The United Nations’ Climate Technology Centre & Network (CTCN) provides technical assistance in response to requests submitted by developing countries via their Nationally-selected focal points, or National Designated Entities (NDEs). Upon receipt of such technical assistance requests, the CTC needs to quickly mobilize its global Network of climate technology experts to design and deliver a customized response tailored to local needs. The volume of requests as well as the amount of experts and technical assistance responses ('solutions') is growing considerably and therefore it was decided to explore if and how an automated process can support filtering technical assistance requests ('requirements') and the identification of experts and resources to fulfil these requests (a "Matchmaking Facility"). Ideally, such an automated process would offer NDEs with the opportunity to query the CTCN's knowledge management system using problem-oriented language to find expertise, case studies/good practice stories, relevant documents and signposts to relevant other knowledge sources. This latter scenario would allow the CTCN to significantly scale-up its technical assistance work to developing countries.
In a consortium of DNV GL, Semantic Web Company and REEEP, a demonstrator Matchmaking Assistant was developed in the first half of 2015 to explore how existing tools such as the REEEP Climate Tagger (which can identify most relevant concepts from unstructured text and is based on an expert-developed climate thesaurus) and the underlying Semantic Web Company’s PoolParty Semantic Suite Technology provide a solution for the above-mentioned challenge.
The focus of the Matchmaking Assistant demonstrator project was on a) providing evidence that the solution can deliver high quality results; b) identifying if and how CTCN would need to adapt its processes to support such a matchmaking solution; and c) exploring how such a solution can be integrated in the existing Knowledge Management System of CTCN.
The CTCN Matchmaking Assistant makes use of the REEEP Climate Tagger Software System (see: http://www.climatetagger.net/) and the respective Knowledge Model (the SKOS Climate Thesaurus http://www.climatetagger.net/climate-thesaurus/) including the underlying PoolParty Semantic Suite Technology (see: http://www.poolparty.biz). The Climate Tagger System is an expanded PoolParty Semantic Integrator Instance that includes software components for a) Thesaurus modelling; b) Text Extraction & Text Analysis; c) semi-automated Annotation / Classification Mechanisms by making use of concepts (from Thesaurus) and by suggesting free terms (from additional frequency analysis); and d) Storage as well as Similarity Mechanisms by the Climate Tagger Content Pool on top of an Apache Solr Index. Furthermore, additional API methods for similarity of knowledge objects are included in the Climate Tagger Software System.
The software enables matchmaking between knowledge objects - namely: Problem Statements (requests from developing countries) as input and Solution Documents describing expertise from the CTC Network as outputs. The CTCN Matchmaking Assistant provides a Recommender User Interface and a Search User Interface. The Recommender takes either a request pdf or word file as its input, analyses the text held within the request, creates a ‘fingerprint’ and provides a rank-ordered list of suggested organisations that hold expertise that is relevant for answering the request.
The overall positive user feedback suggests that the Matchmaking Assistant can not only support CTCN staff in identifying the right knowledgeable organisations from a worldwide pool of expertise, but also provide that support to the intended end user directly.
Further work needs undertaking to refine the knowledge model that maps problem terms to solution terms to further encompass the wide range of potential climate technology solutions for an array of climate problems, including translation of many terms to accommodate for preferred languages in developing countries. Also, the profiling of expertise held by solution providers can be enhanced by feeding the Matchmaking Assistant with richer capability statements and project descriptions of those solution providers. Concluding the demonstrator project, a high-level architecture is proposed for integrating the Matchmaking Assistant in the existing web platform of the CTCN.
Eelco Kruizinga graduated as a cognitive psychologist from the University of Amsterdam, focussing on artificial intelligence. Eelco, currently a senior principal consultant and deputy director with DNV GL, has been active in the knowledge management field since 1992, as a consultant, project manager, lecturer and reviewer of knowledge management plans. His focus is on the design of and implementation of large-scale knowledge management programmes for organisations and networks. He is involved in knowledge capture, knowledge sharing between projects, development of knowledge-based portals and lessons learned programmes. Examples of his work over the years include the leadership of large-scale knowledge management projects such as the European Carbon Capture and Storage Network, the K2 project, a knowledge and learning infrastructure for over 80 European Technology-Enhanced Learning projects and the KM programme for UK’s nuclear decommissioning mission, including tools for knowledge capture of ageing experts. He was the knowledge manager for the Dutch Olympic Committee, where he designed and implemented a knowledge management programme for elite sports coaches and the web-based expertise platform. Eelco is involved in technology roadmapping studies to create sustainable futures for industrial sectors. Eelco has led the design work of a large scale single point of knowledge in aviation safety, skybrary.aero. Eelco now is the KM lead for DNV GL’s strategic partnership with UN’s Climate Technology Centre and Network (CTCN).
Lead Knowledge Management