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Semantic Technologies for Decision Support

- A Pattern-based Approach

STeDS is a research project at IDA-HCS, partly financed by CENIIT. The project started on January 1st 2012, and the project leader is Eva Blomqvist. Participants include also the PhD student Robin Keskisärkkä, as wells as additional collaborators from other organizations, and other groups at LiU.

Abstract. This 6-year project aims to assist the adoption of Semantic Web technologies in Decision Support Systems (DSS), based on the use of Ontology Design Patterns (ODPs), with specific focus on industrial actors. Applying ODPs in this context is novel; nevertheless, ODPs have in other contexts been successfully applied to reduce the complexity and cognitive load of using semantic technologies for both developers and users. Moreover, this project creates a forum for research on semantic technologies at IDA that was missing before this project started. Important results during the first 4 years (2012-2015) include, in addition to a set of methods, software components, and ODPs: 10 scientific publications, collaboration with three new university research groups, acquisition of four national project grants (from Vinnova and Energimyndigheten) and one EU FP7-Sec grant (resulting in increased industry involvement), involvement in standardization through three W3C groups, arranging two public workshops in 2013, one in 2014, and two in 2015, and holding a seminar series at LiU.

Content on this page:

  1. Project overview - the project in brief
  2. Project results (until mid-2015)
  3. Publications
  4. Project plan (with focus on year 5 - 2016)
  5. Detailed background, motivation and project plan (from initial application in 2011)

1. Project Overview - the project in brief

Project staff includes the project leader (Eva Blomqvist) and one PhD student (Robin Keskisärkkä). Initial industry supporters included Saab AB, VSL Systems, FOI, and the SPAWAR Pacific (US). Since the initial application we have expanded the national collaboration through our national grants, including a number of public authorities and several small companies, and international industry involvement is now, due to the VALCRI (EU FP7-Sec) project, being extended through collaboration with a set of new industry partners - for our specific focus areas mainly Space Application Services NV (Belgium), AE Solutions Ltd. (UK), and Object Security Ltd. (UK). Additional collaboration is being set up with the Pacific North west National Laboratory (US) on topics concerning automatic detection of new event pattern types. Locally in the region, ongoing negotiations with Saab AB and Combient AB, will potentially result in a joint demonstrator project.

The research is conducted based on the following general questions:

  • (Q1) What tasks (functionalities) in a DSS can be improved through semantic technologies, and what is the nature of the improvement?
  • (Q2) How can technologies and methods (those relevant according to Q1), be adapted and specialized to fit DSS, and in particular industrial DSS development?
  • (Q3) In what way do ODPs facilitate the practical creation, maintenance and usage of the formal models (ontologies) required by the semantic technologies (resulting from Q1 and Q2), and what ODPs are needed?

Vision and Goals in Short

The vision of the project is to make semantic technologies available to, and widespread in, industrial DSS. Short term goals include the identification of tasks within DSS that can be supported by Semantic Web standards, technologies, and methods, as well as adapting the technologies and methods for the needs of DSS, i.e. supporting DSS developers and users. The use of ODPs will ease the uptake of the technologies, as well as provide ready-made configurable ontological components.

Project Plan Summary

The project will explore the three research questions in parallel, starting from a list of focus areas that was developed in year 1. The focus areas are:

  1. Information integration - in particular the integration between information provided by human actors, e.g. messages and other textual information, and existing structured data.
  2. Flexible information filtering - in particular the opportunity to have context- and user-specific filtering that evolves along with user needs.
  3. Information aggregation, event and situation detection - beyond the plotting of raw information on maps or in graphs, i.e., truly "intelligent" data analysis is needed, what we call semantic Complex Event Processing (semantic CEP). Timeliness and flagging of potentially important situations, or events is essential, as well as information summarization together with the possibility of drill-down into more detailed information.
  4. Model evolution - to be able to handle changes and concept evolution in the real world, without extensive human intervention.
  5. Decision sharing - conveying the meaning of decisions to external parties.

The project will produce, apart from new knowledge in the field, the following tangible results:

  1. A set of methods and corresponding software for semantically enhancing DSS, consisting of empirically well founded solutions based on semantic technologies.
  2. ODPs to facilitate easier building of the formal models that the semantic technologies rely on.
  3. Specialized methodologies for development of the semantic parts of a DSS, including evaluation and validation of the semantic components and their contribution to the system.

Industry Involvement and Collaborations

The initial industry partners consisted of the Space and Naval Warfare Centre Pacific, FOI, VSL Systems AB, and Saab AB, which are all involved in research and development of different aspects of DSS. So far the collaboration consisted in participation in the interview study leading up to the prioritized list of focus areas for the project, and the representatives attending the seminars and some workshops arranged. In the case of Saab AB, a deeper collaboration has been initiated, and both master theses proposals at Saab AB are being planned, as well as a potential joint demonstrator project in collaboration with Combient AB. In addition, from 2014 we benefit from a new set of industry partners originating in the new EU FP7-Sec project, VALCRI, focusing on DSS for criminal intelligence analysis. In that project we are closely collaborating with two software development companies, Space Application Services NV (Belgium) and Object Security Ltd. (UK), for integration of Semantic Web technologies in DSS for intelligence analysis, and an end-user consultancy firm, AE Solutions Ltd. (UK), for technology evaluation and ontology and pattern development, as well as directly with end-users, such as the West Midlands Police Authority (UK).

The project is being conducted at the MDA group (IDA), but also helps to connect other researchers at IDA who are already working on Semantic Web-related topics, e.g., ontology matching and debugging (Database and Web Information Systems Group), logical formalisms and rules (TCSLAB), language processing (CILTLAB), and stream reasoning (KPLAB). In the coming year, the project will particularly benefit from a closer collaboration with Marco Kuhlmann (CILTLAB) for language processing of information entered into the system by humans. Marco has recently be engaged as a collaborator on the VALCRI project and concrete collaboration will start during autumn 2015. In addition researchers from the IDA "Database and Web Information Systems Group", Patrick Lambrix and his PhD student Zlatan Dragisic, are now also engaged in the VALCRI project, where Zlatan is focusing on the ontology evolution task. The (long-term) goal is to create a research group for semantic DSS, acting as a focal point for semantic technology research at LiU. As a first step towards this goal, a first PhD student (Robin) was employed in April 2013, and we have now, by using the VALCRI project as a joint interest, also involved NLP expertise (Marco), and Semantic Web researchers from other groups (Patrick and Zlatan). In addition, a PhD student from Jönköping University focusing on ODPs and ontology engineering methodologies, co-supervised by the applicant, has been engaged both in the VALCRI project and in general for this project.

From an international perspective, the research complements, and benefits from, several current research projects (mentioned further below). The research is also conducted in close collaboration with international research groups, e.g. STLab at ISTC-CNR (Italy), where the applicant did a postdoc, the OAK group at the University of Sheffield, working on collective intelligence and situation awareness, as well as large scale information integration, and Aalto University in Finland, working both on semantic CEP methods, as well as information integration, and the Middlesex Univeristy (UK), who is coordinating the VALCRI project. Additionally, the VALCRI project includes a collaboration with the Pacific North west National Laboratory (US), for exchanging experiences and methods for discovery of new event patterns, taking advantage of their previous work on signature discovery for various DSS tasks.

The project is also related to other CENIIT projects, e.g. "Stream-Based Reasoning Grounded Through Sensing" and "Semantic Parsing for Text Analytics". While the former project studies the basic mechanisms of stream reasoning, without restricting the underlying logical formalism, our project aims to focus on data represented through Semantic Web standards, e.g. RDF/OWL. The latter project is directly related to our information integration focus area, since it deals with information extraction from natural language text. This has led to the inclusion of the project leader of the project, Marco Kuhlman, in our EU-funded VALCRI project. Additional collaboration within the LiU environment is facilitated mainly through the seminar series and workshops, where results are shared between different research groups applying semantic technologies, as well as between related CENIIT projects.

2. Project results (until August 2015)

The first three years focused mainly on two aspects; information aggregation, event and situation detection, sometimes called semantic Complex Event Processing (semantic CEP) (see overview in project publication 9), i.e., focus area 2 and 3, and information integration, i.e., focus area 1. The choice, apart from these having the highest priority according to industry, is due to that we have found several dependencies between the focus areas, based on which we decided to start with a set of areas that early on will render usable and testable results, e.g., in terms of software components. In Figure 1, the relations between the focus areas are illustrated as a conceptual architecture of the overall DSS framework that the project is targeting. Information integration (focus area 1) constitutes the foundation for most of the other focus areas, when working with highly diverse data sources. Similarly, another basic task is the one of filtering out the interesting (or contextually relevant) data (focus area 2). Once data is available in appropriate formats, filtered according to our needs, and linked to appropriate data models, one can effectively perform information aggregation and event detection on such data (focus area 3). Focus areas 4 and 5 represent "support tasks", which allows us to maintain and evolve the semantic models used in all the other tasks (focus area 4 - model evolution) and to share the produced decision information (focus area 5 - decision sharing), with all its attached provenance information. Both the latter focus areas are now (in 2015) starting to be addresses, mainly in the context of the VALCRI project, where the PhD student Zlatan Dragisic is currently focusing on the ontology evolution task.

Conceptual architecture image - future.

Figure 1: An illustration of the conceptual architecture of the project focus areas, with the focus from year 4 and onwards being the complete picture.

Overview of project results during 2012-2015

In April 2013 a PhD student, Robin Keskisärkkä, was officially admitted and employed as a PhD student in this project. Before being admitted as a PhD student, as a master student, he explored and evaluated the state of the art in terms of existing stream processing and semantic CEP solutions (focus areas 2-3), in order to prepare for building our own software prototype realizing semantic CEP for DSS. This initial work was reported in a survey paper at a workshop in June 2013 (see project publication 17), also pointing out current challenges and "white spots" in current solutions.

The current focus of the PhD student, Robin, relies mainly on existing technologies for the core event processing engine, but instead explores novel ways to (i) handle RDF data as input to such an engine, and in particular more complex data than individual triples (position paper presented at the OrdRing'13 workshop - see project publication 15), (ii) declaratively express event patterns (as ODPs) or query templates, rather than only as hand-crafted rules or queries in the event processing system, and (iii) represent the resulting data in a semantic format, i.e., data about complex events, their structure and composition (research paper and pattern presented at the WOP'13 workshop - see project publications 13-14). The work on developing ODPs for semantic CEP has been conducted in close collaboration with a research group at Aalto University, in Helsinki, Finland, and we additionally hope that this effort will contribute to potential W3C recommendations, i.e., industry standards proposals, for representing RDF streams that are being discussed in the W3C RDF Stream Processing Community Group (where the applicant as well as the PhD student is actively participating).

Until the end of 2013 the implementation of a demonstrator application was finalized, for studying the three issues listed above (i-iii) empirically through experiments. An illustration of the architecture of this demo application can be seen in Figure 2. The demonstration application integrated several existing RDF stream processing engines, and adds an adaptor for non-RDF streams, reason over them using complex queries, and present the results in a Web interface. The contextual setting was reasoning over potential parking space availability, based on static knowledge of parking habits, combined with streams of weather data from SMHI.

Conceptual architecture image.

Figure 2: An illustration of the conceptual architecture of the demo application. Weather streams in different formats enter the system through a stream adaptor. By passing through several steps of abstractions and queries (using existing stream processing engines), in the end combined with static information about parking behaviour, the resulting prediction of parking availability is submitted to a Web interface.

Since the beginning of the VALCRI project, in 2014, another demo application has been developed. It relies on a similar architecture as the initial application above, with exchangeable RSP engines, but is integrated into the overall architecture framework of the VALCRI system. This new demo operates on data from automatic number plate recognition (ANPR), coming from video streams, and attempts to detect various situations among suspects' vehicles, such as if two suspects are meeting up. The demo is described more closely in two recent papers see project publication 5 and 6. the demo data and a video describing the demo is also available here.

For supporting pattern-based modelling methods, another software tool is being built by a PhD student (Karl Hammar) at Jönköping University supervised by the applicant. This tool is based on an extension of an earlier tool produced at STLab, CNR, during the postdoc stay of the applicant. The tool will be released as a WebProtégé modelling plugin, and is already available as open source code. Karl has also published a demo paper about this tool, to appear at ISWC 2015 in October (see online demo, and short video, as well as project publications 2 and 9).

Software framework

In the course of 2013-2015 we have also started to set up the software component library for semantically enhanced DSS, which mainly includes components for information filtering and semantic CEP at the moment (and is used for the demos described previously). The overall architecture and workflow currently supported is illustrated in Figure 3. The current implementation of the architecture is focused on RDF Stream Processing (RSP), and was specifically designed to support multiple RSP engines (i.e., to be able to reuse and evaluate existing engines). For each streaming API of interest, if the streaming API, e.g., an online data stream on the Web, follows an RSP standard the default stream adapter can be used (such standards are currently being developed by the W3C RDF Stream Processing Community Group), otherwise a tailored adapter needs to be created, e.g. for transforming data into RDF. The adapters can use external tools to enrich or "decorate" a stream, e.g., through natural language processing if the stream consists of textual data. Stream adapters have currently been developed for the Twitter Streaming API, the SMHI API, and particular datasets from the VALCRI project, such as ANPR (Automatic Number Plate Recognition) data, in addition to a standard adapter.

The dotted box in Figure 3 represents the classes that have to be created to add support for a new RSP engine. Since the input and output of the dotted box is a common RDF format, these classes are the only additions that have to be made to include a new RSP engine into the system. The internal RSP streams register themselves as listeners to stream adapters and consume the streamed data in the way required by the engine, e.g., one triple at a time. Anything that is output by a stream adapter can be consumed by an arbitrary number of internal streams. This makes it easy to duplicate a stream or benchmark two RSP engines using a single setup. Optionally, internal RSP streams can communicate with the stream adapters to throttle the rate with which data is being pushed to them.

In the current state of the art RSP engines, event patterns are represented as queries, which are registered in the individual engines, and each query is assigned a query observer. The query observer translates the output into the common RDF format (if necessary) and pushes the data to all current listeners (RDF Output Streams). The RDF Output Streams can push data back to stream adapters, thus enabling RDF queries to generate streams. Since this creates a cycle it is possible for queries to operate on data generated by themselves, allowing for any number of abstraction steps in the semantic CEP process. Various implementations of the RDF Output Stream can be created, e.g., to store the data in a database or a triplestore, to be used by an application, or to be published on the Web.

It should be noted that our framework does not implement any stream processing engine or CEP engine, since such engines already exist, however, it builds a framework for using such engines in a more advanced way, truly making use of the specific capabilities that Semantic Web technologies provide. What makes this framework novel, and qualifies it for being denoted a semantic CEP system rather than merely another RSP engine, are currently the following three things:

  • The framework is modular and allows for plugging in components for integration of both non-RDF and RDF streams in the same system, and allowing for pre-processing of streams before their consumption, making it possible to, for instance, use textual streams together with RDF and other data streams, neither of which is possible in any of the current RSP engines for the Semantic Web.
  • The framework allows for any number of levels of abstraction of streamed events, using the same or different event patterns over the streams created in previous levels. Although several existing system support multiple streams, there are rarely any notion of abstraction present, hence, even if such a multi-level framework could be realized it is not explicitly supported in other systems.
  • The framework represents events in the streams according to the Event Processing ODP, which allows for retaining information about how a high-level event was derived or abstracted from low-level events, which allows for reasoning on this information. The Event Processing ODP was created by us in collaboration with Aalto university, no other framework currently uses such a rich representation of streamed events.
In future extensions to the framework additional novelties for semantic CEP that will be added include:
  • Declarative representation of event patterns, rather than directly as hand-crafted queries in the dedicated query languages of the respective RSP engine used.
  • OWL reasoning capabilities over the streamed data represented using the Event Processing ODP.

Conceptual architecture image.

Figure 3: An illustration of the conceptual architecture of the current version of the overall software framework being developed.

ODP development

As mentioned previously, ODPs specifically targeted for semantic CEP are being developed (including the already published Event Processing ODP) in line with the semantic CEP method development within this project. However, for targeting information integration and filtering, other types of ODPs are necessary. In this area we have concluded that hand-crafted ODPs, representing modelling best practices, are not always sufficient. For large-scale information integration on the Web, ODPs need to reflect the actual common practice rather than ideal best practices. Based on this observation, we have initiated collaboration with the OAK research group at the University of Sheffield. By combining their experience on large-scale information extraction with our experience on ODPs, we have developed a method for bottom-up extraction of a specific kind of ODPs, what we now call Statistical Knowledge Patterns (SKPs), which characterize and provide access to online linked data in a novel fashion (see project publications 1, 11-12, and 16). Such SKPs will in the course of this project be used mainly to address the problem of providing appropriate ODPs for tasks in information integration, and filtering (focus areas 1 and 2).

Related projects and project acquisition

During autumn 2014, we became partners in an EU project, financed in the last FP7 security call (IP with 17 partners, coordinated by Middlesex University, UK), where our role in the project would be targeted at exactly the focus areas of this project. The funds from this project finance the rest of the PhD student employed for this project, from 2014 and onwards, and in addition partly funds two other PhD students and several senior researchers, including the project leader of this project. LIU leads two WPs in the project and our efforts are spent on one hand on semantic CEP (focus areas 2-3 of this project) in WP9, and on the other hand on ODP-based ontology engineering and model evolution (focus area 4) in WP10, for supporting information integration and filtering (focus areas 1 and 2) as well as semantic CEP. WP9 involves a close collaboration with the Space Applications Services NV (Belgium) and Pacific North west National Laboratory (US), for developing, implementing and testing methods and software for semantic CEP and declarative representations of event patterns through RDF/OWL, and will mainly involve the PhD student Robin Keskisärkkä. WP10 involves a close collaboration with several other academic partners as well as the software development company Object Security Ltd. (UK), Space Applications Services NV (Belgium), and representatives of end users, through consultants such as AE Solutions Ltd. (UK) and actual end-users such as the West Midlands Police Authority (UK), for creating ODPs and models for information integration and filtering, as well as methods for ontology and ODP evolution, which will be a part of the overall VALCRI software framework. This WP mainly involves the PhD student from Jönköping, Karl Hammar, another PhD student from LiU, Zlatan Dragisic, and the project leader, Eva Blomqvist.

Concerning focus area 1, information integration, we have during 2012-2015 also had four smaller externally funded projects targeting this focus area. The main focus of all three projects was to utilize the concept of linked data (based on semantic technologies and standards, such as RDF) in order to perform practical information integration. The first project, "Linked open data in Sweden - Portal and national statistics" , was a collaboration between LIU, Malmö högskola, SCB, and a small start-up company called Metasolutions, financed by Vinnova, aiming to on one hand support SCB in finding novel ways for information publishing and integration on the Web, i.e., through using linked data, and on the other hand promote the use of linked data in Sweden by producing a national information website with easy-to-access information about RDF and linked data in Swedish.

The second and third projects, "DEFRAM - Databas för Effektivare FRAMtagning av energikartläggningar" and the subsequent DEFRAM-2, are a LIU-internal collaboration between IDA and the Energy Systems department at IEI, financed by Energimyndigheten, aiming to showcase the use of, and usefulness of, linked data as a means for publishing and integrating information about energy assessments and reductions on the Web. The project has published data from two separate projects at Energimyndigheten, and linked these data to the much larger database on energy efficiency from the US organization IAC. Project results include a demo Web interface for accessing the integrated data, acting as a demonstrator software prototype for the information integration task of focus area 1.

The fourth project, Linked Geodata, funded by Vinnova and led by Lantmäteriet (addtional partners: MSB, SGU, Naturvårdsverket and the small company Novogit AB), was a prestudy for introducing linked data as a technology for publishing and using geodata at several Swedish authorities. The prestudy is now (in autumn of 2015) being followed-up by a new application for a development project, to be led by SGU, where the ideas from the prestudy are actually implemented and evaluated. In particular, the new application includes the scientific study and quantification of linked data benefits in a comparative setting, which would be a very interesting result for the field.

Although disparate in their domains, these linked data-focused projects apply the same novel technologies for information integration and have several functions within this project; e.g., to (i) showcase semantic technologies and their benefits in real-world use cases in Sweden, to act as "good examples" for other industry actors to attempt similar efforts, and to (ii) collect industry-targeted information, tools, and ODPs, for allowing organizations to more easily understand and practically perform such information integration on their own in the future. If funded, the follow-up project to Linked Geodata will also provide quantitative evidence of the benefits of this technology.

A recent grant from the KK-foundation was awarded SICS East, for a project called ECare@home, where the applicant is now working 50% of her time for SICS East (50% leave from LiU) to participate in this project. The topic is on supporting e-care in a homecare setting, through integrating and semantically interpreting data from various sources (including sensors, health records, and personal notes and diaries) in order to analyse and monitor the situation of a patient and provide improved decision support for caregivers. In terms of our focus areas, the ECare@home project touches upon all of them although main focus will be on 1, 3 and 5. The project is a collaboration between SICS and two Swedish universities, MDH and ÖRU. One additional project application, concerning semantic CEP, is under review by Vetenskapsrådet, decision expected in Nov. 2015.

Collaboration and networking

Collaboration within the LiU environment is facilitated partly through a seminar series and technologies, as well as between related CENIIT projects (in our case mainly the projects "Stream-Based Reasoning Grounded Through Sensing" and "Semantic Parsing for Text Analytics"). However, in 2015 a more concrete collaboration has been initiated in the context of the VALCRI project, by including both Patrick Lambrix (ADIT) and his two PhD students (including Zlatan Dragisic, working on the ontology evolution focus area), as well as Marco Kuhlmann (leader of the CENIIT project "Semantic Parsing for Text Analytics") as active participants in VALCRI. In terms of research environment at LIU, the project is thereby near to reaching its organisational goal of having a research group focusing on Semantic Web technologies for DSS at IDA. Although this group is so far of a "virtual" nature.

In November 2014 LiU arranged the EKAW conference, which acted as a meeting point both for internal collaborators (i.e. it was arranged jointly by researchers from several divisions of IDA) and external collaborators. A workshop at EKAW, WaSABi13 (co-chaired by Karl Hammar), also targeted industrial applications of Semantic Web technologies. The LiU seminar series on semantic technologies has been slightly slowed down, due to the applicant's parental leave last year, however this year it has been resumed and has consisted of 2 seminars so far in 2015, one with and international invited speaker. In addition, another public research-focused workshop on social media and linked data for emergency response was arranged at ISCRAM'15, and the applicant is again co-organiser of the yearlyWorkshop on Semantic Web and Ontology Patterns, at ISWC'15.

3. Project Publications

The project has during its first three years produced the following publications:

  1. Zhang Z., Gentile A. L., Augenstein I., Blomqvist E., Ciravegna F.: An Unsupervised Data-driven Method to Discover Equivalent Relations in Large Linked Datasets. In Semantic Web Journal, (to appear), IOS press, 2015.
  2. Hammar, K.: Ontology Design Patterns in WebProtégé. In Poster and demo proceedings of ISWC2015 (to appear), 2015.
  3. Dragisic Z., Lambrix P., and Blomqvist E.: Integrating Ontology Debugging into the eXtreme Design Methodology. In Proceedings of Workshop on Ontology and Semantic Web Patterns (6th edition) - WOP2015, (to appear), 2015.
  4. Keskisärkkä R. and Blomqvist E.: Sharing and Reusing Continuous Queries - Expression of Interest. In online Proceedings of the RDF Stream Processing Workshop at ESWC2015, 2015.
  5. Keskisärkkä R. and Blomqvist E.: Supporting Real-Time Monitoring in Criminal Investigations. In Post-conference poster and demo proceedings of ESWC2015 (to appear), Springer, 2015.
  6. Keskisärkkä R. and Blomqvist E.: Towards the Use of RDF Stream Processing Engines for Event Enrichment from Social Media Streams. In online proceedings of the Workshop on Semantics and Analytics for Emergency Response (SAFE2015) collocated with the The 12th International Conference on Information Systems for Crisis Response and Management (ISCRAM2015), 2015.
  7. Blomqvist E. and Thollander P.: An Integrated Dataset of Energy Efficiency Measures Published as Linked Open Data. Energy Efficiency, online preprint available 20 March 2015.
  8. Timpka, T., Spreco, A., Dahlström, Ö., Eriksson, O., Gursky, E., Ekberg, J., Blomqvist, E., Strömgren, M., Karlsson, D., Eriksson, H., Nyce, J., Hinkula, J., and Holm, E.: Performance of eHealth Data Sources in Local Influenza Surveillance: A 5-Year Open Cohort Study. In: Journal of Medical Internet Research, 16(4):e116, 2014.
  9. Hammar, K.: Ontology Design Pattern Property Specialisation Strategies. In Knowledge Engineering and Knowledge Management - 19th International Conference, EKAW 2014, Linköping, Sweden, November 24-28, 2014. Proceedings, Springer LNCS, 2014.
  10. Blomqvist, E.: The Use of Semantic Web Technologies for Decision Support - A Survey. In: Semantic Web Journal (online preprint available already 2012), 5(3): 177-201, IOS Press, 2014.
  11. Zhang, Z., Gentile, A. L., Blomqvist, E., Augenstein, I., and Ciravegna, F.: Statistical Knowledge Patterns: Identifying Synonymous Relations in Large Linked Datasets. In: The Semantic Web - ISWC 2013 - Proceedings of the 12th International Semantic Web Conference, 21-25 October 2013, Sydney, Australia. LNCS Vol. 8218, Springer, 2013.
  12. Blomqvist, E., Zhang, Z., Gentile, A. L., Augenstein, I., and Ciravegna, F.: Statistical Knowledge Patterns for Characterizing Linked Data. In: Proceedings of the Workshop on Ontology and Semantic Web Patterns (4th edition) - WOP2013, CEUR workshop proceedings, 2013.
  13. Rinne, M., Blomqvist, E., Keskisärkkä, R., and Nuutila, E.: Event Processing in RDF. In: Proceedings of the Workshop on Ontology and Semantic Web Patterns (4th edition) - WOP2013, CEUR workshop proceedings, 2013.
  14. Rinne, M., Blomqvist, E.: The Event Processing ODP. In: Proceedings of the Workshop on Ontology and Semantic Web Patterns (4th edition) - Pattern track - WOP2013, CEUR workshop proceedings, 2013.
  15. Keskisärkkä, R. and Blomqvist E.: Event Object Boundaries in RDF Streams: A Position Paper. In: Proceedings of the 2nd International Workshop on Ordering and Reasoning - Co-located with the 12th International Semantic Web Conference (ISWC 2013) - Sydney, Australia, October 22nd, 2013. CEUR workshop proceedings, Vol. 1059, 2013.
  16. Zhang, Z., Gentile, A. L., Augenstein, I., Blomqvist, E., and Ciravegna, F.: Mining Equivalent Relations from Linked Data. In: Proceedings of the annual meeting of the Association for Computational Linguistics (ACL) 2013 (short papers).
  17. Keskisärkkä, R. and Blomqvist E.: Semantic Complex Event Processing for Social Media Monitoring - A Survey. To appear in: Proceedings of Social Media and Linked Data for Emergency Response (SMILE) Co-located with the 10th Extended Semantic Web Conference - May 26-30, 2013 at Montpellier, France, CEUR workshop proceedings, 2013.
  18. Blomqvist E., Seil Sepour A., and Presutti V.: Ontology Testing - Methodology and Tool. In: Knowledge Engineering and Knowledge Management - 18th International Conference, EKAW 2012, Galway City, Ireland, October 8-12, 2012. Proceedings. LNCS, Vol. 7603, pp. 216-226, Springer.

4. Project plan (with focus on year 5 - 2016)

The end of 2015 and year 5 of the project will focus on: (i) performing more in-depth empirical evaluations of the methods, components and ODPs already developed, using the demonstrator application in the context of the VALCRI project, and potentially additional demonstrators built in the context of our Saab collaboration, the ECare@home project, and a potential new Vinnova funded project (if accepted), (ii) further extending the methods, components and ODPs, e.g. in particular for new domain contexts, such as within the ECare@home project, and (iii) providing novel approaches and software components for performing ontology evolution. The funded PhD student, Robin, will continue to focus on information filtering and semantic CEP (focus areas 2-3), and ODPs for that task. The applicant will continue to focus on ODPs and methodology development together with the PhD student Karl (funded by JU), as well as developing solutions for information integration (focus area 1) and decision sharing (focus area 5), the latter together with researchers from Middlesex University in the context of the VALCRI project. While the PhD student Zlatan (funded by CUGS and VALCRI) will continue to focus on the model evolution task (focus area 4) in the context of the VALCRI project.

During the coming year we envision to acquire at least two new grants. One from Vinnova for a continued cooperation with Novogit AB and several Swedish authorities, such as SGU. The other consisting of a demonstrator project, for showcasing the industry potential of our technologies, together with Saab and other industry actors, in the context of mission planning support. An initial project description has been drafted and is now being negotiated with the potential funding body and participating industry actors.

5. Detailed background, motivation and project plan (from initial application)


The Semantic Web [1] has been researched for more than ten years; nevertheless, the techniques have only to a certain extent been applied to Decision Support Systems (DSS). At the same time organizations have to cope with an increasing information overload [2-3], thus increasing the need for DSS. Relying on experiences from Content Management Systems (CMS), we note that until recently few CMS used semantic technologies. However, in recent years this has changed, since: (i) Semantic Web standards have emerged, leading to the availability of stable and scalable software frameworks, and (ii) projects such as IKS have developed specialized methods and tools applying specifically to CMS. To some extent, similar methods are relevant for DSS and other frameworks are under development, e.g. distributed Web-scale reasoning [4], and stream reasoning and Complex Event Processing (CEP) [5] in OWL, c.f. the LarKC project. Still, there is a need for methods allowing industry actors to more easily adopt and adapt current semantic technologies to DSS.

We focus on DSS specifically for delivering the right information to the right person, at the right time and location, with an appropriate quality, to meet the information demand of a decision task. This may imply information filtering, enrichment, and context awareness. Information quality can include time aspects, or tracking provenance and assessing reliability. Such a DSS should exploit the recent success of Semantic Web technologies, but doing that it needs to address the following challenges:

  1. There is a reluctance to adopt semantic technologies in industry, since they are (i) perceived as "difficult" and complex, e.g. requiring knowledge of logical formalisms, and (ii) because there are in many cases a lack of empirical evidence showing the benefits of such solutions [6-8].
  2. Few research projects have studied (i) what semantic technologies are suitable for a DSS setting, (ii) what tasks within a DSS they can solve, and how they need to be tailored to DSS, and (iii) in what way those technologies increase the quality and/or performance of existing solutions.

These challenges are confirmed by industry interested in applying semantic technologies. The project will thus be conducted in collaboration with several industrial actors, e.g. Saab and VSL Systems, as well as research organizations, e.g. FOI and the Space and Naval Warfare Centre Pacific of the US Navy. These organizations will, through their long experience of DSS, provide use cases and requirements of semantic DSS, and act as validators and potential adopters of project results.

Design Patterns (DPs) have proven effective in other fields, e.g. software engineering. DPs are well-tested and consensually agreed solutions to recurrent problems. DPs for semantic technologies [9-11] are still in their infancy, but play an important role in the adoption of semantic technologies, and are at the very forefront of semantic technology research. The term Ontology Design Pattern (ODP) was coined simultaneously by the project leader [12], and Aldo Gangemi [13], in 2005. Since then, we have also proposed a pattern-based ontology design methodology [14] and empirically evaluated it [15].

Applying ODPs in DSS is a completely novel approach, which facilitates the adoption of Semantic Web technologies (c.f. 1(i)), and is a means to tailor technologies for use within DSS (c.f. 2(ii)). The type of ODPs to be used are Content ODPs (CPs), which can be manifested as small models, and described in a simplified manner as a tuple CP=<R,V,O>. R is a set of ontological requirements expressing the tasks the CP solves, e.g. inferences or queries, V a set of terms expressing its vocabulary, and O a set of logical axioms using V as lexical grounding. In the rest of this text, we let ODP refer to CPs as described here, more specifically we focus on CPs where O is expressed using OWL (a W3C recommendation); additionally we will use the term ODP model to refer to O.

Project Plan

The project will last for 6 years (2012-01-01 - 2017-12-31), and the research will be conducted based on the following questions:

  • (Q1) What tasks (functionalities) in a DSS can be improved through semantic technologies, and what is the nature of the improvement?
  • (Q2) How can technologies and methods (those relevant according to Q1), be adapted and specialized to fit DSS, and in particular industrial DSS development?
  • (Q3) In what way do ODPs facilitate the practical creation, maintenance and usage of the formal models (ontologies) required by the semantic technologies (resulting from Q1 and Q2), and what ODPs are needed?

Research is conducted so that these questions are addressed partly in parallel, i.e. Q2-3 are studied based on a hypothesis of Q1, then verifying it through empirical studies (returning to Q1). Q1 implies studying current literature, existing (and future) DSS, as well as exploiting existing methods for evaluating semantic technologies. Q2 will use experiences and software from IKS (the project leader was actively involved in IKS and software is readily available), together with existing results from other research projects, e.g. LarKC. The previous research of the project leader, concerning ODPs and related methodologies will be the starting-point for answering Q3. Catalogues of ODPs are also available, e.g. such as the ones collected in a bottom-up fashion by online community portals.

Five hypotheses, based on our experience of semantic technologies and DSS, respectively, were initially given (for the revised list, see top of this page). The list will be subject to change based on literature, industry requirements, and empirical evidence. Each hypothesis includes the general task, and initial ideas for semantic-based solutions:

  1. Situation detection - Given a stream of data, e.g. sensor data, semantically representing it utilizing ODPs, and detecting complex events (c.f. CEP) by performing reasoning on the stream.
  2. Information filtering and integration - Given a large set of data from heterogeneous sources, filtering the data based on ODPs able to express a unified view of the relevant parts, i.e. matching the original models to the ODP model, and using it to query the data (c.f. [16] sect. 6).
  3. Information enrichment - Given an ODP as a view on data, extracting additional data for that ODP from text or structured sources, i.e. using the ODP model for Information Extraction.
  4. Model extension - Given a set of input data, semi-automatically develop or extend a formal model of that data, tailoring it to a specific DSS task by applying ODPs (c.f. method in [19]).
  5. Tracking and sharing decisions - Given a decision-making process, tracking, sharing and comparing decisions, and applying metrics on the process, e.g. efficiency of information flow, c.f. the preliminary work of the Decision and Decision-making W3C incubator.

For assessing the nature of the improvement (c.f. Q1), both quantitative and qualitative evaluations will be used. Quantitative methods include evaluating effectiveness of information delivery, e.g. through precision, recall, and quantifying usability, e.g. through SUS [17], as well as efficiency, e.g. performance and scalability measures. The SEALS platform provides unbiased evaluation datasets, measures, benchmarks and an evaluation software framework. Qualitative methods relate to user satisfaction, perceived usefulness, and ease of use of the semantic technologies for certain tasks.

The first two years have focused on the first hypothesis, i.e. situation detection, sometimes called Complex Event Processing (CEP) [5,18]. The hypothesis is based on observations from several organizations. For instance, in situation awareness and monitoring systems for municipalities (as developed by Saab), low-level sensor data needs to be aggregated and transformed into "situations", which make sense to a user. In a combat management system (as observed at Försvarsmakten), the situation is continually monitored by officers leading the combat, to plan their next move and predict future developments. Finally, during training of emergency managers (as done by VSL Systems) there is a need for monitoring the development of an exercise, e.g. to assess if a training session is proceeding according to plan and to detect deviations. Our hypothesis is that these problems could be supported by semantic CEP and stream reasoning. Concretely, the problem can be described as a process characterized by the following input and output requirements:

(1) A stream of data representing the state of the environment, as well as (2) background knowledge of the domain, which in turn includes a characterization of a set of situations (expressed by means of ODP models) that are relevant from a user perspective.
(1) A stream of complex events representing the current situation in a way that is useful and makes sense to the end user, and where (2) any user-relevant "situations" are marked.

After the first year, an initial prototype based on Semantic Web technologies, e.g. ODPs, was presented, and a second demonstration application was build by the end of the second year. During the third year, these prototypes will be evaluated (according to Q1), e.g. within the new VALCRI project. Parts of the prototypes are also being generalized, to constitute a first component of the general software framework. In parallel, the other four hypotheses (tasks) were analysed in detail, to determine (i) their relevance for DSS, as well as (ii) detailed plans for including such functionalities in the project results.


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Last updated: 2015-10-08