Information supply for business processes: coupling workflow with document analysis and information retrieval

Abecker, Andreas; Bernardi, Ansgar; Maus, Heiko; Sintek, Michael; Wenzel, Claudia

doi:10.1016/s0950-7051(00)00087-3

Cited by 72 publications

(24 citation statements)

References 7 publications

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“…Gnowsis [12,13] additionally investigates peer-to-peer networks in enlarging the central knowledge base like SWAP [6] Based on an index service -Receiver -it combines organizational and individual elements within a Personal Information Model (PIM) [1] -Interpretator -and integrates rulebased (Analyzer) different information sources (e-mail, files, ontologies, web, etc.). Sauermann et al [13] define Semantic Desktop as a device in which an individual stores all her digital information like documents, multimedia and messages.…”

Section: Semantic Desktop "Gnowsis" Enabling Kcfmentioning

confidence: 99%

“…Semantic Desktop Architecture [13] Receiver: The Semantic Desktop uses the software brainFiler [8] providing the index service for all clients and determining structure and concepts of the indexed files. The brainFiler therefore creates term vectors of each file and cluster documents initially with their respective origin folder -see [1] -to improve the precision of a search result.…”

Section: Semantic Desktop "Gnowsis" Enabling Kcfmentioning

confidence: 99%

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Increasing Search Quality with the Semantic Desktop in Proposal Development

Siebert

Smits

Sauermann

et al. 2006

Practical Aspects of Knowledge Management

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Abstract. Quicker response times and less production costs of proposal development require further automation of sales assistant functionality in CRM environments. Automation still struggles with the handling of abstraction and the subjective character of knowledge. Based on the knowledge creation framework the paper outlines and tests the increase of search quality with Semantic Desktop technology. The discussion of peer-to-peer settings and semantic concepts illustrates the influence of individual perspectives on search quality. It reveals first potentials and benefits for process-integration, like semantic CRM and illustrates approaches to increase knowledge worker's productivity.

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Section: Semantic Desktop "Gnowsis" Enabling Kcfmentioning

confidence: 99%

Section: Semantic Desktop "Gnowsis" Enabling Kcfmentioning

confidence: 99%

Increasing Search Quality with the Semantic Desktop in Proposal Development

Siebert

Smits

Sauermann

et al. 2006

Practical Aspects of Knowledge Management

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show abstract

“…First, the main advantages of WfMS for retrieving context are presented, including the drawbacks of current (commercial) systems. Two projects from our research department show different approaches for retrieving workflow context (for a detailed comparison see [3]). After that, a comprehensive classification scheme for workflow context will be presented.…”

Section: Introductionmentioning

confidence: 99%

Workflow Context as a Means for Intelligent Information Support

Maus

2001

Modeling and Using Context

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Abstract. The paper presents Workflow Management Systems (WfMS) as valuable information sources for context information for accomplishing intelligent information support. First, the main advantages of WfMS for retrieving context are presented. Two projects from our research department show different approaches for retrieving workflow context and enabling an intelligent information support. However, they have to cope with the absence of a comprehensive representation concept for workflow context in WfMS. To overcome this, this paper presents a comprehensive classification scheme for workflow context.

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“…However, with the growing amount of information in organizational databases, KMSs face the increasingly difficult challenge of helping users find pertinent information efficiently. Thus, knowledge retrieval is considered a core component of systems that support workers engaged in knowledgeintensive tasks in a business environment (Abecker, Bernardi, Maus, Sintek, & Wenzel, 2000). To resolve the problem of retrieving needed information from a vast amount of codified knowledge, (IR) techniques coupled with workflow management systems (WfMS) are employed to support proactive delivery of task-specific information based on the context of the tasks within the overall process (Abecker et al, 2000;Fenstermacher, 2002).…”

mentioning

confidence: 99%

“…Thus, knowledge retrieval is considered a core component of systems that support workers engaged in knowledgeintensive tasks in a business environment (Abecker, Bernardi, Maus, Sintek, & Wenzel, 2000). To resolve the problem of retrieving needed information from a vast amount of codified knowledge, (IR) techniques coupled with workflow management systems (WfMS) are employed to support proactive delivery of task-specific information based on the context of the tasks within the overall process (Abecker et al, 2000;Fenstermacher, 2002). For example, the KnowMore system maintains task specifications (profiles) that detail the processcontext of tasks and associated information items (Abecker et al, 2000).…”

mentioning

confidence: 99%

Learning dynamic information needs: A collaborative topic variation inspection approach

Liu

Chang

2009

J. Am. Soc. Inf. Sci.

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For projects in knowledge-intensive domains, it is crucially important that knowledge management systems are able to track and infer workers' up-to-date information needs so that task-relevant information can be delivered in a timely manner. To put a worker's dynamic information needs into perspective, we propose a topic variation inspection model to facilitate the application of an implicit relevance feedback (IRF) algorithm and collaborative filtering in user modeling. The model analyzes variations in a worker's task-needs for a topic (i.e., personal topic needs) over time, monitors changes in the topics of collaborative actors, and then adjusts the worker's profile accordingly. We conducted a number of experiments to evaluate the efficacy of the model in terms of precision, recall, and F-measure. The results suggest that the proposed collaborative topic variation inspection approach can substantially improve the performance of a basic profiling method adapted from the classical RF algorithm. It can also improve the accuracy of other methods when a worker's information needs are vague or evolving, i.e., when there is a high degree of variation in the worker's topic-needs. Our findings have implications for the design of an effective collaborative information filtering and retrieval model, which is crucial for reusing an organization's knowledge assets effectively. IntroductionInformation seeking or searching is regarded as the primary activity of knowledge workers when they execute tasks. The 2004 International Data Corporation (IDC) Report (Feldman, 2004) estimated that 90% of a company's accessible information is only used once. If knowledge cannot be accessed easily and reused effectively, the accumulated information is essentially useless and the company's production costs will increase because similar knowledge must be recreated. Thus, successful knowledge management (KM) practices require an understanding of workers' information needs to ensure effective information-seeking activities when they perform long-term tasks.Although some KMSs incorporate information retrieval (IR) functions, workers find it difficult to express their information needs by using short query terms (LaBrie & St. Louis, 2003;Pons-Porrata, Berlanga-Llavori, & Ruiz-Shulcloper, 2007;Ruthven, 2001). In many cases the worker may only have a general idea about a topic and may be uncertain about what information is required to execute the task at hand (Belkin, Oddy, & Brooks, 1982;Jansen, 2005;White, Jose, & Ruthven, 2003, White & Kelly, 2006. The anomalous state of knowledge (ASK) hypothesis, posits that a searcher's information needs arise from an anomaly in the state of knowledge; thus, there is a gap between their knowledge about a task and the perceived requirements of the task. The gap is called the information need and results in information-seeking activities to solve the problem, i.e., satisfy the searcher's information needs (Belkin et al., 1982;Byström & Järvelin, 1995;Mackay, 1960;Taylor, 1968;. To address this problem, we prop...

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Information supply for business processes: coupling workflow with document analysis and information retrieval

Cited by 72 publications

References 7 publications

Increasing Search Quality with the Semantic Desktop in Proposal Development

Increasing Search Quality with the Semantic Desktop in Proposal Development

Workflow Context as a Means for Intelligent Information Support

Learning dynamic information needs: A collaborative topic variation inspection approach

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