Linköpings universitet > Department of Computer and Information Science > Division for Database and Information Techniques > Patrick Lambrix
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  Research - current projects  

Ontology Engineering - Ontology Alignment

Many ontologies contain overlapping information. Ontology alignment deals with finding mappings beween terms in different ontologies.


Ontology alignment framework and system. Development of frameworks for aligning and merging ontologies based on the computation of similarity values between terms in the source ontologies (basic and session-based). Development of an ontology alignment and merging system (SAMBO). (Winner of the Anatomy track of the 2008 Ontology Alignment Evaluation Initiative.) Development of ontology alignment strategies (preprocessing, matching, filtering).

Evaluation of ontology alignment strategies. Development of framework for evaluation and comparison of ontology alignment strategies. Development of an environment for evaluation and comparison of alignment strategies (KitAMO). (First environment of its kind.) Evaluation of alignment strategies and systems. (First larger evaluation of algorithms.)

Recommendation of ontology alignment strategies. Development of framework for recommending ontology alignment strategies. Implementation and evaluation of different recommendation strategies. (One of the first approaches for recommendation of strategies.)

Using partial alignments (PA) in ontology alignment. Development and evaluation of several strategies for using given correct mappings in ontology alignment: for preprocessing, within matchers, for filtering. (First approach for using PA in ontology alignment.)

User Support for Ontology Alignment. Development of requirements. Evaluation of state-of-the-art systems.

Development of a tool for visual exploration of multiple alignments (Alignment Cubes).

See also SAMBO, KitAMO, Alignment Cubes and my annotated bibliography.

Since 2013 our group organizes the Anatomy track of the Ontology Alignment Evaluation Initiative. Since 2015 our group co-organizes the Interactive track of the Ontology Alignment Evaluation Initiative.

Our group co-organized the International Workshop on Visualizations and User Interfaces for Knowledge Engineering and Linked Data Analytics in 2014. As a follow up we founded the Workshop on Visualizations and User Interfaces for Ontologies and Linked Data in 2015.

Ontology Engineering - Ontology completion and debugging

Developing ontologies is not an easy task and often the resulting ontologies are not consistent (logically) or complete (with respect to the domain). Such ontologies, although often useful, lead to problems when used in semantically-enabled applications. Wrong conclusions may be derived or valid conclusions may be missed. To deal with this problem we may want to complete and debug the ontologies.


Method and system for detecting and repairing missing and wrong is-a structure of single ontologies as well as in a network of ontologies. (First to study debugging missing is-a structure (or completing the is-a structure) with advanced repairing method.)
Formalization of the problem of repairing missing is-a structure as a new abductive reasoning problem.
Unified approach to debug is-a structure in and mappings between taxonomies.
Development of system RepOSE (Repairing of Ontological Structure Environment) - different versions for taxonomies, EL ontologies and ALC ontologies.

See also RepOSE and my annotated bibliography.

We co-founded the International Workshop on Debugging Ontologies and Ontology Mappings series (2012-2014). The scope was extended in 2016 and we co-organized the International Workshop on Completing and Debugging the Semantic Web.

Ontology Engineering - Ontology evolution

Ontologies are not static entities but evolve over time. This influences semantically-enabled applications, e.g., through the fact that concepts that are used in annotations can change their meaning and relationships towards other concepts.


User Support for Ontology Evolution. Development of requirements. Literature study of state-of-the-art systems.

See also my annotated bibliography.

Ontology Engineering - Applications

  • AHSO - Animal Health Surveillance Ontology
  • VALCRI - Visual analytics for sense-making in criminal intelligence analysis

Storage and querying for archetype-based electronic health record data

In this work we study the performance of different types of databases for storage and querying for archetyp-based electronic health record data.


Comparison of different NoSQL approaches.

See also paper and others in my annotated bibliography.

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