In this paper we address the problem of knowledge management and interoperability in virtual enterprise environments where knowledge is often fragmented and heterogeneous. We propose a knowledge repository and management infrastructure, called Production and Innovation Knowledge Repository (PIKR), to support open innovation in virtual enterprises. The PIKR provides a set of reference ontologies to semantically describe enterprise knowledge resources, and semantics-based services for accessing and reasoning over such descriptions. We also give an overview of the implementation of the PIKR that is being carried on in the BIVEE European project.
In the era of the globalised market which has deeply transformed the world economy, and increased the international competition, SMEs are more and more pushed to form business alliances and work in virtual enterprises (VEs). Enterprise networks can be a means to reach the critical mass required by the expanding markets. However, heterogeneities of the network’s members can generate interoperability problems, with the consequence of reducing the expected benefits. Here we focus on interoperability at knowledge and information level that impacts on the possibility of exchanging and accessing common knowledge resources within a VE, and in particular within and across the production space (where all the activities related to the core business take place), and the innovation space (mainly characterised by creative units and cooperative interactions) of the VE itself.
In a context of networked enterprises, this aspect is very crucial because it allows relevant resources to be shared, in order to assess how production activities are actually performed, and how performing they are, what kinds of resources (in terms of skills and expertise) the virtual enterprise can count on, what documental resources (e.g., market analyses, technical reports) have been produced or acquired by the VE. It is also very important to know how innovation-related initiatives are carried on, e.g., the degree of participation of people to brainstorming activities, the number of relevant ideas collected in certain periods of time, the number of proposed ideas which have been concretely exploited, and so on.
To address interoperability problems, a good solution is the development of a
semantics-driven common knowledge base, characterized by the adoption of shared
reference ontologies. The purpose of this paper is to present a proposal of a
knowledgebased infrastructure, named PIKR (Production and Innovation Knowledge
Repository), to support knowledge interoperability and management in VE environments.
This proposal is being conceived in the framework of the BIVEE European project1,
and adheres to the Linked Data approach [
The mission of the PIKR is to create a semantics-based unified view of the information and knowledge that flow within and across the Production Space and the Innova1 Business Innovation in Virtual Enterprise Environments. http://bivee.eu tion Space of VEs. In particular, these two spaces are seen through the following types of knowledge resources: Processes, which describe actual production activities; Documents, which are concrete footprints of all kinds of activities, both at production and innovation level; Actors and their competencies, which refer to the capabilities of the VE and its members; Key Performance Indicators (KPIs), for monitoring both the Production and the Innovation space.
Then, the PIKR is organized into two layers (Fig. 1): the Intensional PIKR (IPIKR), which contains a federation of ontologies to describe the enterprise resources, and the Factual PIKR (F-PIKR), which contains the semantic representation (Semantic Descriptors) of the actual enterprise resources in terms of the above ontologies.
The ontologies in the I-PIKR are partitioned into Knowledge Resource Ontologies (KROs), and Domain Specific Ontologies (DSOs). KROs are independent of any application domain and declare what kind of information, links, constraints and business rules, for each type of knowledge resource (i.e., Processes, Documents, Actors, and KPIs), we intend to semantically represent (Semantic Descriptors Skeleton, SDS), while DSOs allow Semantic Descriptors to be enriched with domain specific contents (e.g., furniture domain).
According to this view, the I-PIKR contains four main KROs: ProcOnto, DocOnto, ActorOnto, and KPIOnto, for describing processes, documents, actors, and key performance indicators, respectively, and inter-connections between them. Consequently, the Semantic Descriptors, which describe actual knowledge resources (e.g., the technical report realized in a specific project, or the process for producing a certain product) will be instances of the KROs. The Semantic Descriptor Skeletons will be characterized by a common structure organized into the following sections: Header: collects information represented by traditional metadata like the ones proposed by the Dublin Core Vocabulary2 (e.g., name, natural language description). This section also contains the link to the actual knowledge resources that is assumed to be stored in a proprietary system, e.g., a content management system. Domain Specific Content: collects information about what the content talks about in terms of the DSOs. For instance, in this section one can say that a given technical report is about the design of an innovative contour chair in carbon fibre. Related Knowledge Resources: collects links to related Semantic Descriptors, allowing the representation of semantic associations and dependencies among resources (e.g., the input document of an activity, an indicator occurring in a formula defining a KPI). External Links: links to resources external to the VE available on the internet (e.g., technical documentation, external policies or regulations, web-sites). Extended Representation: links to representations of the resource that will allow the enactment of specific reasoning facilities (e.g., a mathematical representation of a KPI, a machine processable representation of a business process). Domain Specific Content and Related Knowledge Resources items can be enriched with business rules, i.e, constraints that characterize the semantic descriptor skeletons 2 http://dublincore.org with respect to the particular reality of a virtual enterprise. These constraints can depend, for instance, on the specific application domain, on the dimensions of the VE, or on the VE internal policies. For example a feasibility study for justifying the prototyping of a product needs financial information, if the expected cost is higher than a certain amount (e.g., 300 Keuro). This means that for feasibility studies, financial information is not always mandatory, even in the same VE.
For the definition of the KROs we are following different approaches with respect
to the different kinds of resources. In the case of the DocOnto, we are considering
both production and innovation related documents. For the production documents
(e.g., invoices, bills of materials) there is plenty of literature and standards (e.g., UBL
[
In the case of the ProcOnto, we refer to a logic-based language for representing
and reasoning with process knowledge. We propose to adopt BPAL (Business Process
Abstract Language), which is a process ontology, strongly inspired to the BPMN [
In the case of the KPIOnto, we refer to existing classifications like the one in [
KROs, DSOs and Semantic Descriptors represent the Knowledge Repository of the PIKR, on top of which some semantics-enabled services are made available by the PIKR Reasoner. The services are here briefly described. 3 http://www.value-chain.org/en/cms/1960
Search. This module provides keyword-based search services, following an
interaction paradigm similar to traditional web information retrieval engines. The user
request is expressed as an ontology-based feature vector describing the criteria for the
selection of the resources of interest. The search engine returns a list of ranked results
by applying semantic similarity techniques (e.g., the SemSim metric [
Query. This module provides services to retrieve pieces of knowledge which
exhibit some given properties. Queries are posed in terms of the vocabulary and semantic
relations provided by the PIKR ontologies, and the underlying reasoning engine
returns a list of answers that satisfy all specified properties. These answers may consist
of factual knowledge (semantic descriptors), conceptual knowledge (ontological
terms), or references to resources. The most prominent standard for querying
OWL/RDFS resources is the SPARQL (SPARQL Protocol and RDF Query
Language) [
Consistency Checking. This module provides services for checking the compliance of the factual knowledge captured in the semantic descriptors with respect to business policies and internal regulations established for the whole VE or for individual enterprises. Such compliance requirements are represented in the I-PIKR in terms of business rules, i.e., statements that define or constrain some aspects of the business, specifying the structure of the domain entities (structural constraints) and influencing the way business operations are conducted (behavioral constraints). In our frame the compliance check takes place by verifying the consistency between the assertions contained in the F-PIKR and the axioms defined in the Knowledge Resource Ontologies formalizing the business rules. An example of structural constraint is “Each Innovation_Report needs to be composed by a Project_Proposal and a Market_Report”, while an example of behavioral constraint is “A Monitoring_Sheet cannot be produced unless a Gantt_Chart has been finalized before”.
KPI Reasoning. This module provides inference services for supporting KPI elicitation (i.e., the identification of the KPIs which are suitable for a given VE), by analyzing KPIs from different perspectives (e.g., organization and time dimensions). This module also supports the harmonization of the measures provided by VE members which are needed for the evaluation of KPIs. Indeed, since measures can be originated by different data sources (e.g., proprietary information systems) from different enterprises in the VE, they need to land on a reference representation compliant with the KPI formulas. Examples of heterogeneities between data definitions and required input for KPI evaluation could be in terms of terminology (e.g., Customer_Requested_Date vs. Expected_Delivery_Date), or granularity (e.g., aggregated vs. atomic data). 4
PIKR Reasoner
WS
WS e r a w e l d d i M n o i t a r g e It n e c i v r e S r e l d n a H a t a D w a R We are currently developing the PIKR on top of the Semantic Mediawiki plus4 (SMW+) suite which provides a solid infrastructure for building powerful and flexible “collaborative knowledge-bases” upon a wiki, and also encompasses user-friendly environments for presenting and collecting both human-readable and machineprocessable contents. SMW+ also allows the integration of different triple stores (we are currently using the Jena,5 toolkit) providing basic storage and retrieval facilities for RDF data.
The Inference Engine, which is the core of the PIKR Reasoner, is being implemented
as a Java application, interfaced with XSB Prolog [
In this paper we presented a semantics-based infrastructure, called PIKR, aiming at providing a unified view of different kinds of knowledge resources that are present in a virtual enterprise context, for supporting knowledge management and interoperability in production and innovation related activities. This infrastructure is designed, 4 http://www.smwplus. net 5 http://incubator.apache.org/jena/index.html according to the Linked Data approach, by describing knowledge resources and their semantic relations in terms of a federation of reference ontologies, which define production processes, documents, actors and key performance indicators. While the actual knowledge resources are stored at the premises of the respective owner companies in the virtual enterprise, the PIKR maintains resource images in the form of semantic descriptors that can be regarded as instances of the ontologies. On top of this descriptions, a set of semantic services is offered for easing the navigation and the retrieval of such resources, along with a set of facilities for reasoning over them.
While in this paper we give an overview of the PIKR infrastructure, in the next future we will address the following issues: the building of the specific reference ontologies, the full implementation of the PIKR.
Acknowledgments. This work has been partly funded by the European Commission through the ICT Project BIVEE: Business Innovation and Virtual Enterprise Environment (No. FoFICT-2011.7.3-285746). The authors wish to thank all BIVEE project partners for their contribution during the development of various ideas and concepts presented in this paper.