Document and Corpus Quality Challenges for Knowledge Management in Engineering Enterprises

摘要

Enterprise data is an amalgam of mostly semistructured and unstructured data and documents stored in heterogeneous systems. The available structure is often not readily apparent or modeled to be useful. Formats such as PDF, DWG, Excel, or Word offer a high grade of flexibility; the issue is rather that their freeform content does not divulge its structure and meaning. In the case of binary formats as used in CAD or simulation tools, even basic textual content may be missing. Structured metadata is only sometimes available. When taking a step back, we see a challenging quality issue based not on individual documents, but on the whole corpus of enterprise documents. Our specific background is an engineering setting with large sets of documents from whole development and manufacturing lifecycles. An individual document is of limited use; the organizational knowledge is distributed throughout many separate documents and entities. In some cases, it is easy to find, but in other cases, heterogenous documents all over the organization make up the knowledge of, for example, how to build a complex processing plant. For such complex retrieval tasks, different types of relations between documents and their entities have to be identified, such as same author, same or similar parts, part of same project, subpart or subproject, predecessor, precondition, clarifications, updates, vendor lists, financial or structural relations, similar tasks in previous projects, and many more [Ahlers and Mehrpoor 2014].