DIY-CDR tool demonstration

DIY-CDR (Do-it-yourself Component Discoverer and Recommender) is a tool to help users to discover existing components when they want to DIY their own solutions.

It uses the Controlled Fully Automated Ontology Assisted Matching Strategy (C-FOAM) as the matching strategy. C-FOAM contains three matching algorithms at three different levels – String, Lexical and Conceptual (or Graphical).

The demonstration of DIY-CDR contains two scenarios. We use Yahoo! pipes to test and demonstrate DIY-CDR. When a user wants to build his own Yahoo! Pipe, he can use DIY-CDR to find existing Yahoo! Pipes and build his solution based on them.

Scenario One: search with one concept 

If the result of the interpreter in DIY-CDR is a concept, then DIY-CDR will illustrate the components that contain this concept in the annotation set. For example, if the input is “bringing”, then C-FOAM will find “bring” (string matching result) and “fetch” (lexical matching result). All the components that are annotated with "bring" or “fetch” will be illustrated.

Scenario Two: search with several concepts

In this scenario, the result of the interpreter in DIY-CDR is a context, which corresponds to a set of lexons. For example, if the end user's input is “finding place of news”, DIY-CDR will find the contexts identified with “news location finder”, “news producer finder” and “TV news reader”. As “location” is the synonym of “place”, DIY-CDR will select “news location finder” as the internal output. Afterwards, DIY-CDR will use a graph matching algorithm to check whether it matches any of the annotations from all the components.

Scenario of Naughty Boy Use Case

There are two non-technical end users in our story. Mary is a homemaker with a one-year old son called James. On a certain day when she was reading her newspaper in the living room, James discovered her iPhone and started banging his new toy. Therefore Mary decides to install an existing application call “naughty boy protector” from the onto-DIY app-store. When James started banging Mary’s phone again, the (Nabaztag) bunny, standing next to his crib, started speaking loudly with the same intonation as his mother “Do not touch the iPhone, James!” It continued reminding the boy to put down his mother’s iPhone until Mary came in, took her iPhone and laid it flat again and set the bunny to its initial state. Following happened within Onto-DIY. When James starts shaking Mary’s iPhone, its sensor senses the acceleration. This raw sensor information is sent to the execution environment in the network through the communication layer. The data semantics server checks the information source and finds the proper mapping of the concepts in the ontology base. Then it communicates with the ontology server. The ontology server finds a set of relevant semantic rules, which are stored in the commitment repository. Based on the SDT outcomes, the corresponding action message is send to the bunny (over the communication layer). When the bunny gets the message, it starts saying “Do not touch the iPhone, James!” repeatedly untill the bunny is set back with its initial state.

Initially, the technicians build a few software and hardware pieces. There are preinstalled ontologies (version 1.0, created by domain experts, knowledge engineers and ontology engineers), databases and the condition/action rules (including ontological commitments, SDTs and rules stored/implemented in the smart objects). One day, James starts realizing that shaking the iPhone always trigger the bunny and an angry mother. So he decided to smash the bunny. Onto-DIY got messages from the bunny, such as, its ears were continuously moved with force, or its switch button was tapped very rapidly. Unfortunately, the existing ubiquitous network did not know how to react. In the next part of this report, we will describe how Mary can create an application (DIY her own solution) in order to react on the new situation. After Mary will create her own application successfully, her new solution will be automatically uploaded to the community portal of Onto-DIY. New DIY ideas and concepts are shared and can now be used by other people as parts for the realization of their new DIY ideas or just as an inspiration. Together with the semantics, the DIY solutions will continuously evolve within the community.

Reference

Yan Tang, Christophe Debruyne, Johan Criel, Onto-DIY: A Flexible and Idea Inspiring Ontology-based Do-It-Yourself Architecture for Managing Data Semantics and Semantic Data, in proc. of the 9th international conference on ontologies, databases, and applications for semantics (ODBASE'2010), Springer Verlaag, LNCS 6427, p. 1036 ff. Hersonissou, Crete, Greece, Oct 26~28, 2010

Attachments

1. Bunny ontology
2. SDT xml file

Table4OLD Manual

The tool (web interface) can be accessed at  http://culturebrussels.appspot.com/

We can use Table4OLD (decision table for Open Linked Data, culturebrussels.appspot.com) to manage decision rules defined on top of domain ontologies. These decision rules are presented in the form of (semantic) decision tables. In the demonstration, we use a use case in the field of culture event and public transport in Brussels. We intend to show how easy a semantic decision table can be used as a user interface for non-technical people. In the meanwhile, it also gives enough technical transparency and modification possibilities to technicians and amateurs.

When you enter the URL to a Web browser, you'll see a screen as shown below. (note: all images here can be enlarged by clicking on them)

There are two GWT modules on this page - 1) Example Module and 2) the Module of Semantic Decision Table Editor and Executor - see below

1. Example Module 

When you click on the "Example" button from the Module of Example, then you'll load an example of SDT.

In this example, you can see a decision table, its annotation using ontologies and pictures. Note that the the format of decision tables follows the CSA 1970 standard, as illustrated at http://en.wikipedia.org/wiki/Decision_table

If you click on the "Annotation" button, then you'll see the annotation information of this SDT. The annotations have the format of lexons. Check http://starlab.vub.ac.be/website/node/45 for the explanations of lexons.

If you want to add colours to different groups of conditions, then click on the "Add Color" button. We want to provide a visualization facility to the decision modelers.

For instance, in the above decision table, we can see that the third condition entry uses only one condition stub for all the decision columns.

Click on the "Show Process Information" button if you want to track the server information.

If you don't want to be bothered by this Module, then click on the "Hide View" button. This Module will be hidden.

2. Module of Semantic Decision Table Editor and Executor

In the Module of Semantic Decision Table Editor and Executor, there is one component called semantic decision table editor. Knowledge engineers have modeled the domain ontology (-ies) and annotated the concepts concerning conditions and actions before this tool has been launched to the end users. Note that they can always update the ontologies and annotations, which are currently stored as Google documents (in particular, Google fusion tables) at https://www.google.com/fusiontables/DataSource?docid=1jRlTFuLMFZQ3XebjosdcakbjnVOFuzxbN1N-mSs

and the event data is stored at https://www.google.com/fusiontables/DataSource?docid=1QRrMFL9oJeHg21r8LQipekXB7FjSt-BGwOsKMyk

When you click on the "Get Stubs from O" button, you will get two types of labels. One is denoted by rectangles with a gray background, and the other is indicated by rectangles with a yellow background. The former is condition stubs and the latter is action stubs. Below these stubs, you will find two boxes indicated with "Condition" and "Action" (see the figure shown below).

You need to hold the left key of the mouse while dragging a condition stub into the Condition box and dragging an action stub into the Action box. After the label is dragged into a desired box, you may release the mouse and drop it.

After a condition stub is dropped to the Condition box, the module will load the ontology and find its predefined value types in the ontology. For example, Language has a value type of String, and its possible values are EN, FR, or NL. Then, the tip box that resides to the right of the Action box will show examples to help the decision modeler to specify the condition entries. For example, we want to have condition entries - "en, fr" - for the condition stub "Language". According to the tip, we enter "en;fr" in the input field and press the "submit" button (see the figure shown below). 

We repeat this step until all the desired conditions are entered. 

Then, we drag and drop the desired action stubs to the Action box. Unlike a condition, the entries of which needs to be specified by end users, an action does not need to have an action entries because the action entries of an action, by default, can only be Boolean values in this particular application. Note that in other cases, an action entry can as well be a String, which is out of the scope of this demonstration. 

Then, click the "Generate" button. You'll see an automatically generated semantic decision table, with a complete decision rule set as shown below. 

f

Now, we need to create decision rules. We click on an action stub in a decision column in order to activate this action. For example, we want to have a decision rule as "IF Language is en, AND isFullyBooked is Y, THEN ShowEventTitle". In order to have this rule, we only need to click on the condition stub in column 1 (as shown in the figure below). 

After we have modeled all the decision rules, we can click the "Execute" button in order to activate the decision columns that can be executed.  

For instance, when we click the "Execute 1" button, we will get a list of events, the language of which is English and is fully booked. What we see in the list is the event titles from those events (because of the "ShowEventTitle"action in column 1).

When you click on a condition stub, you will find the ontology model of the concept, to which the condition stub points. For example, if you click "Language", then you'll see a model as shown in the following figure.

Technical background: Table4OLD is developed using Google Web Toolkit (GWT, for a graphical part), JSON (as the communication means between the server and clients) and Google Map API (for a graphical part). It knowledge base contains ontologies, annotations and event data, which are stored as Google Fusion Tables. A pre-process of making the Google Fusion Tables from open linked data is realized by the Jena API.

OTM SeDeS 2010 Workshop

Today, the 1st International Workshop on Semantic & Decision Support (SeDeS'2010) successfully took place at at the Aldemar Knossos Royal Village Hotel and Conference Center in Hersonissou, Crete, Greece.

It is organized as a special session track from the 5th International Workshop on Enterprise Integration, Interoperability and Networking (EI2N'2010), which is one of the OTM conferences and workshops.

The call for papers saw 12 submissions, among which the SeDeS Programme Committee has selected 4 papers to be presented at EI2N'2010. 1 accepted paper has been withdrew at the publication preparation phase. Each submission was viewed by at least three and at most five members of the Programme Committee. The selected papers cover the topics of ontology-based decision making applications in the fields of eGovernment, eLearning, business rule management and Human Resource Management. 

1. Presentations

1.1. Re-engineering Business Rules for a Government Innovation Information Portal

Peter Spyns and Geert Van Grootel LNCS 6428, p. 565ff

Abstract. For any information system the quality of the underlying data is crucial for the quality of the information offered to a user. Quality control when uploading data into a portal is essential. In this paper we describe a practical case of how existing business rules are re-engineered into semantically grounded business rules that validate data before being uploaded into a government innovation information portal. Some practical experiences and lessons learnt are presented. The case has shown that not only a substantial gain in engineering time can been achieved but also that the support of related stakeholders is more easily obtainable.

1.2. A Personalized and Collaborative eLearning Materials Recommendation Scenario Using Ontology-Based Data Matching Strategies

Ioana Ciuciu and Yan Tang LNCS 6428, p. 575ff

Abstract. We propose a virtual teacher for the evaluation of students’ competencies. It aims to improve learning by making personalized suggestions on the learning materials. It is based on three main components: 1) a semantically enriched content management system (CMS), playing the role of knowledge base, 2) a 3D anatomy browser and 3) an ontology-based matching strategy called Controlled Fully Automated Ontology Based Matching Strategy (C-FOAM), providing the evaluation methodology. Together with the collaborative knowledge base, which allows knowledge to be represented in natural language and to be further reused, the evaluation methodology becomes the main contribution of the paper. The approach is demonstrated on a learning scenario illustrated around 3D anatomical structures.

1.3. Ontological-Based Model for Human Resource Decision Support System (HRDSS)

Rohayati Ramli, Shahrul Azman Noah, and Maryati Mohd Yusof LNCS 6428, p. 585

Abstract. This research concerns the development of Ontology-based model as an input to Human Resource Decision Support System (HRDSS) and to assist in the efficient and effective data analysis and leveraging the semantic content of ontology. These are to give intelligence support in decision-making and to proposed and develop suitable system architecture of the intelligence DSS model for the Human resource planning at national level. The initial model was developed based on the literature review on issues related to Human resource planning complex unstructured decision making process. We have been working on ontology to manage knowledge in human resource and integrate multiple data resources in order to support decision making in forecasting and projection for supply and demand in Human Resource Development.

2.Background

Decision support has been gradually evolved in both the fields of theoretical decision support studies and practical assisting tools for decision makers since 1960’s. The goal of decision support is to enhance decision processes in an accurate and efficient manner. Its application fields vary from business processes, business information management, system analysis, robotics, medical decision support, programming, project management, eGovernment & eBusiness, eLearning & eTraining, market analysis, judicial support, smart objects and ubiquitous systems.

Ontology Engineering (OE) brings new synergy to decision support. It will change (and actually now is changing) the decision support landscape, as it will enable new breeds of decision models, DSS applications and systems to be developed. Its realm will be significantly extended. By enabling digital intelligence in everyday decision support flows with interoperability and shareability, decision tasks and decision making processes in our workplaces, our homes, our businesses and even our own routine lives, could be simplified in a more efficient, more accurate way and more comfortable way.

SeDeS presents the latest innovations and achievements of academic/business/governmental communities on Decision Support Systems (DSS). The workshop focuses on theory, systems, computer aided methods, models, algorithms, techniques, methodologies and applications related to supporting decision making.

OTM SeDeS 2010 Workshop (accepted papers)

• Peter Spyns, Geert Van Grootel:
  Re-engineering Business Rules for a Government Innovation Information Portal. 565-574
  
• Ioana Ciuciu, Yan Tang:
  A Personalized and Collaborative eLearning Materials Recommendation Scenario Using Ontology-Based Data Matching Strategies. 575-584
  
• Rohayati Ramli, Shahrul Azman Noah, Maryati Mohd Yusof:
  Ontological-Based Model for Human Resource Decision Support System (HRDSS). 585-594
  

OTM SeDeS 2010 Workshop (photos)

From SeDeS

From SeDeS

From SeDeS

From SeDeS

From SeDeS

From SeDeS

From SeDeS

From SeDeS

From SeDeS

From SeDeS

From SeDeS

From SeDeS

From SeDeS

OTM SeDeS 2011 Workshop (accepted papers)

• Sonda Hbaieb, Souhail Dhouib, Habib Chabchoub:
  A Decision Support System Based on Metaheuristic Model for Aircrafts Landing Problems. 571-580
  
• Nadejda Alkhaldi, Christophe Debruyne:
  Comparing XML Files with a DOGMA Ontology to Generate Ω-RIDL Annotations. 581-590
  
• Willem De Roover, Jan Vanthienen:
  On the Relation between Decision Structures, Tables and Processes. 591-598
  
• Cristián Vásquez, Ioana Ciuciu:
  Ontology-Based Approach for Context-Dependent Annotation Templates Recommendations. 599-603
  
• Wided Guédria, Yannick Naudet, David Chen:
  Maturity Model as Decision Support for Enterprise Interoperability. 604-608
  

OTM SeDeS 2012 Workshop

The OTM SeDeS workshop has a LinkedIn Group. It's address is http://www.linkedin.com/groups/International-Semantics-Decision-Support-SeDeS-4521314. We welcome the workshop participants to join this group.

On the basis of request, the presentations, papers, photos and discussions from this workshop will be published in this LinkedIn Group.

For more photos, see: https://picasaweb.google.com/108269558167122518691/SeDeS

OTM SeDeS 2012 Workshop (accepted papers)

1. Nondeterministic Decision Rules in Classification Process
   Piotr Paszek, Barbara Marszał-Paszek
2. A Novel Context Ontology to Facilitate Interoperation of Semantic Services in Environments with Wereable Devices
   Gregorio Rubio Cifuentes, Estefanía Serral Asensio, Pedro Castillejo Parrilla, José Fernán Martinez Ortega
3. Ontology Based Method for Supporting Business Process Modeling Decisions
   Avi Wasser, Maya Lincoln
4. Survey on semantic sensor network ontology for integrating Internet of Things data
   Jiehan Zhou, Timo Ojala, Jukka Riekki
5. Modeling Decision Structures and Dependencies
   Feng Wu, Laura Priscilla, Mingji Gao, Filip Caron, Willem De Roover, Jan Vanthienen

OTM SeDeS 2012 Workshop (presentations)

The presentations can be downloaded from this page.

A Novel Context Ontology to Facilitate Interoperation of Semantic Services in Environments with WereableDevices
Gregorio Rubio, Universidad Politécnica de Madrid, Madrid (Spain)
Estefanía Serral, Universidad Politécnica de Valencia, Valencia (Spain)
Pedro Castillejo, Universidad Politécnica de Madrid, Madrid (Spain)
José Fernán Martínez, Universidad Politécnica de Madrid, Madrid (Spain)

Presentation

Nondeterministic Decision Rules in Classification Process
Piotr Paszek, University of Silesia, Sosnowiec (Poland)
Barbara Marszał-Paszek, University of Silesia, Sosnowiec (Poland)

Presentation

Sensor Information Representation for the Internet of Things
Jiehan Zhou, University of Oulu, Oulu (Finland)
Teemu Leppänen, University of Oulu, Oulu (Finland)
Meirong Liu, University of Oulu, Oulu (Finland)
Erkki Harjula, University of Oulu, Oulu (Finland)
Timo Ojala, University of Oulu, Oulu (Finland)
Mika Ylianttila, University of Oulu, Oulu (Finland)
Chen Yu, Huazhong University of Science and Technology, Wuhan (China)

presentation

On Semantics in Onto-DIY (short paper)
Yan Tang, Vrije Universiteit Brussel, Brussels (Beglium)
Zhenzhen Zhao, Télécom SudParis, Yvry (France)
presentation 

Ontology Based Method for Supporting Business Process Modeling Decisions
Avi Wasser, University of Haifa, Haifa (Israel)
Maya Lincoln, ProcessGene Ltd (Israel)

presentation
 

Modeling Decision Structures and Dependencies
Feng Wu, K U Leuven, Leuven (Belgium)
Laura Priscilla, K U Leuven, Leuven (Belgium)
Mingji Gao, K U Leuven, Leuven (Belgium)
Filip Caron, K U Leuven, Leuven (Belgium)
Willem De Roover, K U Leuven, Leuven (Belgium)
Jan Vanthienen, K U Leuven, Leuven (Belgium)

presentation

Knowledge mining approach for optimization of inference processes in rule knowledge bases (short paper)
Agnieszka Nowak-Brzezinska, University of Silesia, Sosnowiec (Poland)
Roman Siminski, University of Silesia, Sosnowiec (Poland)

(absent)

Panel discussion (EI2N and SeDeS'2010)

We had a panel discussion on the role of Ontology Engineering (OE) in Enterprise Interoperability (EI) in the afternoon, on Oct. 27th, 2010. It lasted for an hour and half. Critical issues concerning OE and EI have been discussed. We really appreciate how active all the participants were to pose and answer questions.

The slides used for the panel discussion can be downloaded here.

Background

Decision support has been gradually evolved in both the fields of theoretical decision support studies and practical assisting tools for decision makers since 1960’s. The goal of decision support is to enhance decision processes in an accurate and efficient manner. Its application fields vary from business processes, business information management, system analysis, robotics, medical decision support, programming, project management, eGovernment & eBusiness, eLearning & eTraining, market analysis, judicial support, smart objects and ubiquitous systems.

Ontology Engineering (OE) brings new synergy to decision support. It will change (and actually now is changing) the decision support landscape, as it will enable new breeds of decision models, DSS applications and systems to be developed. Its realm will be significantly extended. By enabling digital intelligence in everyday decision support flows with interoperability and shareability, decision tasks and decision making processes in our workplaces, our homes, our businesses and even our own routine lives, could be simplified in a more efficient, more accurate way and more comfortable way.

SeDeS presents the latest innovations and achievements of academic/business/governmental communities on Decision Support Systems (DSS). The workshop focuses on theory, systems, computer aided methods, models, algorithms, techniques, methodologies and applications related to supporting decision making.

Formalizing ontological commitments using Modal Logic.

Problem title: ontological commitment in Modal Logics

Contact: Yan Tang

Problem Description: Applications (such as semantic decision support systems, especially SDT) commit to an ontology through ontological commitments (also called application axiomatizations). As many different applications may commit to the same ontology, one may ask what is the semantic relationship (similarity/difference) between these applications. You will formalize this relationship using Modal Logic, which allows logical reasoning cross situations. The notions of ontology and ontological commitment are described and formalized (in first-order-logic) in chapter 3. Please have a look and don't hesitate to contact us if you have any question.

Research Category 1 (not required for internships):

Research issues : Modal logic, formalization of concept/ ontological commitments, proof theory, etc.

skills: logic, DOGMA, ontologies.

further readings:

1) M. Fitting, Basic Modal Logic, Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 1, Logical Foundations, 1993.

2) E. Clarke and O. Grumberg and D. Peled, Model Checking, The MIT Press, 1999.

Research Category 2 (required for internships):

Research issues: commitment visulaization based on Modal Logics, logics and verification,

skills:Java Eclipse Plugin development

further readings:

1) M. Fitting, Basic Modal Logic, Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 1, Logical Foundations, 1993.

2) Modal Logic by John McCarthy

3) Introduction of Modal Logic by Anthony A. Aaby

4) 1. T. R. Gruber. A translation approach to portable ontologies. Knowledge Acqui-sition, 5(2):199-220, 1993. 2.

5) T. R. Gruber. Toward principles for the design of ontologies used for knowledge sharing. Workshop on Formal Ontology, Padova, Italy, 1992.

SDRule-L graphical presentation

Problem title: SDRule-L graphical presentation

Contact: Yan Tang

Problem Description: Applications (e.g. decision support systems, especially SDT) commit to an ontology through ontological commitments (also called application axiomatizations). Ontology based decision making systems mainly contain semantically grounded decision rules, which can be modeled using SDRule-L, an extension of ORM. There are currently 7 extra operators and connectors in SDRule-L. Its markup language is a hybrid language of ORM-ML and FOL RuleML.

You will build an extension to the existing tool to visualize and model these rules using SDRule-L. The logical operators and connectors allow logical reasoning cross situations. You can find more details on the rule writing of ontological commitment here.

Please have a look and don't hesitate to contact me if you have any question.

Research Category:

Research issues : logic, proof theory, etc.

skills: logic, DOGMA, ontologies, Eclipse and Eclipse plugin development, JGF, Java.

further readings:

1. M. Fitting, Basic Modal Logic, Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 1, Logical Foundations, 1993.
2. T. R. Gruber. A translation approach to portable ontologies. Knowledge Acqui-sition, 5(2):199-220, 1993. 2.
3. T. R. Gruber. Toward principles for the design of ontologies used for knowledge sharing. Workshop on Formal Ontology, Padova, Italy, 1992.
4. T. A. Halpin, Information modeling and relational databases: from conceptual analysis to logical design, San Francisco, California, Morgan Kaufman Publishers, 2001.
5. Yan Tang, Peter Spyns, Robert Meersman, Towards Semantically Grounded Decision Rules Using ORM+, Proc. of International RuleML Symposium on Rule Interchange and Applications (RuleML'07), in, Adrian Paschke and Yevgen Biletskiy (eds.), Springer Verlag, LNCS 4824.
6. Damien Trog; Jan Vereecken; Stijn Christiaens; Pieter De Leenheer; Robert Meersman , T-Lex: A Role-based Ontology Engineering Tool , ORM 2006, 02/11/2006, Volume 4278, Montpellier, France, (2006)

Student: Yan Gu (till 1st Feb., 2008), Christophe Debruyne (current)

Francisco Martinez Garcia

Bio

Francisco Martinez, born in Granada (Spain), got his Professional Bachelor in Applied Informatics in 2007 and his Computer Science Engineering in 2011. In the same year he joined the VUB for the Master in Computer Science Since 2007 He worked as Programmer in Oracle solutions in Granada, in 2010 He got a Grant in junior research in the University of Granada. In 2012, Francisco joined Euroclear Bank as Analyst BI Developer. He is responsible of the Develop and Support in Windows DWHs for Banking and Finance.

Master Thesis Topic: Towards Using Semantic Decision Tables for Business Process Management in SBVR

Thesis Description

Keywords: Ontology Engineering and Technologies, Semantic Decision Tables, Financial, Banking,

Expected result: The goal of this research is to use semantic technologies to enhance Business Process Management. In particular, we want to use semantic decision tables (SDT[1][2]) to control decisions in SBVR.

This thesis is considered as a follow-up work of Specifying Process-Aware Access Control Rules in SBVR [5]. The idea is to use semantic decision tables for modeling decision rules that are used in controling processes from SBVR [6]. 

Background 

SDT is modeled in the DOGMA framework, which formulates ontology-based applications as three layers: The ontological lexon, the ontological commitment, the application layer. The BPM is a methodology designed to organize, manage, analyze and reengineer the processes running in an organization, their lifecycles has been increasingly supported by a set of software technologies that have as results BPM System that are already used by both business and IT engineers. The Ontology is a logical theory accounting for the intended meaning of a formal vocabulary, it reflects commitments, and conceptualization.

In DOGMA, consists of the following two layers: a lexon layer and a commitment layer. A lexon is a binary fact type, which can be formalized as . t1 and t2 are two terms that point to two concepts or objects. r1 and r2 are the two roles that t1 and t2 can play with. r is the context identifier, which points to where t1 and t2 are originally defined and disambiguated and where r1 and r2 are meaningful. A commitment layer is a view from an application on how to use lexons. The goal of the Master Thesis: It is illustrate how we can use semantic in the BPM in order to help to bridging the gap between business and IT will improve using semantic technologies, identifying for each step of the BPM the required functionalities. The implementation of a Business Semantics Glossary allows you to capture any kind of formal semantic relation up to formal ontologies and business rules could provide an semantic layer has a huge potential in this domains, and interest to the advantages over the traditional report design and remove the most difficult data intensive task.

Literature

1. Yan Tang, On Semantic Decision Tables, PhD thesis, VUB STARLab, 2009
2. Tang, Y.,and Meersman, R. (2008):Towards Building Semantic Decision TableswithDomain Ontologies, in book"Challenges in Information Technology Management", M.C.Chan et al.(eds.), ISBN 978-981-281-906-2, 981-281-906-1, World Scientific
3. Peter Spyns, Yan Tang and Robert Meersman, An Ontology Engineering Methodology for DOGMA, Journal of Applied Ontology, special issue on "Ontological Foundations for Conceptual Modeling", Giancarlo Guizzardi and Terry Halpin (eds.), Volume 3, Issue 1-2, p.13-39 (2008), 
4. Simperl, E. and Sure, Y. (2008). The business view: Ontology engineering costs. In Hepp,M., de Leenherr, P., de Moor, A., and Sure, Y., editors,Ontology Management: Semantic Web, Semantic Web Services, and Business Applications. Springer
5. Goedertier, S., Mues, C., and Vanthienen, J. (2007), Specifying Process-Aware Access Control Rules in SBVR, in Paschke, A. and Biletskiy, Y., editors, Advances in Rule Interchange and Applications, Proceedings of The International RuleML Symposium (RuleML 2007), Lecture Notes in Computer Science (Springer), volume 4824, pp. 39-52
6. OMG SBVR website

Ioannis Arampatzis

Short Bio

Ioannis Arampatzis comes from Greece.

Master Thesis Topic

Towards Managing Semantic Business Rules for Internet-of-Things

Thesis Description

Keywords: Ontology Engineering, business rule modeling, process rule modeling, conceptual modeling, Internet-of-Things, open linked data, decision table, Java, Android, Web Server application, ontology technologies (Jena, SPARQL, owl, RDF(s)), smart environment simulation.

Expected result: Android application for managing ontology-based business rules for Internet-of-Things (IoT) or for open linked data.

Internet of Things (IoT) is an Internet where objects are connected together into a signle and coherent network. The word "Thing" refers to an object that can communicate, interact, exchange data, information and even knowledge with other objects. IoT is a digital overlay of physical world comprised with this kind of smart objects.

(IoT application, source: http://images.gizmag.com/hero/the-internet-of-things-moves-a-step-closer-with-open-source-ultra-low-power-jennet-ip.jpg)

The issue of scalability¹ has been always a major challenge in IoT, which brings ontology into the theme. An ontology is a specification of a conceptualization. The term "Ontology"is borrowed from philosophy, where an Ontology is a systematic account of Existence. For systems, what "exists" is that which can be represented. When the knowledge of a domain is represented in a declarative formalism, the set of objects that can be represented is called the universe of discourse (UoD). This set of objects, and the describable relationships among them, are reflected in the representational vocabulary with which a knowledge-based program represents knowledge. We can describe the ontology of a program by defining a set of representational terms. In such an ontology, definitions associate the names of entities in the UoD with human-readable text describing what the names mean, and formal axioms that constrain the interpretation and well-formed use of these terms [1].

An ontology is easy to scale because of the famality and supporting reasoning engines. It gives the possibility to easily scale an existing IoT framework. For instance, when we want to introduce new sensors, machines and software components to an existing smart home. It is possible to annotate them using domain ontologies (if the types² have been already formalized in the ontologies) or create new ontological models in the domain ontologies (if the types are new and not yet represented in the ontologies).

Another major research issue in IoT is how we can easily manage business rules (especially decision rules) over IoT. There exist several decision support technologies, such as decision trees, decision tables, Baysian nets, balanced score cards, decision models etc. A Semantic Decision Table (SDT [2]) is a decision table properly annotated and formalized using ontologies. The implicit decision rules and meta-rules, such as the ones of describing dependencies among conditions, among actions, between conditions and actions, and, across decision tables, are properly modeled as application-specific ontological models³.

Unlike other decision support tools, on the one hand, SDT is business-oriented, meaning that it is suitable for being used by non-technical business people.  On the other hand, SDT and its supporting methodologies and tools have the possibility of formalizing business requirements into desired formats, such as XML-based Semantic Decision Rule Language (SDRule-L, [4]), Description Logic [5], OWL [6].

In this thesis, we want to use SDTs to model business rules over domain ontologies. In return, new business requirements will be captured and proposed to enrich the ontologies. The following knowledge items will be enriched and formalized.

• Annotation set
• Concepts and concept definitions
• Roles and relations
• Instances and data
• Axioms, constraints and process rules

The expected result is an Android application, which can be deployed as a mobile or tablet application (see the screenshot in the next paragraph). It is considered as a portal to communicate with an ontology server, a database server and a business rule server (these three servers can be merged into one).

A possible use case is illustrated in the following figure. The decision rules are indicated in the scaned use case.

Footnotes:

1. Scalability is an ability of a system, which can be expanded easily according to increasing demands.
2. Type is a set containing a number of things or persons sharing a particular characteristic, or set of characteristics, that causes them to be regarded as a group, more or less precisely defined or designated; It is, to some extent, the synonym of class or category.
3. An application ontology is extracted and abstracted from domain ontologies  [3]. It is application specific and has a certain level of abstraction, which can be targeted for, e.g., enhancing readability of human beings, or, in an inverse dimention, for improving machine interoperability.

References:

[1] http://www-ksl.stanford.edu/kst/what-is-an-ontology.html
[2] Yan Tang, Semantic Decision Tables - A New, Promising and Practical Way of Organizing Your Business Semantics with Existing Decision Making Tools , ISBN 978-3-8383-3791-3, LAP LAMBERT Academic Publishing AG & Co. KG, Saarbrücken, Germany, 2010. http://www.amazon.co.uk/Semantic-Decision-Tables-Promising-Organizing/dp/3838337913
[3] Zhao, G., and Meersman, R., (2005): Architecting Ontology for Scalability and Versatility, OTM Springer LNCS 3761/2005, pp1605-1614, DOI: 10.1007/11575801_42 (see the attached paper)
[4]Yan Tang and Robert Meersman, SDRule Markup Language: Towards Modeling and Interchanging Ontological Commitments for Semantic Decision Making, Chapter V (Section I) in Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches, IGI Publishing, ISBN: 1-60566-402-2, USA, 2009, abstract http://www.igi-global.com/bookstore/titledetails.aspx?TitleId=35856 (see the attachment list)
[5]Yan Tang Demey and Trung Kien Tran, Using SOIQ to Formalize Semantics within one Semantic Decision Table, 6th International Symposium on Rules, RuleML 2012, Springer LNCS 7438
[6]Yan Tang and Robert Meersman, Towards Directly Applied Ontological Constrains in a Semantic Decision Table, in proc. of 5th International Symposium on Rules, RuleML 2011, Springer LNCS 7018, pp. 193-207, Frank Olken, Monica Palmirani, Davide Sottara (eds.), Nov. 3-5, 2011, Ft. Lauderdale, FL, USA http://www.springerlink.com/content/6324u18562pqq671/ ,

Other reading materials can be downloaded in the list (see below).

Sizhe Xi

Motivation

• to research on ontology engineering for the master project (Semantic actuation architecture in WSN)

Expected duration

• from July, 2010 till July, 2011

Obtained Degree

• Bachelor of Communication Engineering, School of Information Engineering, East Jiaotong University of China

Academic Activities

• March 2006~August 2006, Irkutsk State Railway Transport University, Russia, exchange student
• January 2006~August 2007, University ACM team, East Jiao Tong University, China, winning 3rd place in 2007 Nanjing ACM contest
• October 2007~ August 2008, Telecommunication Engineering and Information System Lab, East Jiao Tong University, china, Designing hardware interface and software in project of software -controlled GPS receiver, mainly in DSP and FPGA
• 2007~now, School of Software, East Jiao Tong University, China. Part-time job as a programmer working on designing desktop and web applications in java, flex and AIR

Research Domains

• Infrastructure development,
• WSN
• sensor network
• zigbee
• Agent learning
• Pattern recognition
• Software application design
• web application
• mobile application design 
• OSGI java modular system

Researching Projects

• Smart home automation project, mainly focus on applying the ZIGBEE, RFID technology to monitor and control the room remotely in various ways such as mobile and web application, and publish all room status as a SOA service which is implemented with restful service.
• Activity recognition, which is about recognition of human activity by analyzing Accelerometer data collected from mobile phone, I am currently designing the architecture and applying all different AI techniques to recognize what this person is doing in certain time, such as walking, running, sitting or standing still so forth and so on..

Skills

• Language: Chinese (mother tone), English (expert level)
• Software:
• Programming
• Java
• C
• actionscript 3
• Environment
• Flex
• Java Eclipse
• Design:
• UML
• ORM
• Database
• SQL
• Hardware design:
• DSP
• ZIGBEE
• UART
• FPGA

Sjoerd Dufoer

Short Bio

Sjoerd Dufoer, born in Bruges (Belgium), got his Bachelor in applied informatics in 2005. In 2006, Sjoerd joined Belgian Federal Public Service Finances as a solution designer of integration systems. He is responsible for  the exchange of data with other institutions and enterprises. In 2008, he started as a work student from Vrije Universiteit Brussel.  He joined  VUB STARLab in 2012 as a master student. 

Master Thesis Topic

Towards Visualizing and Validating SDRule-L Models in a real Business Case

Thesis Description

Keywords: Ontology Engineering, data modeling, conceptual modeling, Web Server application, ontology technologies (Jena, SPARQL, owl, RDF(s)), web technologies

Expected result: A graphical tool for visualizing and validating SDRule-L models. The models can be stored in SDRule-ML.

An ontology is a semiotic representation of agreed conceptualization in a subject domain. It can be modeled in form of data models. Ontologies are becoming increasingly important due to the emerge of ontology based applications, like the Semantic Web and for ontology-based data modeling. During the design and development of ontology-based application, the semantics of application rules needs to be precisely modeled and checked with their respective domain.

An ontology, in DOGMA, consists of the following two layers: a lexon layer and a commitment layer. A lexon is a binary fact type, which can be formalized as <r, t1, r1, r2, t2>. t1 and t2 are two terms that point to two concepts or objects. r1 and r2 are the two roles that t1 and t2 can play with. r is the context identifier, which points to where t1 and t2 are originally defined and disambiguated and where r1 and r2 are meaningful. A commitment layer is a view from an application on how to use lexons. Typically, it contains a constraint, such as each t1 plays the role of r1 for exactly twice.

A commitment can be modeled graphically using different languages, e.g. UML, ER, ORM/ORM2 etc. In this thesis, we will use Semantic Decision Rule Language (SDRule-L) to model commitments. SDRule-L is an extension to Object Role Modeling Language (ORM/ORM2). An example of SDRule-L is shown as below.

We will study how to model, visualize, verify and interchange SDRule-L models in order to support ontology-based decision making. Furthermore, it will be examplified for a particular business case. The inquiry of validation of the commitments and constraints is the main question of subject we will research. SDRule-L models are stored in an XML-based markup language called Semantic Rule Markup Language (SDRule-ML).

Up to today there is no real tool where it is possible to verify the SDRule-L models or SDRule-ML files, export the models to SDRule-ML,  and visualize data annotated with an SDRule-L model.  The challenges thus become the ones in this thesis. We will also illustrate our effort with a real-life business case containing data models from a big enterprise/government institution. Possible opportunities and challenges of SDRule-L will be answered.

Literature

[1] http://www-ksl.stanford.edu/kst/what-is-an-ontology.html

[2] Yan Tang, On Semantic Decision Tables, PhD thesis, VUB STARLab, 2009

[3] Zhao, G., and Meersman, R., (2005): Architecting Ontology for Scalability and Versatility, OTM Springer LNCS 3761/2005, pp1605-1614, DOI: 10.1007/11575801_42

[4]Yan Tang and Robert Meersman, SDRule Markup Language: Towards Modeling and Interchanging Ontological Commitments for Semantic Decision Making, Chapter V (Section I) in Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches, IGI Publishing, ISBN: 1-60566-402-2, USA, 2009, abstract http://www.igi-global.com/bookstore/titledetails.aspx?TitleId=35856

[5]Terry Halpin, Tony Morgan (2008): Information Modeling and Relational Databases, Second Edition, The Morgan Kaufmann Series in Data Management Systems, 976 pages, Morgan Kaufmann, March 17, 2008, ISBN-10: 0123735688, ISBN-13: 978-0123735683

[6] Peter Spyns, Yan Tang and Robert Meersman, An Ontology Engineering Methodology for DOGMA, Journal of Applied Ontology, special issue on "Ontological Foundations for Conceptual Modeling", Giancarlo Guizzardi and Terry Halpin (eds.), Volume 3, Issue 1-2, p.13-39 (2008), http://portal.acm.org/citation.cfm?id=1412421

[7] Yan Tang and Damien Trog, Model Ontological Commitments Using ORM+ in T-Lex, in proc. of ORM workshop, OTM 2008, Springer Berlin / Heidelberg, ISBN 978-3-540-88874-1, Volume 5333/2008, pp. 787-796, 9-14 Oct. 2008. http://www.springerlink.com/content/u377342150707751/

[8] Yan Tang, Peter Spyns and Robert Meersman, Towards Semantically Grounded Decision Rules Using ORM+, Proc. of International RuleML Symposium on Rule Interchange and Applications (RuleML'07), in, Adrian Paschke and Yevgen Biletskiy (eds.), Springer Verlag, LNCS 4824, pp.78-91,October 25-26, 2007, Orlando, Florida, http://portal.acm.org/citation.cfm?id=1785393

[9] Description logic: http://www.cs.ox.ac.uk/people/ian.horrocks/Publications/download/2007/BaHS07a.pdf

[10] Decision Ontology: http://code.google.com/p/decision-ontology/,

http://www.google.be/url?sa=t&rct=j&q=&esrc=s&source=web&cd=6&cad=rja&ved=0CEoQFjAF&url=http%3A%2F%2Fdecision-ontology.googlecode.com%2Ffiles%2Fdecision_ontology_outline_v02.pdf&ei=fo6GUJedCMeZ0QWUyoGQBw&usg=AFQjCNE_uRAmUJmbT1g-QNVByEpq9_UX1Q&sig2=BRGl1W8t07WYIQX1ZDzXfg