connotaNons on their own domain. â« The presented work uses KM, IR tools and domain ontologies as a. semanNc referenNal to represent, enrich and retrieve ...
A SEMANTIC ENRICHMENT APPROACH BASED ON THE VECTOR SPACE MODEL SUPPORTING COLLABORATION IN THE MANUFACTURING DOMAIN Paulo Figueiras, Raquel Melo, Ruben Costa, Carlos AgosDnho, Celson Lima, Ricardo Gonçalves
PresentaDon Outline • IntroducDon • Main Concepts • Knowledge Enrichment Process • C2NET E-‐Procurement Approach • PracDcal Use-‐Case • Conclusions & Future Work
IntroducDon The problem is …
§ European SMEs need to have access to advanced management systems and to collaboraDve tools due to their restricted resources.
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SMEs manufacturing value chains are distributed and dependent on complex informa9on and material flows requiring new approaches to reduce the complexity of manufacturing management systems. They need ubiquitous tools suppor9ng collabora9on among value chain partners and providing advanced algorithms to achieve op9miza9on of manufacturing assets and to respond faster and more efficiently to unforeseen changes. Manufacturing Industry is in need of tools to have real-‐9me communica9on with the shop floor to overcome the lengthy process of faults detec9on, monitoring and produc9on planning.
IntroducDon § Knowledge is of crucial importance to companies in the manufacturing domain and it is difficult to store and share. § Knowledge Management (KM) and InformaDon Retrieval (IR) are mechanisms for achieving a beZer capitalizaDon over the available business informaDon. § One scenario in which efficient knowledge management and sharing would help companies collaborate for achieving beZer results is collaboraDve e-‐procurement. Companies share a common understanding of the knowledge in order to use the same semanDc connotaDons on their own domain. § The presented work uses KM, IR tools and domain ontologies as a semanDc referenDal to represent, enrich and retrieve crucial knowledge that can be used during the e-‐procurement process. §
It also aims at automa9ze ontology enrichment through na ontology evolu9on process, in order to close the loop of knowledge crea9on and capitaliza9on in e-‐ procurement tasks.
Main Concepts • Knowledge Management • InformaDon Retrieval • Indexing and Searching • SemanDc ReferenDals & Ontologies
• IndexaDon through SemanDc Vectors & the Vector Space Model
Knowledge Enrichment Process 1. Pre-‐Processing Stage
Load Domain Search and label relevant Store relevant Ontology and Knowledge Sources with Knowledge Sources RelaDons’ support of Domain on Knowledge weights Experts repository
• The pre-‐processing stage holds the preparaDon of both operaDonal environment and input sources (e.g. domain ontologies, documents) • Experts play a key role to help inspecDng and pre-‐labelling those relevant knowledge sources (KS) • All relevant KSs are selected and stored in a Knowledge Base repository, to help to deal with the management of all sources to be indexed. • Experts may also need to validate weights of ontological relaDons (next step).
Knowledge Enrichment Process 2. Ontology EvoluDon
Enriching ontology with new concepts and relaDons
• The ontology evoluDon happens when new KSs are included in the knowledge repository • New concepts must be added, new relaDons may be idenDfied and equivalent terms can be extended. • Allows the assessment of the relevance of the domain ontology used regarding the current knowledge base repository. • AssociaDon rules are automaDcally discovered based on the semanDc liaisons connecDng ontological concepts.
Knowledge Enrichment Process 3. SemanDc Vector CreaDon
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CreaDon of SemanDc Vectors
the system calculates the G-‐idf scores for all terms, to reduce the size of the staDsDcal vector according to a certain relevance another procedure normalizes the staDsDcal vector aaer pruning the terms. Next, the semanDc enrichment is responsible for the generaDon of the keyword, taxonomy and ontology-‐based vectors, respecDvely.
Knowledge Enrichment Process 4. ClassificaDon
5. EvaluaDon
Classify input Knowledge Sources into predefined categories
Analyse evidence / ExplanaDon for ClassificaDon
• Relies on the applicaDon of unsupervised classificaDon algorithm (K-‐Means clustering), in order to group KSs into various categories, called clusters
• Assesses the overall approach using classical precision and recall metrics to measure performance
C2NET Cloud Plaeorm Architecture
C2NET E-‐Procurement Approach KS semanDc vectors to find crucial knowledge within an e-‐Procurement collaboraDve task within C2NET (best supplier or logisDcs operator, in terms of price, speed of delivery, distance, cargo space and quanDDes, etc) Will collect business, producDon and operaDonal informaDon from different sources (PDM/PLM, producDon systems, ERP, smart sensors) Will use a semanDc search module based on the Vector Space Model, and calculates the proximity between a query inpuZed by a user and the KSs in the C2NET KB C2NET users upload their KSs, such as catalogues, inventory lists, supplier and logisDcs plans, among others, these will be pre-‐processed and the corresponding semanDc vectors will be created and stored. Ontology evoluDon is also executed.
C2NET Metalworking Networked SME’s
The companies were not able to easily share informaDon between to help them achieve common goals (even being geographically close)
Conclusions By using the semanDc search tool, companies within C2NET will be able to easily find documents and other knowledge sources which, otherwise, would be difficult to capitalize on, and to share with other companies.
This assumpDon will be evaluated during the Dme of execuDon of the C2NET project by both the evaluaDon measures made to the knowledge enrichment and ontology evoluDon processes and the project’s pilot companies themselves, during the pilot execuDon.
Nevertheless, the aim is to help these companies to organize their knowledge, and to solve their knowledge capitalizaDon issues, both internally and when collaboraDng with third parDes.
Thank you! Any quesDons?
