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- 143 creator achille-fokoue.
- 143 creator aditya-kalyanpur.
- 143 creator edith-schonberg.
- 143 creator julian-dolby.
- 143 creator kavitha-srinivas.
- 143 type InProceedings.
- 143 label "Extracting Enterprise Vocabularies Using Linked Open Data".
- 143 sameAs 143.
- 143 abstract "A common vocabulary is vital to smooth business operation, yet codifying and maintaining an enterprise vocabulary is an arduous, manual task. We describe a process to automatically extract a domain specific vocabulary (terms and types) from unstructured data in the enterprise guided by term definitions in Linked Open Data (LOD). We validate our techniques by applying them to the IT (Information Technology) domain, taking 58 Gartner analyst reports and using two specific LOD sources -- DBpedia and Freebase.".
- 143 hasAuthorList authorList.
- 143 hasTopic Semantic_Web.
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- 143 title "Extracting Enterprise Vocabularies Using Linked Open Data".
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- 156 creator james-dobree.
- 156 creator john-darlington.
- 156 creator landong-zuo.
- 156 creator manuel-salvadores.
- 156 creator nicholas-gibbins.
- 156 creator nigel-r-shadbolt.
- 156 creator sm-hazzaz-imtiaz.
- 156 type InProceedings.
- 156 label "Supporting Multi-View User Ontology to Understand Company Value Chains".
- 156 sameAs 156.
- 156 abstract "The objective of the Market Blended Insight (MBI) project is to develop web based techniques to improve the performance of UK Business to Business (B2B) marketing activities. The analysis of company value chains is a fundamental task within MBI because it is an important model for understanding the market place and the company interactions within it. The project has aggregated rich data profiles of 3.7 million companies that form the active UK business community. The profiles are augmented by Web extractions from heterogeneous sources to provide unparalleled business insight. Advances by the Semantic Web in knowledge representation and logic reasoning allow flexible integration of data from heterogeneous sources, transformation between different representations and reasoning about their meaning. The MBI project has identified that the market insight and analysis interests of different types of users are difficult to maintain using a single domain ontology. Therefore, the project has developed a technique to undertake a plurality of analyses of value chains by deploying a distributed multi-view ontology to capture different user views over the classification of companies and their various relationships.".
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- 156 title "Supporting Multi-View User Ontology to Understand Company Value Chains".
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- 157 creator chen-wang.
- 157 creator daniel-c-wolfson.
- 157 creator feng-cao.
- 157 creator li-ma.
- 157 creator nick-kanellos.
- 157 creator xiaoyuan-wang.
- 157 creator xingzhi-sun.
- 157 creator yue-pan.
- 157 type InProceedings.
- 157 label "Semantic Enhancement for Enterprise Data Management".
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- 157 abstract "Taking customer data as an example, the paper presents an approach to enhance the management of enterprise data by using Semantic Web technologies. Customer data is the most important kind of core business entity a company uses repeatedly across many business processes and systems, and customer data management (CDM) is becoming critical for enterprises because it keeps a single, complete and accurate record of customers across the enterprise. Existing CDM systems focus on integrating customer data from all customer-facing channels and front and back office systems through multiple interfaces, as well as publishing customer data to different applications. To make the effective use of the CDM system, this paper investigates semantic query and analysis over the integrated and centralized customer data, enabling automatic classification and relationship discovery. We have implemented these features over IBM Websphere Customer Center, and shown the prototype to our clients. We believe that our study and experiences are valuable for both Semantic Web community and data management community.".
- 157 hasAuthorList authorList.