Posts Tagged ‘ontology’

ODBASE 2013: Accepted papers and Conference Programme

August 4, 2013

10 September

KEYNOTE by Prof. Dr. Manfred Hauswirth (DERI Galway), 08:30-10:00

Querying and Mining Semantic Information , 10:30:00-12:00:00

  1. Efficient Parallel Processing of Analytical Queries on Linked Data,
    Stefan Hagedorn, Kai-Uwe Sattler
  2. Analysis of User Editing Patterns in Ontology Development Projects,
    Hao Wang, Tania Tudorache, Dejing Dou, Natalya Noy, Mark Musen
  3. Complexity of Inconsistency-Tolerant Query Answering in Datalog+/–,
    Thomas Lukasiewicz, Maria Vanina Martinez, Gerardo Simari
  4. Preference-Based Query Answering in Probabilistic Datalog+/- Ontologies,
    Thomas Lukasiewicz, Maria Vanina Martinez, Gerardo Simari
  5. Towards a Cooperative Query Language for Semantic Web Database Queries,
    Stéphane Jean, Allel Hadjali, Ammar Mars

Semantic Matching and Mapping, 13:30:00-15:00:00

  1. The AgreementMakerLight Ontology Matching System,
    Daniel Faria, Catia Pesquita, Emanuel Santos, Matteo Palmonari, Isabel Cruz, Francisco Couto
  2. Flexible Matchmaking for RESTful Web Services,
    Fatma Slaimi, Sana Sellami, Omar Boucelma, Ahlem Ben Hassine
  3. Mix’n’Match: An Alternative Approach for Combining Ontology Matchers,
    Simon Steyskal, Axel Polleres
  4. Evaluation of Technologies for Mapping Representation in Ontologies,
    Olga Kovalenko, Christophe Debruyne, Estefanía Serral, Stefan Biffl

Semantic Information Management 1, 15:30:00-17:00:00

  1. Incremental Maintenance of RDF Views of Relational Data,
    Vania Vidal, Marco Casonava, Diego Cardoso
  2. Semantically Interlinked Notification System for Ubiquitous Presence Management,
    Qaiser Mehmood, Muhammad Intizar Ali, Ollie Fagan, Owen Friel, Alessandra Mileo
  3. Semantic Measures Based on RDF Projections: Application to Content-Based Recommendation Systems,
    Sébastien Harispe, Sylvie Ranwez, Stefan Janaqi, Jacky Montmain
  4. Analyzing Dimension Mappings and Properties in Data Warehouse Integration,
    Domenico Beneventano, Marius Octavian Olaru, Maurizio Vincini
  5. Imposing a semantic schema for the detection of potential mistakes in knowledge resources,
    Vincenzo Maltese

11 September:

Ontology Engineering, 10:30:00-12:00:00

  1. Semantic Enrichment of OLAP Cubes: Multi-dimensional Ontologies and their Representation in SQL and OWL,
    Bernd Neumayr, Christoph Schütz, Michael Schrefl
  2. Ontology driven information extraction from tables using connectivity analysis,
    Ashwin Bahulkar, Sreedhar Reddy
  3. Efficient Projection of Ontologies,
    Julius Köpke, Johann Eder, Michaela Schicho
  4. Using a Reputation Framework to Identify Community Leaders in Ontology Engineering,
    Christophe Debruyne, Niels Nijs
  5. Categorization of modeling language concepts: graded or discrete?,
    Dirk van der Linden

Social Event Afternoon

12 September

Semantic Information Management 2, 10:30:00-12:00:00

  1. Dealing with Context Ambiguity in Context-Based Information Re-finding,
    Tangjian Deng, Liang Zhao, Ling Feng
  2. Cognitive Modeling for Topic Expansion,
    Sumant Kulkarni, Srinath Srinivasa, Rajeev Arora
  3. Extended Tversky Similarity for Resolving Terminological Heterogeneities Across Ontologies,
    DuyHoa Ngo, Zohra Bellahsene, Konstantin Todorov
  4. From Theoretical Framework To Generic Semantic Measures Library,
    Sébastien Harispe, Stefan Janaqi, Sylvie Ranwez, Jacky Montmain
  5. Towards Efficient Stream Reasoning,
    Debnath Mukherjee, Snehasis Banerjee, Prateep Misra

Semantics in Use, 13:30:00-15:00:00

  1. Ontology-Based Semantic Annotation of Documents in the Context of Patient Identification for Clinical Trials,
    Peter Geibel, Martin Trautwein, Hebun Erdur, Lothar Zimmermann, Stefan Krüger, Josef Schepers, Kati Jegzentis, Frank Müller, Christian Hans Nolte, Anne Becker, Markus Frick, Jochen Setz, Jan Friedrich Scheitz, Serdar Tütüncü, Tatiana Usnich, Alfred Holzgreve, Thorsten Schaaf, Thomas Tolxdorff, Christian Hans Nolte, Anne Becker, Markus Frick, Jochen Setz, Jan Friedrich Scheitz, Serdar Tütüncü, Tatiana Usnich, Alfred Holzgreve, Thorsten Schaaf, Thomas Tolxdorff
  2. Can Ontologies Systematically Help in the Design of Domain-Specific Visual Languages?,
    Maria das Graças da Silva Teixeira, Ricardo de Almeida Falbo, Giancarlo Guizzardi
  3. A Data Space System for the Criminal Justice Chain,
    Jan van Dijk, Sunil Choenni, Erik Leertouwer, Marco Spruit, Sjaak Brinkkemper
  4. Exploiting social tagging in Web API search,
    Devis Bianchini, Valeria De Antonellis, Michele Melchiori

On Business Service Semantics

March 8, 2013

Below you can find the slides of my talk about this topic at the International Conference on Exploring Service Sciences in Porto, Feb 2013.

De Leenheer, P.; Cardoso, J.; Pedrinaci (2013) Ontological Representation and Governance of Business Semantics in Compliant Service Networks. In Proc. of IESS 2012, Springer, LNBIP 143, pp. 155–169

Business Service Semantics: Ontological Representation & Governance of Business Semantics in Compliant Service Networks from Pieter De Leenheer
Abstract: The Internet would enable new ways for service innovation and trading, as well as for analysing the resulting value networks, with an unprecedented level of scale and dynamics. Yet most related eco- nomic activities remain of a largely brittle and manual nature. Service- oriented business implementations focus on operational aspects at the cost of value creation aspects such as quality and regulatory compliance. Indeed they enforce how to carry out a certain business in a prefixed non-adaptive manner rather than capturing the semantics of a business domain in a way that would enable service systems to adapt their role in changing value propositions. In this paper we set requirements for SDL- compliant business service semantics, and propose a method for their ontological representation and governance. We demonstrate an imple- mentation of our approach in the context of service-oriented Information Governance.

The Virtue of Naming concepts

July 16, 2009

Everybody knows the Pizza Ontology that has been used for ages now to demonstrate tools and methods in the Semantic Web community. Nowadays the Beer Ontology is gaining interest, and I wonder how many concept types the Belgian beer namespace will consist, as there is no clear enumeration of that :-) Anyway, when talking about pizza or even about Belgian beers, we are still playing around with small ontologies.

(Too long) names to decontextualise the proliferation of concept types

Seriously, an ontology should refer to context-independent and language-neutral concepts. However, natural language (vocabulary etc.) is still needed to represent these concepts. Wittgenstein once said:

“The limits of my language means the limits of my world. “

When building large conceptual frameworks of thousands of concept types, vocabulary is usually exhausted before finishing. BTW, is the job ever finished given the proliferation of concepts in communities? Anyway, (as in natural language) terms will have different meanings depending on the context. E.g., the term  java can refer to coffee, a country, or a programming language. In the latter case we can even doubt whether we are talking about java as a sub-type or an instance of the concept type programming language. Let’s not see how deep the philosophical rabbit hole goes here. IMHO, in a formal semantic system we could consider to introduce a fuzzy parameter that can switch between both perspectives.

Now, let’s get back to the ambiguity problem of vocabulary. Lacking better solutions, many of these large ontologies have chosen very long labels to refer their concepts in an unambiguous manner (as the title of this blog already suggests). Usually, these labels are concatenations of a number of parameters that determine the context of the label. Consider, for example, the IFRS Taxonomy 2009 which is a complete translation of International Financial Reporting Standards (IFRSs) as of 1 January 2009 into XBRL:Picture 1

The label for the illustrated concept reads (first take a deep breath):


And this is not a single occurance. The IFRS taxonomy counts hundreds of concept labels fo this size. See for yourself:

Picture 2

This may be ok for one single person who built the ontology, and actually chose the labels, but when sharing it is not understandable for machines, or even other user. This situation creates a vicious circle: long labels are difficult to navigate, hence users introduce new concept types as they cannot retrieve what they are looking for. When defining these new concept types, they have no choice than to invent new labels “with the wet index”, inexorably aggravating the situation.


The problem is also found when people tend to overcategorise. This is an excerpt of a product taxonomy from Kevin Jenkins during a discussion on SemWeb on this matter:

Product (Root Class)
--- software
------ desktop software
----------- desktop internet software
------------------- desktop internet access software (individual)
------------------- desktop internet browser software (individual)
------------------- desktop internet messaging software (individual)
------------ desktop multimedia software
------------------- desktop multimedia 3d software (individual)
------------------- desktop multimedia audio software (individual)
------------------- desktop multimedia video software (individual)
------ internet software
------------ internet saas software
------------------- internet saas collaboration software (individual)
------------------- internet saas videosharing software (individual)
------------ internet cloud software
------ enterprise software

In order to differentiate a subtype from its parent, a term is appended to the more general label. According to Azamat Abdoullaev long classification is done according the scheme “noun specifying another noun”, like below:

((subsubclass)(subclass(class)): audio multimedia desktop software.

He compares it withe problem of URI schemes or computer directory (folder, catalog) names, it will be written as a root hierarchy:


However, this is not how humans talk to each other. Humans tend to contextualise their concepts through sentences in which they qualify certain attributes. This is done in terms of facts. E.g, following example shows 4 facts for this Person.

Person drives Car with Brand “Minerva” and married to Woman with Name “Athena”.

The fact types used here are:

Person drives Car
Car with Brand
Person married to Woman
Woman with Name

Hence, using simple fact types we can describe very complex concept types without even using categorisation in many cases. The terms used to refer to the concept types of course need to be disambiguated. There is no deus ex machina here: context is a social construct as well that has to be included in the ontology.

Context as first-class citizen

Context is an inexorable construct when representing ontologies. As I already discussed in an earlier publication. Particularly when stakeholders in a community use a different vocabulary to refer to the common concept types.

In our approach we use a context identifier g to articulate a term t with a concept type identifier c with the following function.


Hence, c is a URI that refers to a language-neutral and context-independent concept type. This can be represented in the WordNet manner in terms of a gloss (=informal description) plus a synset (=set of synonymous terms). For one of the terms on the above fact types this would be (based on WordNet):

(drivingfordummies, person)->(gloss,synset)
gloss="a human being"

Assuming that this fact type was extracted from a book called Driving for Dummies. So by keeping track of the context of elicitation g of very fact type, we can disambiguate the involved terms properly without the need for very long labels.

Further Reading

In my PhD, I developed a methodology that enacts a community to collaboratively construct an ontology architecture consisting of several layers (upper common, lower common, stakeholder level).

  • The top layer refers to language-neutral and context-independent concepts that are already agreed and applied by the community.
  • The lowest stakeholder layer consists of “stakeholder perspectives” on these upper layers, specialising the upper layer with locally relevant concept types represented by local vocabularies.
  • Gradually these lower perspectives are reconciled in the lower common layer, and when a new version is produced parts are promoted the upper common layer.

Hence community does not only have to agree on the concept types (gloss) but also on the preferred terms (synset) to refer to these concept types.

Ontology Management for the Semantic Web, Semantic Web Services, and Business Applications (Springer)

January 15, 2008

bookcoverManaging ontologies and annotated data throughout their life-cycles is at the core of semantic systems of all kinds. Ontology Management, an edited volume by senior researchers in the field, provides an up-to-date, concise and easy-to-read reference on this topic.

This book volume describes relevant tasks, practical and theoretical challenges, limitations and methodologies, plus available software tools. The editors discuss integrating the conceptual and technical dimensions with a business view on using ontologies, by stressing the cost dimension of ontology engineering and by providing guidance on how up-to-date tooling helps to build, maintain, and use ontologies. Also included is a one-stop reference on all aspects of managing ontological data and best practices on ontology management for a number of application domains.

Ontology Management is designed as a reference or secondary text for researchers and advanced-level students studying semantic systems, Semantic Web Services (SWS) and Web Services, information systems, data and knowledge engineering, and the Semantic Web in general. Practitioners in industry will find this work invaluable as well.

Hepp, M., De Leenheer, P., de Moor, A., and Sure, Y. (eds.) Ontology Management for the Semantic Web, Semantic Web Services, and Business Applications. Springer Series “Semantic Web and Beyond: Computing for Human Experience” . ISBN: 978-0-387-69899-1, November 2007.

Context Dependency Management in Ontology Engineering: a Formal Approach

June 12, 2007

jods_viii_deleenheer_fig1A viable ontology engineering methodology requires supporting domain experts in gradually building and managing increasingly complex versions of ontological elements and their converging and diverging interrelationships. Contexts are necessary to formalise and reason about such a dynamic wealth of knowledge. However, context dependencies introduce many complexities. In this article, we introduce a formal framework for supporting context dependency management processes, based on the DOGMA framework and methodology for scalable ontology engineering. Key notions are a set of context dependency operators, which can be combined to manage complex context dependencies like articulation, application, specialisation,
and revision dependencies. In turn, these dependencies can be used in context-driven ontology engineering processes tailored to the specific requirements of collaborative communities. This is illustrated by a real-world case of interorganisational competency ontology engineering.

De Leenheer, P., de Moor, A., and Meersman, R. (2007) Context Dependency Management in Ontology Engineering: a Formal Approach. Journal on Data Semantics VIII, LNCS 4380, Springer-Verlag, pp. 26-56.