Knowledge Engineering and DIscovery (KEDI)

The Knowledge Engineering and DIscovery research group makes sense of data. We extract simple statements from unstructured texts or complex linked data sets, integrate them into consolidated perspectives and derive new facts and their explanations from that. This way we empower people with disruptive knowledge discovery methods, focusing especially on the domains of life sciences and digital humanities.

The research of the KEDI group focuses on:

Data Integration
Graph-based Knowledge Representation
Graph Mining and Statistical Relational Learning

“Our Knowledge Engineering activities intend to help data consumers and publishers to manipulate the data by providing innovative ways to search for relevant datasets/vocabularies and by providing new insights into their own data/vocabulary use.”

Knowledge Engineering

Knowledge Graphs – Graphs are useful for modeling objects, their relationships to each other, and the conceptual structures wherein which they lie. In our research, we explore the possibility to use both graph and geometric interpretations of the knowledge graph to support new type of analysis.

Ontology Design – The adoption of Semantic Web technologies (RDF, RDFS, OWL) has lead to the emergence of large datasets using richer representation models capturing the data semantics. The contribution of our research is to promote and facilitate the reuse of well documented ontologies or vocabularies in the Linked Data ecosystem.

Related technology: Linked Open Vocabularies

Linked Data Publication – Data providers have so far published hundreds of linked datasets and billions of triples on the Web, thus contributing to the birth of the so-called “Web of Data”. Nevertheless, Linked Data publication still faces problems that hinder its adoption. Our research focuses on monitoring and providing data publishers with empirical evidence of use and accessibility of their data.

Related technology: SPARQLES, LOD4All

“With knowledege graphs, scientists have now the means to aggregate semantic information from various sources. The challenge is not only to extract meaningful information from this data, but to gain knowledge, to discover previously unknown insight, look for patterns, and to make sense of the data.”

Knowledge Discovery

Graph Summarisation and Serendipitous Browsing – With more than millions nodes and edges, it is, for a human, almost impossible to understand the information encoded in large graphs. We are investigating graph summarisation methods to help users extract and understand the underlying information. Related technology: Semantic Text Exploration, Linkspire

Statistical Relational Learning – While a large majority of Machine Learning techniques focus on propositional data which is assumed to be identically and independently distributed, Many real world datasets are fundamentally based on complex relational structure where the data distribution is neither identical nor independent. An emerging research area, Statistical Relational Learning (SRL), attempts to learn in relational domain.

Distributional Data Semantics – Distributional data semantics is a bleeding edge field of research that aims at providing insights into the meaning of large and heterogeneous data sets in a bottom-up manner, using the implicit patterns emerging from the data themselves. The main contribution of our research consists of combining the classical distributional principles with graph analysis methods and symbolic reasoning. Related technology: CORAAL, SKIMMR

Tools & demos

Publications View all »

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Muñoz, Emir; Nováček,; Vandenbussche, Pierre-Yves

Facilitating Prediction of Adverse Drug Reactions by Using Knowledge Graphs and Multi-Label Learning Models (Journal Article)

Briefings in Bioinformatics, pp. 1-13, 2017.

(Links | BibTeX)

Mohamed, Sameh; Muñoz, Emir; Nováček,; Vandenbussche, Pierre-Yves

Identifying Equivalent Relation Paths in Knowledge Graphs (Inproceeding)

Gracia, Jorge; Bond, Francis; McCrae, John; Buitelaar, Paul; Chiarcos, Christian; Hellmann, Sebastian (Ed.): Language, Data, and Knowledge: First International Conference, LDK 2017, Galway, Ireland, June 19-20, 2017, Proceedings, pp. 299–314, Springer International Publishing, 2017.

(Links | BibTeX)

Muñoz, Emir; Nickles, Matthias

Mining Cardinalities from Knowledge Bases (Inproceeding)

Benslimane, Djamal; Damiani, Ernesto; Grosky, William; Hameurlain, Abdelkader; Sheth, Amit; Wagner, Roland (Ed.): Database and Expert Systems Applications: 28th International Conference, DEXA 2017, Lyon, France, August 28-31, 2017, Proceedings, Part I, pp. 447–462, Springer International Publishing, 2017.


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Latest News View all »

Translating Embeddings (TransE) – Knowledge Graph Mining

TransE is a textbook case link prediction model in multi-relational data (knowledge graphs). Learn more about Link Prediction models in this post presenting in a simple way Translating Embeddings (TransE), a method for the prediction of missing relationships in knowledge graphs:

Opening Position – Research Assistant – Knowledge Graph Construction

Insight, NUI Galway are in a collaboration with Fujitsu Laboratories, Limited. The collaboration focuses on research and development in the area of knowledge discovery and explanation from open biomedical data (e.g., drug and protein databases or scientific publications). As a part of the programme we are looking for a research assistant who will be located in Galway, Ireland and will help us with knowledge graph construction (extraction, consolidation and cleansing). The research will be realised in close collaboration with Fujitsu Laboratories in Japan and Fujitsu business units. This will be an exciting role with the opportunity to work on cutting-edge research that is immediately being applied in realistic proof of concepts as a part of our ongoing collaborations with experts in biology, medicine and pharmacology. Application and information can be found on Insight Website: Application end date: Friday, 15 September, 2017

Opening Position – Research Scientist in Relational Learning

Fujitsu Ireland is undertaking a research project in the area of knowledge discovery and explanation from open biomedical data (e.g., drug and protein databases or scientific publications). We are looking for a research scientist/post-doc who will be located in Galway, Ireland and will pursue research primarily in the area of statistical relational learning and discovery informatics. This will be an exciting role with the opportunity to work with a world class research team from both Insight and Fujitsu Laboratories with the emphasis on developing practical applications of cutting-edge research aimed at a global audience.

Job Purpose and Responsibilities

At a high level the chosen candidate will need to have the following capabilities:
  • Working within a team of researchers based locally in Ireland and internationally (Fujitsu Labs in Japan)
  • Working on industrially relevant research within multinational academic research settings
  • Delivering novel research as well as helping to implement the research outcomes in the form of prototypes
  • Occasional supervision of junior researchers in specific sub-projects
The person will be hired as a Fujitsu Ireland employee on a contract basis for the period of the current research project which will be until July, 2018 with a possibility of an extension in a follow-up programme.

Skills, Knowledge and Experience

PhD degree in Computer Science, Applied Mathematics, Bioinformatics or similar with a strong potential for pursuing independent research (proven by a track record of projects the applicant participated in and/or refereed publications)
  • Working knowledge of the following machine learning sub-fields:
    • Statistical relational learning
    • Unsupervised learning (e.g., clustering)
  • Hands-on experience with knowledge discovery from Graphs (e.g., RDF)
  • Knowledge of Python
  • Experience with at least one of the following scientific computing frameworks:
    • NumPy, SciPy and scikit-learn
    • MATLAB or GNU Octave
    • TensorFlow, Theano or Keras

Person Specification

Strong communications and writing skills Experience in research environment is an advantage Positive and enthusiastic team player

For the Successful Candidate

An opportunity to join one of the world’s most successful ICT Services and Solutions organisations Learning and progression prospects in a challenging environment Involved in a cutting edge project working with a highly talented team in close collaboration with a world class data analytics centre CVs to be submitted to by close of business on Friday the 15th of September 2017