The Smart Data Analytics group is always looking for good students to write theses. The topics can be in one of the following broad areas:

Please note that the list below is only a small sample of possible topics and ideas. Please contact us to discuss further, to find new topics, or to suggest a topic of your own.


Topic                                                                                                                                                                                                            Level Contact Person
Scalable graph kernels for RDF data
Develop graph kernels forRDF data and use traditional machine learning methods for classification.
B, M Dr. Hajira Jabeen
Distributed Knowledge graph Clustering
Clustering of heterogenous data contained in a Knowledge graphs
B, M Dr. Hajira Jabeen
Distributed Anomaly Detection in RDF
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data.
B, M Dr. Hajira Jabeen
PyTorch Integration in Spark
PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. This thesis integrates PyTorch in Apache Spark following guidelines and run KG embeddig models as preliminary tests. https://docs.databricks.com/applications/deep-learning/spark-integration.html
B Dr. Hajira Jabeen
Use of Ontology information in Knowledge graph embeddings
Knowledge graph embedding represents entities as vectors in a common vector space. The objective of this work is to extend the existing KG embedding models to include schema information in the KG embedding models like in TransE, or ConvE etc and compare the performance.
B, M Dr. Hajira Jabeen
Negative sampling in Knowledge graph embeddings
Knowledge graph embedding represents entities as vectors in a common vector space. The objective of this work is to extend the existing KG embedding models to include more efficient and effective negative sampling methods like in TransE, or ConvE etc and compare the performance.
B, M Dr. Hajira Jabeen
Entity Resolution
Entity resolution is the task of identifying all mentions that represent the same real-world entity within a knowledge base or across multiple knowledge bases. We address the problem of performing entity resolution on RDF graphs containing multiple types of nodes, using the links between instances of different types to improve the accuracy.
B, M Dr. Hajira Jabeen
Rule/Concept Learning in Knowledge Graphs
In the Semantic Web context, OWL ontologies play the key role of domain conceptualizations while the corresponding assertional knowledge is given by the heterogeneous Web resources referring to them. However, being strongly decoupled, ontologies and assertional bases can be out of sync. In particular, an ontology may be incomplete, noisy, and sometimes inconsistent with the actual usage of its conceptual vocabulary in the assertions. Despite of such problematic situations, we aim at discovering hidden knowledge patterns from ontological knowledge bases, in the form of multi-relational association rules, by exploiting the evidence coming from the (evolving) assertional data. The final goal is to make use of such patterns for (semi-)automatically enriching/completing existing ontologies.
 B, M Dr. Hajira Jabeen
Intelligent Semantic Creativity : Culinarian
Computational creativity is an emerging branch of artificial intelligence that places computers in the center of the creative process. We aimt to create a computational system that creates flavorful, novel, and perhaps healthy culinary recipes by drawing on big data techniques. It brings analytics algorithms together with disparate data sources from culinary science.
In the most ambitious form, the system would employ human-computer interaction for rating different recipes and model the human cogitive ability for the cooking process.
The end result is going to be an ingredient list, proportions, and as well as a directed acyclic graph representing a partial ordering of culinary recipe steps.
B, M Dr. Hajira Jabeen
IoT Data Catalogues
While platforms and tools such as Hadoop and Apache Spark allow for efficient processing of Big Data sets, it becomes increasingly challenging to organize and structure these data sets. Data sets have various forms ranging from unstructured data in files to structured data in databases. Often the data sets reside in different storage systems ranging from traditional file systems, over Big Data files systems (HDFS) to heterogeneous storage systems (S3, RDBMS, MongoDB, Elastic Search, …). At AGT International, we are dealing primarily with IoT data sets, i.e. data sets that have been collected from sensors and that are processed using Machine Learning-based (ML) analytic pipelines. The number of these data sets is rapidly growing increasing the importance of generating metadata that captures both technical (e.g. storage location, size) and domain metadata and correlates the data sets with each other, e.g. by storing provenance (data set x is a processed version of data set y) and domain relationships.
M Dr. Martin Strohbach, Prof. Dr. Jens Lehmann

(Work at AGT International in Darmstadt)

Big Data quality Assessment (assigned)
Data quality is considered as a multidimensional concept that covers different aspects of quality such as accuracy, completeness, and timeliness. With the advent of Big Data, traditional quality assessment techniques are facing different challenges. Therefore, we should adopt the traditional techniques to big data technologies. The goal of this thesis is to re-implement the assessment techniques in the SANSA framework.
B, M Dr. Anisa Rula, Gezim Sejdiu
Understanding Short-Text: a Named Entity Recognition perspective Named Entity Recognition (NER) models play an important role in the Information Extraction (IE) pipeline. However, despite decent performance of NER models on newswire datasets, to date, conventional approaches are not able to successfully identify classical named-entity types in short/noisy texts. This thesis will thoroughly investigate NER in microblogs and propose new algorithms to overcome current state-of-the-art models in this research area. B, M  Diego Esteves
Multilingual Fact Validation Algorithms DeFacto (Deep Fact Validation) is an algorithm able to validate facts by finding trustworthy sources for them on the Web. Currently, it supports 3 main languages (en, de and fr). The goal of this thesis is to explore and implement alternative information retrieval (IR) methods to minimize the dependency of external tools on verbalizing natural language patterns. As result, we expect to enhance the algorithm performance by expanding its coverage. B, M Diego Esteves
Experimental Analysis of Class CS Problems
In this thesis, we explore unsolved problems of theoretical computer science with machine learning methods, especially reinforcement learning.
B, M Diego Esteves
Generating Property Graphs from RDF using a semantic preserving conversion approach
Graph Databases are on a rise since the last decade due to their dominance in mining and analysis of complex networks. Property Graphs (PGs), one of the graph data models which Graph Databases use, are suitable for the representation of many real-life application scenarios. They allow to efficiently represent complex networks (e.g. social networks, E-commerce) and interactions. In order to leverage this advantage of graph databases, conversions of other data models to property graphs are a current area of research. The aim of this thesis is to (i) propose a novel systematic conversion approach for generating PGs from RDF (one of the graph data models) (ii) and carry out exhaustive experiments on both RDF and PG datasets with respect to their native storage databases (i.e. Graph DBs vs Triplestores). This will allow to identify the types of queries for which graph databases offer performance advantages and ideally allow to adapt the storage mechanism accordingly. The outcome of this work will be integrated into the LITMUS framework, which is an open extensible framework for benchmarking of diverse Data Management Solutions.
B Harsh Thakkar
Graph partitioning for RDF data (assigned)
Big RDF datasets need to be stored and processed in distributed RDF data stores that are built on top of cluster servers. Several partitioning schemes like horizontal, vertical, and hash partitioning, exist that allow for splitting the datasets into several nodes, in order to achieve scalability and efficient query processing. The goal of this thesis is to study graph partitioning approaches for RDF data, compare the state of the art, and implement corresponding algorithms that will be integrated into the SANSA framework.
B, M Dr. Ioanna Lytra, Gezim Sejdiu
Recommendation system for RDF partitioners (assigned)
In order to store and query big RDF datasets efficiently in distributed environments, different partitioning techniques need to be implemented. Several techniques have been proposed for splitting Big RDF Data, ranging from vertical, hash, graph to semantic-based partitioners. However, the selection of the “best partitioner” depends highly on the structure of the dataset and the query efficiency and effectiveness are coupled to the query engine used. The goal of this thesis will be to develop a recommender system that will suggest the “best partitioner” based on the structure of the data and specific requirements.
B, M Gezim Sejdiu, Dr. Ioanna Lytra
Relation Linking for Question Answering in German
The task of relation linking in question answering is the identification of the relation (predicate) in a given question and its linking to the corresponding entity in a knowledge base. It is an important step in question answering, which allows us afterwards to build formal queries against, e.g., a knowledge graph. Most of the existing question answering systems focus on the English language and very few question answering components support other languages like German. The goal of this thesis is to identify from the literature as well as develop relation extraction tools that could be adapted to work for German questions.
B, M Dr. Ioanna Lytra
Query Decomposer and Optimizer for querying scientific datasets (assigned)
The amount of scientific datasets has increased dramatically in recent years. Copernicus data repository – http://www.copernicus.eu/ is a prominent example of a collection of datasets related to climate, atmosphere, agriculture, and marine domains, publicly available on the Web. Until now, scientists have to look for the appropriate datasets, download them, and query/analyze them using their own infrastructure. Being able to query/analyze scientific data without knowing about the underlying datasets is not at the moment possible. The goal of this thesis will be to create a query engine that will be able to query scientific datasets transparently, without being aware of the available datasets.
B, M Dr. Ioanna Lytra
Knowledge Data Containers with Access Control and Security Capabilities (assigned)
The amount of Linked Data both open, made available on the Web, and private, exchanged across companies and organizations, have been increasing in recent years. This data can be distributed in form of Knowledge Graphs (KGs), but maintaining these KGs is mainly the responsibility of data owners or providers. Moreover, building applications on top of KGs in order to provide, for instance, analytics, data access control, and privacy is left to the end user or data consumers. However, many resources in terms of development costs and equipment are required by both data providers and consumers, thus impeding the development of real-world applications over KGs. KGs as well as data processing functionalities can be encapsulated in a client-side system called Knowledge Graph Container, intended to be used by data providers or data consumers. The goal of this thesis is to integrate access control and security capabilities in these KG containers.
M Dr. Ioanna Lytra
Hidden Research Community Detection 2.0
Scientific communities are well known as research fields, however, researchers communicate in hidden communities that are built considering the types of communities considering the co-authorship, topic interest, attended events etc. In this thesis which will be the second phase of an already done master thesis, we will focus on identifying more of such communities by defining similarity metrics inside objects of a research knowledge graph we will build using several datasets.
M Sahar Vahdati
Movement of Research Results and Education through OpenCourseWare
This thesis is a research based work in which we will build a knowledge graph for OCW (online courses) and development of research topics considered in this KG, we will use an analytics tool to define interesting queries that can give us insights on answering the research question of how aligned is research with teaching material.
B, M Sahar Vahdati
Development and implementation of a semantic Configuration- and Change-Management
This thesis is offered in cooperation with Schaeffler Technologies AG & Co. KG. A solid knowledge of OWL and RDF is needed and a general interest in configuration and change management. The thesis is available and work environment is possible in English. A more detailed description in German is available here (pdf).
M Niklas Petersen
An Approach for (Big) Product Matching
Consider comparing the same product data from thousands of e-shops. However, there are two main challenges that make the comparison difficult. First, the completeness of the product specifications used for organizing the products differs across different e-shops. Second, the ability to represent information about product data and their taxonomy is very diverse. To improve the consumer experience, e.g., by allowing for easily comparing offers by different vendors, approaches for product integration on the Web are needed.
The main focus of this work is on data modeling and semantic enrichment of product data in order to obtain an effective and efficient product matching result.
M Dr. Giulio NapolitanoDebanjan Chaudhuri
Learning word representations for out-of-vocabulary words using their contexts.
Natural language processing (NLP) research has recently witnessed a significant boost, following the introduction of word embeddings as proposed by Mikolov et. al. (2013) (Distributed Representations of Words and Phrases and their Compositionality). However, one of the biggest challenges of using word embeddings using the vanilla neural net architecture with words as input and context as outputs is the handling of out-of-vocabulary (oov) words, as the model fails badly on unseen words. In this project we are suggesting an architecture using the proposed word2vec model only. Here, given an unseen word, we would predict a distributed embedding for it using the contexts it is being used in using the matrix that has learned to predict context given the word. (More details)
M Dr. Giulio NapolitanoDebanjan Chaudhuri
Reflecting on the User Experience Challenges of CEUR Make GUI and Harnessing the Experience: CEUR Make GUI
CEUR Make GUI is a graphical user interface supporting the workflow of publishing open access proceedings of scientific workshops via CEUR-WS.org, one of the largest open access repositories. For more details on the topic please go through the publications mentioned below. In this thesis we aim to work on improving the user experience challenges documented in the first part of the thesis as cited below and also on refactoring the current code base. We would like to address the challenges such as permissiveness of the input fields and displaying of feedback through well defined UI patterns.Email:Muhammad.rohan.ali.asmat@iais.fraunhofer.deCurrent Repository . Thesis . Publication . Task Board
B, M Rohan Asmat
Developing Collaborative Workspace for Workshop Editors: CEUR Make GUI
CEUR Make GUI is a graphical user interface supporting the workflow of publishing open access proceedings of scientific workshops via CEUR-WS.org, one of the largest open access repositories. For more details on the topic please go through the publications mentioned below. In this thesis we aim to work on producing a collaborative workspace for editing workshop proceedings and enhancing the user experience of the software. Based on the development of collaborative workspace we would also like to address the user experience and collaborative and cooperative workspace challenges through a structured protocol.Email:Muhammad.rohan.ali.asmat@iais.fraunhofer.deCurrent Repository . Thesis . Publication . Task Board
B, M Rohan Asmat
RDF compression techniques
As a starting point, realizing a fresh state-of-the-art of compression techniques for RDF could be made. These techniques can mainly be divided into two families: the ones that compress as much as possible datasets in order to make transfers easier (see e.g. the study of Fernández et al.) and the ones which still allow data to be queried (see e.g. the HDT structure). Secondly, a reflexion on a new compression model may be thought about and then realized/implemented successfully -obviously, we already have some suggestion which could help the student 😉 like for instance trying to compress the RDF graphs according to patterns which could be used in parallel of SPARQL query shapes.
M Dr. Damien Graux
Provide tools for LaTeX leveraging semantic web standards
When articles are written (and submitted to pear review), one of the biggest fear of researchers is to forget some state-of-the-art works. Indeed, articles should be positioned among the already existing ones to show they are new. However, a specific relevant paper can sometimes be forgotten by authors. To avoid this unpleasant situation, one could imagine a LaTeX package able to check if no citation is missing in a manuscript. To do so, several things might be implemented: (i) extending the already existing pdf2rdf tool by implementing a tex2rdf module; (ii) generating bib-code from these RDF data; (iii) extracting RDF data from the reference sections of articles; (iv) aggregating all these RDF data and loading this dataset into a store; (v) developing a LaTeX package which would be able to automatically query this endpoint to possibly provide missing references.
M Dr. Damien Graux

RDF2Résumé
In a nutshell, this topic aims at leveraging the Semantic Web standards in the context of professional descriptions. Indeed, to describe oneself, the common behavior is to write a CV (or a web-page, or both) which should in theory fit the targeted goal. More generally, these kinds of presentation can also be done by companies or large groups to present a whole team for tenders or proposals perspectives…

More precisely, the topic will give the opportunity to automatically realize the tasks of extracting/enriching people information to make them compliant with a specific goal.

In details, RDF2Résumé would imply the following distinct steps:
A] Converting RDF to professional material:
1. to design a CV vocabulary to be used after;
2. to be provide a simple tool (a simple piece of software such as a script) able to generate -let’s say- LaTeX/HTML code from an RDF file compliant with the aforementioned vocabulary;
3. in parallel to propose several final templates;
4. and finally to realize a basic user-interface; (+) to give the possibility of automatically changing languages.
B] Enabling semantic enrichments of the profiles using external sources.
C] Linking the tool with an already realized job platform to automatically link job seekers with job offers.

M Dr. Damien Graux
A block-chain forecast model: Extracting & analyzing user most used smart-contract features to predict block-chain future
In the recent past years, the block-chain concept [1] has become a key technology to record transactions between two parties while providing several properties. Up on the general block-chain architecture, some distributed computing platform have emerged such as the Ethereum [2] which gives the opportunity of building and deploying smart contracts [3]: automatic actions that can be triggered by specific events in the chain.By construction, block-chain-related technologies are open-source and publicly available which allows one user to check for instance the complete history of the chain or some specific events. Moreover, the structure of the chain itself can also be represented as a large knowledge graph.The goal of this study is to crawl the Ethereum block-chain smart-contracts history -leveraging the knowledge graph introduced above- in order to compute statistics and then try to predict the future of the chain. To do so, several steps have to be done:
1. being able to retrieve information inside the large RDF graph representing the chain using the SPARQL query language;
2. understanding the way smart-contracts are scripted;
3. deploying ML algorithm on these data excerpts;
4. drawing conclusions…
M Dr. Damien Graux
Semantic Integration Approach for Big Data
Dimension = Volume & Variety
Current Big Data platforms do not provide a semantic integration mechanism, especially in the context of integrating semantically equivalent entities that not share an ID.
In the context of this thesis, the student will evaluate and make the necessary extensions to the MINTE integration framework in a Big Data scenario.
Datasets: We are going to work with Biomedical Dataset
Programming Language: Scala
Frameworks: Ideally integrated in SANSA platform, but this is not a must.
References:
Synthesizing Knowledge Graphs from web sources with the MINTE framework
Semantic Join Operator to Integrate Heterogeneous RDF Graphs
MINTE semantically integrating RDF graphs
M Diego Collarana
Semantic Similarity Metric for Big Data
Dimension = Volume, Variety
Identifying when two entities, coming from different data sources, are the same is a key step in the data analysis process.
The goal of this thesis topic is to evaluate the performance of the semantic similarity metrics we have develop in a Big Data scenario.
So we will build a framework/operators of the semantic similarity functions and evaluate.We are going to work with the following metrics: GADES, GARUM, FCA (New to be develop) (See references).

Datasets: We are going to work with Biomedical Dataset.
Programming Language: Scala, Java
Frameworks: Ideally integrated in SANSA platform, but this is not a must.
References:
A Semantic Similarity Measure Based on Machine Learning and Entity Characteristics
A Graph-based Semantic Similarity Measure

M Diego Collarana
Embedding’s for RDF Molecules
The use of embeddings in the NLP community is already a common practice. Currently there are the same efforts in the Knowledge Graphs community.
Several approaches such as TransE, RDF2Vec, etc… propose models to create embeddings out of the RDF molecules.
The goal of this thesis is to extend the similarity metric MateTee (see references) with the state-of-the-art-approaches to create embedding from Knowledge Graph Entities.
Datasets: We are going to work with Knowledge Graphs such as DBpedia y Drugbank.
Programming Language: Phython
References:
A Semantic Similarity Metric Based on Translation Embeddings for Knowledge Graphs
http://usc-isi-i2.github.io/DL4KGS/
M Diego Collarana
Hybrid Embedding for RDF Molecules
Following with the topic discussed above, the goal in this thesis is to research about hybrid embeddings. i.e., combining Word Embeddings with Knowledge Graph embeddings.
This more a foundational research.

Programming Language: Python
References:
No references for the moment, part of the work is to find some related literature.
M Diego Collarana
RDF Molecules Browser
Forster serendipitous discoveries by browsing RDF molecules of data, specially focus on the facets/filters to promote knowledge discovery not intended initially.
Programming Language: ReactJS
References:
A Faceted Reactive Browsing Interface for Multi RDF Knowledge Graph Exploration
A Serendipity-Fostering Faceted Browser for Linked Data
Fostering Serendipitous Knowledge Discovery using an Adaptive Multigraph-based Faceted Browser
M Diego Collarana