Collaboratively Patching Linked Data

Today's Web of Data is noisy. Linked Data often needs extensive preprocessing to enable efficient use of heterogeneous resources. While consistent and valid data provides the key to efficient data processing and aggregation we are facing two main challenges: (1st) Identification of erroneous facts and tracking their origins in dynamically connected datasets is a difficult task, and (2nd) efforts in the curation of deficient facts in Linked Data are exchanged rather rarely. Since erroneous data often is duplicated and (re-)distributed by mashup applications it is not only the responsibility of a few original publishers to keep their data tidy, but progresses to be a mission for all distributers and consumers of Linked Data too. We present a new approach to expose and to reuse patches on erroneous data to enhance and to add quality information to the Web of Data. The feasibility of our approach is demonstrated by example of a collaborative game that patches statements in DBpedia data and provides notifications for relevant changes.Comment: 2nd International Workshop on Usage Analysis and the Web of Data (USEWOD2012) in the 21st International World Wide Web Conference (WWW2012), Lyon, France, April 17th, 201

A document management methodology based on similarity contents

The advent of the WWW and distributed information systems have made it possible to share documents between different users and organisations. However, this has created many problems related to the security, accessibility, right and most importantly the consistency of documents. It is important that the people involved in the documents management process have access to the most up-to-date version of documents, retrieve the correct documents and should be able to update the documents repository in such a way that his or her document are known to others. In this paper we propose a method for organising, storing and retrieving documents based on similarity contents. The method uses techniques based on information retrieval, document indexation and term extraction and indexing. This methodology is developed for the E-Cognos project which aims at developing tools for the management and sharing of documents in the construction domain

Review of Indexing Techniques Applied in Information Retrieval

Indexing is one of the important tasks of Information Retrieval that can be applied to any form of data, generated from the web, databases, etc. As the size of corpora increases, indexing becomes too time consuming and labor intensive, therefore, the introduction of computer aided indexer. A review of indexing techniques, both human and automatic indexing has been done in this paper. This paper gives an outline of the use of automatic indexing by discussing various hashing techniques including fuzzy finger printing and locality-sensitive hashing. Two different processes of matching that are used in automatic subject indexing are also reviewed. Accepting the need of automatic indexing in a possible replacement to manual indexing, studies in the development of automatic indexing tools must continu

Term Extraction and Disambiguation for Semantic Knowledge Enrichment: A Case Study on Initial Public Offering (IPO)

Domain knowledge bases are a basis for advanced knowledge-based systems, manually creating a formal knowledge base for a certain domain is both resource consuming and non-trivial. In this paper, we propose an approach that provides support to extract, select, and disambiguate terms embedded in domain specific documents. The extracted terms are later used to enrich existing ontologies/taxonomies, as well as to bridge domain specific knowledge base with a generic knowledge base such as WordNet. The proposed approach addresses two major issues in the term extraction domain, namely quality and efficiency. Also, the proposed approach adopts a feature-based method that assists in topic extraction and integration with existing ontologies in the given domain. The proposed approach is realized in a research prototype, and then a case study is conducted in order to illustrate the feasibility and the efficiency of the proposed method in the finance domain. A preliminary empirical validation by the domain experts is also conducted to determine the accuracy of the proposed approach. The results from the case study indicate the advantages and potential of the proposed approach

Curating E-Mails; A life-cycle approach to the management and preservation of e-mail messages

E-mail forms the backbone of communications in many modern institutions and organisations and is a valuable type of organisational, cultural, and historical record. Successful management and preservation of valuable e-mail messages and collections is therefore vital if organisational accountability is to be achieved and historical or cultural memory retained for the future. This requires attention by all stakeholders across the entire life-cycle of the e-mail records. This instalment of the Digital Curation Manual reports on the several issues involved in managing and curating e-mail messages for both current and future use. Although there is no 'one-size-fits-all' solution, this instalment outlines a generic framework for e-mail curation and preservation, provides a summary of current approaches, and addresses the technical, organisational and cultural challenges to successful e-mail management and longer-term curation.

RefConcile – automated online reconciliation of bibliographic references

Comprehensive bibliographies often rely on community contributions. In such a setting, de-duplication is mandatory for the bibliography to be useful. Ideally, it works online, i.e., during the addition of new references, so the bibliography remains duplicate-free at all times. While de-duplication is well researched, generic approaches do not achieve the result quality required for automated reconciliation. To overcome this problem, we propose a new duplicate detection and reconciliation technique called RefConcile. Aimed specifically at bibliographic references, it uses dedicated blocking and matching techniques tailored to this type of data. Our evaluation based on a large real-world collection of bibliographic references shows that RefConcile scales well, and that it detects and reconciles duplicates highly accurately

Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks