Search (244 results, page 1 of 13)

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Abstract

Content Management Systeme (CMS) sind in vielen Organisationen bereits seit längerer Zeit fester Bestandteil zur Verwaltung und kollaborativen Bearbeitung von Text- und Multimedia-Inhalten. Im Zuge der rasch ansteigenden Fülle an Informationen und somit auch Wissen wird die Überschaubarkeit der Datenbestände jedoch massiv eingeschränkt. Diese und zusätzliche Anforderungen, wie automatisch Datenquellen aus dem World Wide Web (WWW) zu extrahieren, lassen traditionelle CMS immer mehr an ihre Grenzen stoßen. Dieser Beitrag diskutiert die neuen Herausforderungen an traditionelle CMS und bietet Lösungsvorschläge, wie CMS kombiniert mit semantischen Technologien diesen Herausforderungen begegnen können. Die Autoren stellen eine generische Systemarchitektur für Content Management Systeme vor, die einerseits Inhalte für das Semantic Web generieren, andererseits Content aus dem Web 2.0 syndizieren können und bei der Aufbereitung des Content den User mittels semantischer Technologien wie Reasoning oder Informationsextraktion unterstützen. Dabei wird auf Erfahrungen bei der prototypischen Implementierung von semantischer Technologie in ein bestehendes CMS System zurückgegriffen.

Theme

Content Management System

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Content

Introduction.- Ontology Development.- Compliance using Metadata.- Variety Management for Big Data.- Text Mining in Economics.- Generation of Natural Language Texts.- Sentiment Analysis.- Building Concise Text Corpora from Web Contents.- Ontology-Based Modelling of Web Content.- Personalized Clinical Decision Support for Cancer Care.- Applications of Temporal Conceptual Semantic Systems.- Context-Aware Documentation in the Smart Factory.- Knowledge-Based Production Planning for Industry 4.0.- Information Exchange in Jurisdiction.- Supporting Automated License Clearing.- Managing cultural assets: Implementing typical cultural heritage archive's usage scenarios via Semantic Web technologies.- Semantic Applications for Process Management.- Domain-Specific Semantic Search Applications.

LCSH

Management information systems
Management of Computing and Information Systems

RSWK

Wissensbasiertes System

Subject

Wissensbasiertes System
Management information systems
Management of Computing and Information Systems

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Abstract

Purpose Ontologies are used to formally describe the concepts within a domain in a machine-understandable way. Matching of heterogeneous ontologies is often essential for many applications like semantic annotation, query answering or ontology integration. Some ontologies may include a large number of entities which make the ontology matching process very complex in terms of the search space and execution time requirements. The purpose of this paper is to present a technique for finding degree of similarity between ontologies that trims down the search space by eliminating the ontology concepts that have less likelihood of being matched. Design/methodology/approach Algorithms are written for finding key concepts, concept matching and relationship matching. WordNet is used for solving synonym problems during the matching process. The technique is evaluated using the reference alignments between ontologies from ontology alignment evaluation initiative benchmark in terms of degree of similarity, Pearson's correlation coefficient and IR measures precision, recall and F-measure. Findings Positive correlation between the degree of similarity and degree of similarity (reference alignment) and computed values of precision, recall and F-measure showed that if only key concepts of ontologies are compared, a time and search space efficient ontology matching system can be developed. Originality/value On the basis of the present novel approach for ontology matching, it is concluded that using key concepts for ontology matching gives comparable results in reduced time and space.

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 68(2016) no.1, S.99-111

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Abstract

The main motivation of this chapter was to gather existing requirements and solutions, and to present a generic architectural design of semantic digital libraries. This design is meant to answer a number of requirements, such as interoperability or ability to exchange resources and solutions, and set up the foundations for the best practices in the new domain of semantic digital libraries. We start by presenting the library from different high-level perspectives, i.e., user (see Sect. 2) and metadata (see Sect. 1) perspective; this overview narrows the scope and puts emphasis on certain aspects related to the system perspective, i.e., the architecture of the actual digital library management system. We conclude by presenting the system architecture from three perspectives: top-down layered architecture (see Sect. 3), vertical architecture of core services (see Sect. 4), and stack of enabling infrastructures (see Sect. 5); based upon the observations and evaluation of the contemporary state of the art presented in the previous sections, these last three subsections will describe an in-depth model of the digital library management system.

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Abstract

Content Management Systems (CMSs) are used in almost every industry by millions of end-user organizations. In contrast to the 90s, they are no longer used as isolated applications in one organization but they support critical core operations in business ecosystems. Content management today is more interactive and more integrative: interactive because end-users are increasingly content creators themselves and integrative because content elements can be embedded into various other applications. The authors of this book investigate how Semantic Technologies can increase interactivity and integration capabilities of CMSs and discuss their business value to millions of end-user organizations. This book has therefore the objective, to reflect existing applications as well as to discuss and present new applications for CMSs that use Semantic Technologies. An evaluation of 27 CMSs concludes this book and provides a basis for IT executives that plan to adopt or replace a CMS in the near future.

Content

On the Changing Market for Content Management Systems: Status and Outlook - Wolfgang Maass Empowering the Distributed Editorial Workforce - Steve McNally The Rise of Semantic-aware Applications - Stéphane Croisier Simplified Semantic Enhancement of JCR-based Content Applications -Bertrand Delacretaz and Michael Marth Dynamic Semantic Publishing - Jem Rayfield Semantics in the Domain of eGovernment - Luis Alvarez Sabucedo and Luis Anido Rifón The Interactive Knowledge Stack (IKS): A Vision for the Future of CMS - Wernher Behrendt Essential Requirements for Semantic CMS - Valentina Presutti Evaluation of Content Management Systems - Tobias Kowatsch and Wolfgang Maass CMS with No Particular Industry Focus (versch. Beiträge)

LCSH

Management information systems

Subject

Management information systems

Theme

Content Management System

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Abstract

Ontologies need to be stored, sometimes aligned and their evolution needs to be managed. All these tasks together are called ontology management. Alignment is a central task in ontology re-use. Re-use of existing ontologies often requires considerable effort: the ontologies either need to be integrated, which means that they are merged into one new ontology, or the ontologies can be kept separate. In both cases, the ontologies have to be aligned, which means that they have to be brought into mutual agreement. The problems that underlie the difficulties in integrating and aligning are the mismatches that may exist between separate ontologies. Ontologies can differ at the language level, which can mean that they are represented in a different syntax, or that the expressiveness of the ontology language is dissimilar. Ontologies also can have mismatches at the model level, for example, in the paradigm, or modelling style. Ontology alignment is very relevant in a Semantic Web context. The Semantic Web will provide us with a lot of freely accessible domain specific ontologies. To form a real web of semantics - which will allow computers to combine and infer implicit knowledge - those separate ontologies should be aligned and linked.
Support for evolving ontologies is required in almost all situations where ontologies are used in real-world applications. In those cases, ontologies are often developed by several persons and will continue to evolve over time, because of changes in the real world, adaptations to different tasks, or alignments to other ontologies. To prevent that such changes will invalidate existing usage, a change management methodology is needed. This involves advanced versioning methods for the development and the maintenance of ontologies, but also configuration management, that takes care of the identification, relations and interpretation of ontology versions. All these aspects come together in integrated ontology library systems. When the number of different ontologies is increasing, the task of storing, maintaining and re-organizing them to secure the successful re-use of ontologies is challenging. Ontology library systems can help in the grouping and reorganizing ontologies for further re-use, integration, maintenance, mapping and versioning. Basically, a library system offers various functions for managing, adapting and standardizing groups of ontologies. Such integrated systems are a requirement for the Semantic Web to grow further and scale up. In this chapter, we describe a number of results with respect to the above mentioned areas. We start with a description of the alignment task and show a meta-ontology that is developed to specify the mappings. Then, we discuss the problems that are caused by evolving ontologies and describe two important elements of a change management methodology. Finally, in Section 4.4 we survey existing library systems and formulate a wish-list of features of an ontology library system.

Source

Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a

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Abstract

This chapter describes two case studies conducted by the Swiss Life insurance group with the objective of proving the practical applicability and superiority of ontology-based knowledge management over classical approaches based on text retrieval technologies. The first case study in the domain of skills management uses manually constructed ontologies about skills, job functions and education. The purpose of the system is to give support for finding employees with certain skills. The ontologies are used to ensure that the user description of skills and the machine-held index of skills and people use the same vocabulary. The use of a shared vocabulary increases the performance of such a system significantly. The second case study aims at improving content-oriented access to passages of a 1000 page document about the International Accounting Standard on the corporate intranet. To this end, an ontology was automatically extracted from the document. It can be used to reformulate queries that turned out not to deliver the intended results. Since the ontology was automatically built, it is of a rather simple structure, consisting of weighted semantic associations between the relevant concepts in the document. We therefore call it a 'lightweight ontology'. The two case studies cover quite different aspects of using ontologies in knowledge management applications. Whereas in the second case study an ontology was automatically derived from a search space to improve information retrieval, in the first skills management case study the ontology itself introduces a structured search space. In one case study we gathered experience in building an ontology manually, while the challenge of the other case study was automatic ontology creation. A number of the novel Semantic Web-based tools described elsewhere in this book were used to build the two systems and both case studies described have led to projects to deploy live systems within Swiss Life.

Source

Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a

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Abstract

We saw in the introduction how the Semantic Web makes possible a new generation of knowledge management tools. We now turn our attention more specifically to Semantic Web based support for virtual communities of practice. The notion of communities of practice has attracted much attention in the field of knowledge management. Communities of practice are groups within (or sometimes across) organizations who share a common set of information needs or problems. They are typically not a formal organizational unit but an informal network, each sharing in part a common agenda and shared interests or issues. In one example it was found that a lot of knowledge sharing among copier engineers took place through informal exchanges, often around a water cooler. As well as local, geographically based communities, trends towards flexible working and globalisation have led to interest in supporting dispersed communities using Internet technology. The challenge for organizations is to support such communities and make them effective. Provided with an ontology meeting the needs of a particular community of practice, knowledge management tools can arrange knowledge assets into the predefined conceptual classes of the ontology, allowing more natural and intuitive access to knowledge. Knowledge management tools must give users the ability to organize information into a controllable asset. Building an intranet-based store of information is not sufficient for knowledge management; the relationships within the stored information are vital. These relationships cover such diverse issues as relative importance, context, sequence, significance, causality and association. The potential for knowledge management tools is vast; not only can they make better use of the raw information already available, but they can sift, abstract and help to share new information, and present it to users in new and compelling ways.
In this chapter, we describe the OntoShare system which facilitates and encourages the sharing of information between communities of practice within (or perhaps across) organizations and which encourages people - who may not previously have known of each other's existence in a large organization - to make contact where there are mutual concerns or interests. As users contribute information to the community, a knowledge resource annotated with meta-data is created. Ontologies defined using the resource description framework (RDF) and RDF Schema (RDFS) are used in this process. RDF is a W3C recommendation for the formulation of meta-data for WWW resources. RDF(S) extends this standard with the means to specify domain vocabulary and object structures - that is, concepts and the relationships that hold between them. In the next section, we describe in detail the way in which OntoShare can be used to share and retrieve knowledge and how that knowledge is represented in an RDF-based ontology. We then proceed to discuss in Section 10.3 how the ontologies in OntoShare evolve over time based on user interaction with the system and motivate our approach to user-based creation of RDF-annotated information resources. The way in which OntoShare can help to locate expertise within an organization is then described, followed by a discussion of the sociotechnical issues of deploying such a tool. Finally, a planned evaluation exercise and avenues for further research are outlined.

Source

Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a

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Abstract

The purpose of this research is to develop an ontology with subsequent testing and evaluation, for identifying utility and value. The domain that has been chosen is human nervous system (HNS) disorders. It is hypothesized here that an ontology-based patient records management system is more effective in meeting and addressing complex information needs of health-care personnel. Therefore, this study has been based on the premise that developing an ontology and using it as a component of the search interface in hospital records management systems will lead to more efficient and effective management of health-care.It is proposed here to develop an ontology of the domain of HNS disorders using a standard vocabulary such as MeSH or SNOMED CT. The principal classes of an ontology include facet analysis for arranging concepts based on their common characteristics to build mutually exclusive classes. We combine faceted theory with description logic, which helps us to better query and retrieve data by implementing an ontological model. Protégé 5.2.0 was used as ontology editor. The use of ontologies for domain modelling will be of acute help to doctors for searching patient records. In this paper we show how the faceted approach helps us to build a flexible model and retrieve better information. We use the medical domain as a case study to show examples and implementation.

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Abstract

Purpose - The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts. Design/methodology/approach - In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query. Findings - The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology "with and without query expansion" is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42. Practical implications - When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved. Originality/value - In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 66(2014) no.6, S.678-696

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Abstract

By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.

Content

Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

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Abstract

This paper presents QACID an ontology-based Question Answering system applied to the CInema Domain. This system allows users to retrieve information from formal ontologies by using as input queries formulated in natural language. The original characteristic of QACID is the strategy used to fill the gap between users' expressiveness and formal knowledge representation. This approach is based on collections of user queries and offers a simple adaptability to deal with multilingual capabilities, inter-domain portability and changes in user information requirements. All these capabilities permit developing Question Answering applications for actual users. This system has been developed and tested on the Spanish language and using an ontology modelling the cinema domain. The performance level achieved enables the use of the system in real environments.

Source

Information processing and management. 45(2009) no.2, S.175-188

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Abstract

A method to develop a prototype domain ontology has been described. The domain selected for the study is Accelerator Driven Systems. This is a multidisciplinary and interdisciplinary subject comprising Nuclear Physics, Nuclear and Reactor Engineering, Reactor Fuels and Radioactive Waste Management. Since Accelerator Driven Systems is a vast topic, select areas in it were singled out for the study. Both qualitative and quantitative methods such as Content analysis, Facet analysis and Clustering were used, to develop the web-based model.

Date

22. 7.2010 19:41:16

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Abstract

This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.

Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

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Abstract

This paper describes a state-of-the-art supervised, knowledge-intensive approach to the automatic identification of semantic relations between nominals in English sentences. The system employs a combination of rich and varied sets of new and previously used lexical, syntactic, and semantic features extracted from various knowledge sources such as WordNet and additional annotated corpora. The system ranked first at the third most popular SemEval 2007 Task - Classification of Semantic Relations between Nominals and achieved an F-measure of 72.4% and an accuracy of 76.3%. We also show that some semantic relations are better suited for WordNet-based models than other relations. Additionally, we make a distinction between out-of-context (regular) examples and those that require sentence context for relation identification and show that contextual data are important for the performance of a noun-noun semantic parser. Finally, learning curves show that the task difficulty varies across relations and that our learned WordNet-based representation is highly accurate so the performance results suggest the upper bound on what this representation can do.

Source

Information processing and management. 46(2010) no.5, S.589-610

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Abstract

Domain Ontology learning has been introduced as a technology that aims at reducing the bottleneck of knowledge acquisition in the construction of domain ontologies. However, the discovery and the labelling of non-taxonomic relations have been identified as one of the most difficult problems in this learning process. In this paper, we propose OntologyLine, a new system for discovering non-taxonomic relations and building domain ontology from scratch. The proposed system is based on adapting Open Information Extraction algorithms to extract and label relations between domain concepts. OntologyLine was tested in two different domains: the financial and cancer domains. It was evaluated against gold standard ontology and was compared to state-of-the-art ontology learning algorithm. The experimental results show that OntologyLine is more effective for acquiring non-taxonomic relations and gives better results in terms of precision, recall and F-measure.

Source

Knowledge discovery, knowledge engineering and knowledge management: 7th International Joint Conference, IC3K 2015, Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. Eds.: A. Fred et al

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Abstract

Medical insurance fraud is common in many countries' medical insurance systems and represents a serious threat to the insurance funds and the benefits of patients. In this paper, we present an inference model of medical insurance fraud detection, based on a medical detection domain ontology that incorporates the knowledge base provided by the Medical Terminology, NKIMed, and Chinese Library Classification systems. Through analyzing the behaviors of irregular and fraudulent medical services, we defined the scope of the medical domain ontology relevant to the task and built the ontology about medical sciences and medical service behaviors. The ontology then utilizes Semantic Web Rule Language (SWRL) and Java Expert System Shell (JESS) to detect medical irregularities and mine implicit knowledge. The system can be used to improve the management of medical insurance risks.

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Abstract

Libraries are the tools we use to learn and to answer our questions. The quality of our work depends, among others, on the quality of the tools we use. Recent research in digital libraries is focused, on one hand on improving the infrastructure of the digital library management systems (DLMS), and on the other on improving the metadata models used to annotate collections of objects maintained by DLMS. The latter includes, among others, the semantic web and social networking technologies. Recently, the semantic web and social networking technologies are being introduced to the digital libraries domain. The expected outcome is that the overall quality of information discovery in digital libraries can be improved by employing social and semantic technologies. In this chapter we present the results of an evaluation of social and semantic end-user information discovery services for the digital libraries.

Date

1. 8.2010 12:35:22

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Abstract

An information-seeking system is described which combines traditional keyword querying of WWW resources with the ability to browse and query against RD annotations of those resources. RDF(S) and RDF are used to specify and populate an ontology and the resultant RDF annotations are then indexed along with the full text of the annotated resources. The resultant index allows both keyword querying against the full text of the document and the literal values occurring in the RDF annotations, along with the ability to browse and query the ontology. We motivate our approach as a key enabler for fully exploiting the Semantic Web in the area of knowledge management and argue that the ability to combine searching and browsing behaviours more fully supports a typical information-seeking task. The approach is characterised as "low threshold, high ceiling" in the sense that where RDF annotations exist they are exploited for an improved information-seeking experience but where they do not yet exist, a search capability is still available.

Source

Hawaii International Conference on System Sciences: Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4, Big Island, Hawaii, January 05-January 08, 2004

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Abstract

In this paper we present an approach to question answering over heterogeneous knowledge sources that makes use of different ontology management components within the scenario of a digital library application. We present a principled framework for integrating structured metadata and unstructured resource content in a seamless manner which can then be flexibly queried using structured queries expressed in natural language. The novelty of the approach lies in the combination of different semantic technologies providing a clear benefit for the application scenario considered. The resulting system is implemented as part of the digital library of British Telecommunications (BT). The original contribution of our paper lies in the architecture we present allowing for the non-straightforward integration of the different components we consider.