine-tuning such applications, actual KGs are needed. However, in certain domains like medical records or smart home devices, creating large-scale public knowledge graphs is challenging due to privacy concerns. To address this, generating synthetic knowledge graph data that mimics the original while preserving privacy is highly beneficial.

Jesse’s thesis explored the feasibility of generating meaningful synthetic time series data for knowledge graphs. He specifically does this in the smart building / IoT domain, building on our previous work on IoT knowledge graphs, including OfficeGraph.

To this end, two existing generative adversarial networks (GANs), CTGAN and TimeGAN, are evaluated for their ability to produce synthetic data that retains key characteristics of the original OfficeGraph dataset. Jesse compared among other things the differences in distributions of values for key features, such as humidity, temperature and co2 levels, seen below.

Key value distributions for CTGAN-generated data vs original data
Key value distributions for TimeGAN-generated data vs original data

The experiment results indicate that while both models capture some important features, neither is able to replicate all of the original data’s properties. Further research is needed to develop a solution that fully meets the requirements for generating meaningful synthetic knowledge graph data.

More details can be found in Jesse’s thesis (found below) and his Github repository https://github.com/JaManJesse/SyntheticKnowledgeGraphGeneration

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HEDGE-IoT project kickoff

The HorizonEurope project HEDGE-IoT started January 2024. The 3.5 year project will build on existing technology to develop a Holistic Approach towards Empowerment of the DiGitalization of the Energy Ecosystem through adoption of IoT solutions. For VU, this project allows us to continue with the research and development initiated in the InterConnect project on data interoperability and explainable machine learning for smart buildings.

Researchers from the User-Centric Data Science group will participate in the project mostly in the context of the Dutch pilot, which will run in Arnhems Buiten, the former testing location of KEMA in the east of the Netherlands. In the pilot, we will collaborate closely with the other Dutch partners: TNO and Arnhems Buiten. At this site, an innovative business park is being realized that has its own power grid architecture, allowing for exchange of data and energy, opening the possibility for various AI-driven services for end-users.

VU will research a) how such data can be made interoperable and enriched with external information and knowledge and b) how such data can be made accessible to services and end-users through data dashboards that include explainable AI.

The image above shows the Arnhems Buiten buildings and the energy grid (source: Arnhems Buiten)

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Explainable AI using visual Machine Learning

The InterConnect project gathers 50 European entities to develop and demonstrate advanced solutions for connecting and converging digital homes and buildings with the electricity sector. Machine Learning (ML) algorithms play a significant role in the InterConnect project. Most prominent are the services that do some kind of forecasting like predicting energy consumption for (Smart) devices and households in general. The SAREF ontology allows us to standardize input formats for common ML approaches and that explainability can be increased by selecting algorithms that inherently have these features (e.g. Decision Trees) and by using interactive web environments like Jupyter Notebooks a convenient solution for users is created where step by step the algorithmic procedures can be followed and visualized and forms an implementation example for explainable AI.

Read more, and watch our live demonstration video on the InterConnect project page.

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Comparing Synthetic Data Generation Tools for IoT Data

[This post is based on the Bachelor Information Sciences project of Darin Pavlov and reuses text from his thesis. The research is part of VU’s effort in the InterConnect project and was supervised by Roderick van der Weerdt]

The concepts and technologies behind the Internet of Things (IoT) make it possible to establish networks of interconnected smart devices. Such networks can produce large volumes of data transmitted through sensors and actuators. Machine Learning can play a key role in processing this data towards several use cases in specific domains automotive, healthcare, manufacturing, etc. However, access to data for developing and testing Machine Learning is often hindered due to sensitivity of data, privacy issues etc.

One solution for this problem is to use synthetic data, resembling as much as possible real data. In his study, Darin Pavlov conducted a set of experiments, investigating the effectiveness of synthetic IoT data generation by three different tools:

This table shows the results of one of the two Machine Learning detection tests showing how difficult it is to differentiate the synthetic data from the real one with a Machine Learning model. For two datasets, the result is calculated as 1 minus the average ROC AUC score

Darin compared the tools on various distinguishability metrics. He observed that Mostly AI outperforms the other two generators, although Gretel.ai shows similar satisfactory results on the statistical metrics. The output of SDV on the other hand is poor on all metrics. Through this study we aim to encourage future research within the quickly developing area of synthetic data generation in the context of IoT technology.

More details can be found in Darin’s thesis.

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Interconnect Project kickoff

On 1 October 2019, the Horizon2020 Interconnect project has started. The goal of this huge and ambitious project is to achieve a relevant milestone in the democratization of efficient energy management, through a flexible and interoperable ecosystem where distributed energy resources can be soundly integrated with effective benefits to end-users.

To this end, its 51 partners (!) will develop an interoperable IOT and smart-grid infrastructure, based on Semantic technologies, that includes various end-user services. The results will be validated using 7 pilots in EU member states, including one in the Netherlands with 200 appartments.

The role of VU is to develop in close collaboration with TNO extend and validating the SAREF ontology for IOT as well as and other relevant ontologies. VU will lead a task on developing Machine Learning solutions on Knowledge graphs and extend the solutions towards usable middle layers for User-centric ML services in the pilots, specifically in the aforementioned Dutch pilot, where VU will collaborate with TNO and VolkerWessel iCity and Hyrde.

Interconnect team photo, taken at the location of the kickoff meeting: the FC Porto stadium

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The ESWC2019 PhD Symposium

As part of the ESWC 2019 conference program, the ESWC PhD Symposium was held in wonderful Portoroz, Slovenia. The aim of the symposium, this year organized by Maria-Esther Vidal and myself, is to provide a forum for PhD students in the area of Semantic Web to present their work and discuss their projects with peers and mentors.

Even though this year, we received 5 submissions, all of the submissions were of high quality, so the full day symposium featured five talks by both early and middle/late stage PhD students. The draft papers can be found on the symposium web page and our opening slides can be found here. Students were mentored by amazing mentors to improve their papers and presentation slides. A big thank you to those mentors: Paul Groth, Rudi Studer, Maria Maleshkova, Philippe Cudre-Mauroux,  and Andrea Giovanni Nuzzolese.

The program also featured a keynote by Stefan Schlobach, who talked about the road to a PhD “and back again”. He discussed a) setting realistic goals, b) finding your path towards those goals and c) being a responsible scientist and person after the goal is reached.

Students also presented their work through a poster session and the posters will also be found at the main conference poster session on tuesday 4 June.

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Who uses DBPedia anyway?

[this post is based on Frank Walraven‘s Master thesis]

Who uses DBPedia anyway? This was the question that started a research project for Frank Walraven. This question came up during one of the meetings of the Dutch DBPedia chapter, of which VUA is a member. If usage and users are better understood, this can lead to better servicing of those users, by for example prioritizing the enrichment or improvement of specific sections of DBPedia Characterizing use(r)s of a Linked Open Data set is an inherently challenging task as in an open Web world, it is difficult to know who are accessing your digital resources. For his Msc project research, which he conducted at the Dutch National Library supervised by Enno Meijers , Frank used a hybrid approach using both a data-driven method based on user log analysis and a short survey of know users of the dataset. As a scope Frank selected just the Dutch DBPedia dataset.

For the data-driven part of the method, Frank used a complete user log of HTTP requests on the Dutch DBPedia. This log file (see link below) consisted of over 4.5 Million entries and logged both URI lookups and SPARQL endpoint requests. For this research only a subset of the URI lookups were concerned.

As a first analysis step, the requests’ origins IPs were categorized. Five classes can be identified (A-E), with the vast majority of IP addresses being in class “A”: Very large networks and bots. Most of the IP addresses in these lists could be traced back to search engine

indexing bots such as those from Yahoo or Google. In classes B-F, Frank manually traced the top 30 most encounterd IP-addresses, concluding that even there 60% of the requests came from bots, 10% definitely not from bots, with 30% remaining unclear.

The second analysis step in the data-driven method consisted of identifying what types of pages were most requested. To cluster the thousands of DBPedia URI request, Frank retriev

ed the ‘categories’ of the pages. These categories are extracted from Wikipedia category links. An example is the “Android_TV” resource, which has two categories: “Google” and “Android_(operating_system)”. Following skos:broader links, a ‘level 2 category’ could also be found to aggregate to an even higher level of abstraction. As not all resources have such categories, this does not give a complete image, but it does provide some ideas on the most popular categories of items requested. After normalizing for categories with large amounts of incoming links, for example the category “non-endangered animal”, the most popular categories where 1. Domestic & International movies, 2. Music, 3. Sports, 4. Dutch & International municipality information and 5. Books.

Frank also set up a user survey to corroborate this evidence. The survey contained questions about the how and why of the respondents Dutch DBPedia use, including the categories they were most interested in. The survey was distributed using the Dutch DBPedia websitea and via twitter however only attracted 5 respondents. This illustrates

the difficulty of the problem that users of the DBPedia resource are not necessarily easily reachable through communication channels. The five respondents were all quite closely related to the chapter but the results were interesting nonetheless. Most of the users used the DBPedia SPARQL endpoint. The full results of the survey can be found through Frank’s thesis, but in terms of corroboration the survey revealed that four out of the five categories found in the data-driven method were also identified in the top five resulting from the survey. The fifth one identified in the survey was ‘geography’, which could be matched to the fifth from the data-driven method.Frank’s research shows that although it remains a challenging problem, using a combination of data-driven and user-driven methods, it is indeed possible to get an indication into the most-used categories on DBPedia. Within the Dutch DBPedia Chapter, we are currently considering follow-up research questions based on Frank’s research.

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Big Data Europe Project ended

All good things come to an end, and that also holds for our great Horizon2020 project “Big Data Europe“, in which we collaborated with a broad range of techincal and domain partners to develop (Semantic) Big Data infrastructure for a variety of domains. VU was involved as work package leader in the Pilot and Evaluation work package and co-developed methods to test and apply the BDE stack in Health, Traffic, Security and other domains..

You can read more about the end of the project in this blog post at the BDE website.

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SEMANTiCS2017

This year, I was conference chair of the SEMANTiCS conference, which was held 11-14 Sept in Amsterdam. The conference was in my view a great success, with over 310 visitors across the four days, 24 parallel sessions including academic and industry talks, six keynotes, three awards, many workshops and lots of cups of coffee. I will be posting more looks back soon, but below is a storify item giving an idea of all the cool stuff that happened in the past week.

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Big Data Europe Platform paper at ICWE 2017

With the launch of the Big Data Europe platform behind us, we are telling the world about our nice platform and the many pilots in the societal challenge domains that we have executed and evaluated. We wrote everything down in one comprehensive paper which was accepted at the 7th international conference on Web Engineering (ICWE 2017) which is to be held in Rome next month.

High-level BDE architecture (copied from the paper Auer et al.)

The paper “The BigDataEurope Platform – Supporting the Variety Dimension of Big Data”  is co-written by a very large team (see below) and it presents the BDE platform — an easy-to-deploy, easy-to-use and adaptable (cluster-based and standalone) platform for the execution of big data components and tools like Hadoop, Spark, Flink, Flume and Cassandra.  To facilitate the processing of heterogeneous data, a particular innovation of the platform is the Semantic Layer, which allows to directly process RDF data and to map and transform arbitrary data into RDF. The platform is based upon requirements gathered from seven of the societal challenges put forward by the European Commission in the Horizon 2020 programme and targeted by the BigDataEurope pilots. It is validated through pilot applications in each of these seven domains. .A draft version of the paper can be found here.

 

The full reference is:

Sören Auer, Simon Scerri, Aad Versteden, Erika Pauwels, Angelos Charalambidis, Stasinos Konstantopoulos, Jens Lehmann, Hajira Jabeen, Ivan Ermilov, Gezim Sejdiu, Andreas Ikonomopoulos, Spyros Andronopoulos, Mandy Vlachogiannis, Charalambos Pappas, Athanasios Davettas, Iraklis A. Klampanos, Efstathios Grigoropoulos, Vangelis Karkaletsis, Victor de Boer, Ronald Siebes, Mohamed Nadjib Mami, Sergio Albani, Michele Lazzarini, Paulo Nunes, Emanuele Angiuli, Nikiforos Pittaras, George Giannakopoulos, Giorgos Argyriou, George Stamoulis, George Papadakis, Manolis Koubarakis, Pythagoras Karampiperis, Axel-Cyrille Ngonga Ngomo, Maria-Esther Vidal.   . Proceedings of The International Conference on Web Engineering (ICWE), ICWE2017, LNCS, Springer, 2017

 

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