We address the problem of providing knowledge management support to business innovation. We consider the context of virtual enterprise environments, where knowledge is often fragmented and heterogeneous, and interoperability is a crucial requirement. 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. The proposal is being conceived in the framework of the BIVEE European project and adheres to the Linked Data approach.
In increasingly competitive environments, where new business practices and products
are regularly introduced, organizations need to solicit, support and ease innovation, as
it appears a critical factor for them to be successful. ICT solutions have facilitated the
creation of new kinds of business alliances, the so called Virtual Enterprises (VEs),
facilitating also the advent of the so called Open Innovation paradigm, which assumes
that “firms can and should use external ideas as well as internal ideas, and internal
and external paths to market, as the firms look to advance their technology” [
In any case, innovation generation and development need the observation and awareness of the reality around, both internally and externally to the enterprise. A very interesting sort of observation-oriented approach to innovation is biomimetism, which is defined as “the study of the structure and function of biological systems as models for the design and engineering of materials and machines”1. It starts from the assumption that there may be a good reason why nature has designed animal and plants as they are. An example of application of this pattern is the design of the Shinkansen, the innovative Japanese bullet train, which has been inspired by the study of the kingfisher’s beak, which turns out to be supremely efficient at crossing the airwater interface with the minimum amount of turbulence2.
But, beyond very challenging and fascinating approaches, it is also very crucial the observation and monitoring reality internal to the enterprise. This means to know how production activities are actually performed, and how performing they are, what kinds of resources (in terms of skills and expertise) the enterprise can count on, what documental resources (e.g., market analysis, technical reports) have been produced or acquired by the enterprise. But it is also very important to know how innovation-related initiatives are carried on, e.g., what is the degree of participation of people to brainstorming activities, how many relevant ideas have been collected in certain periods of time, how many proposed ideas have been concretely exploited, and so on.
According to the above considerations, innovation-related activities need a very effective knowledge management and interoperability support, especially in virtual enterprise environments and open innovation scenarios, where the landscape is more fragmented, and heterogeneous.
The purpose of this paper is to present a proposal of knowledge-based
infrastructure, named PIKR (Production and Innovation Knowledge Repository), to support
business innovation. This proposal is being conceived in the framework of the BIVEE
European project3, 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 (where all the activities related to the core business take place), and the Innovation Space (mainly characterised by creative units and cooperative interactions) 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 any kind of activity, 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 vision, 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.
Independently of their specificities, due to the different nature of the knowledge resource types, the KROs will aim to catch certain categories of information so that 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 Vocabulary4 (e.g., name, natural language descrip4 http://dublincore.org tion). This section also contains the link to the actual knowledge resources that is assumed to be stored in a proprietary system, e.g., a document management system or a business process (BP) management tool. Domain Specific Content: collects information about the content of the described knowledge resource 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 (the input document of an activity, an indicator occurring in a formula defining a KPI, the temporal dependency between a feasibility study and a project proposal). External links: links to resources external to the VE and possibly available on the internet (e.g., technical documentation, external policies or regulations, web-sites, bibliographic references). 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 which give the possibility to characterize the semantic descriptor skeletons 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 proposed 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 [
5 http://www.value-chain.org/en/cms/1960 6 http://www.smwplus.com 7 http://incubator.apache.org/jena/index.html
On top of the Knowledge Repository, the PIKR Reasoner makes available four main types of semantics-enabled services.
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 member 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
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 both the production and innovation development activities. This infrastructure is designed, 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 three main issues: the building of the specific reference ontologies as illustrated in Section 2, the definition of the semantic services, and 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 acknowledge the Commission for its support. We also wish to acknowledge our gratitude and appreciation to all BIVEE project partners for their contribution during the development of various ideas and concepts presented in this paper.