Journal of Information Science

The information explosion on the Internet has placed high demands on search engines. Despite the improvements in search engine technology, the precision of current search engines is still unsatisfactory. Moreover, the queries submitted by users are short, ambiguous and imprecise. This leads to a number of problems in dealing with similar queries. The problems include lack of common keywords, selection of different documents by the search engine and lack of common clicks etc. These problems render the traditional query clustering methods unsuitable for query recommendations. In this paper, we propose a new query recommendation system. For this, we have identified conceptually related queries by capturing users’ preferences using click-through graphs of web search logs and by extracting the best features, relevant to the queries, from the snippets. The proposed system has an online feature extraction phase and an offline phase in which feature filtering and query clustering are performed. Query clustering is carried out by a new tripartite agglomerative clustering algorithm, Query-Document-Concept Clustering, in which the documents are used innovatively to decouple queries and features/concepts in a tripartite graph structure. This results in clusters of similar queries, associated clusters of documents and clusters of features. We model the query recommendation problem in four different ways. Two models are non-personalized and personalized content-ignorant models. Other two are non-personalized and personalized content-aware models. Three similarity measures are introduced to estimate different kinds of similarities. Experimental results show that the proposed approach has better precision, recall and F-measure than the existing approaches.

Flat folksonomy uses simple tags and has emerged as a powerful instrument for classifying and sharing a huge amount of knowledge on Web 2.0. However, it has semantic problems, such as ambiguous and misunderstood tags. To alleviate such problems, researchers have built structured folksonomies with a hierarchical structure or relationships among tags. Structured folksonomies, however, also have some fundamental problems, such as limited tagging of pre-defined vocabulary and time-consuming manual effort required to select tags. To resolve these problems, we suggested a new method of attaching a tag with its category, which we call a categorized tag (CT), to web content. CTs entered by users are automatically and immediately integrated into a collaboratively built structured folksonomy (CSF), reflecting the tag-and-category relationships supported by the majority of users. Then, we developed a CT-based knowledge organization system (CTKOS), which builds upon the CSF to classify organizational knowledge and enables us to locate appropriate knowledge. In addition, the results of the evaluation, which we conducted to compare our proposed system with the flat folksonomy system, indicate that users perceive CTKOS to be more useful than the flat folksonomy system in terms of knowledge sharing (i.e. the tagging mechanism) and retrieval (i.e. the searching mechanism).

The objective of search and recommendation is to provide users with documents that are relevant to their needs. Keyword-based search and recommendation approaches suffer from sparsity and semantic ambiguity problems because they correlate users’ needs with documents only via keywords. Thus, for a given query, some documents that are semantically relevant to a user’s needs are not provided if they do not include specific keywords. To address this, some search approaches have used the authority of documents, which is commonly represented using hyperlinks within documents. However, if there are no hyperlinks, it is difficult to exploit the authority for ranking documents. As the links of documents are determined by their owners, the authority derived from links does not consider users’ current needs. In order to resolve these problems, we propose a unified framework for semantic search and recommendation to enrich the semantics of users’ needs and documents with their corresponding concepts and to use personalized authority derived from recommendation approaches. The proposed approach makes it possible to retrieve documents with a high degree of semantic relevance as well as high authority. Through extensive experiments, we show that our approach outperforms conventional search and recommendation approaches.

In this paper, an analysis of interlinking between 100 major European universities is given. Since websites contain links to webpages for other organizations, they may reveal the strongest relationships established between two organizations. This analysis of web links allowed us to determine the different behaviours among the universities with regard to incoming or outgoing web links; some universities had significantly greater incoming than outgoing activity. In general, there was a low level of interaction between the universities studied. Also, we observed the existence of geographic–linguistic patterns in establishing links. Five primary nuclei or blocks of universities can be identified: the group composed almost exclusively of universities from the UK; the group composed in large part of German universities, along with some from Switzerland and Austria; the cluster of universities from Mediterranean countries, including various French universities; the group of Belgian and Dutch universities, along with some from French-speaking Switzerland; and finally, the group made up of universities from the Nordic countries. Although there are some universities that overlap with several groups or clusters, the overall design is rather clear. On the other hand, the whole picture seems to agree with the results of other studies based on bibliographic co-authorship production.

Most of the existing spatial indices are constructed using a single hierarchal index structure; hence a large number of index pages (nodes) are most likely to be inspected during spatial query execution. Since spatial queries usually fetch spatial objects based on their spatial position in the space, it is significant that spatial objects are clustered in such a way that pertinent objects to a query are fetched quickly. This paper presents a method for partitioning the whole space into set of small subspaces. Then, an index structure for each subspace (called the Multi Small Index) is built. This makes it is easy to quickly retrieve spatial objects that are relevant to the query in question using their corresponding small spatial index structures and ignoring other irrelevant indices. To evaluate our new approach, we conducted a set of experimental studies using a collection of real-life spatial datasets (TIGER data files) with diverse sizes and different object sizes, densities and distributions, as well as various query sizes. The results show that (using small query sizes) our proposed structure (Multi Small Index) outperforms the original R-tree (Single Big Index) structure, achieving nearly 50% saving in disk access.

Knowledge management is normally approached in the context of a single organization’s activities. Recently the focus has been extended to activities which span beyond organizational boundaries, especially to the key role of social learning across organizations. The concept of ‘local social knowledge management’ has been used to stress the process of social learning in regional networking. This study describes the local social knowledge management in a regional development project. The knowledge sharing and creation practices in the theme groups of the project are described and particular attention is paid to the evolution of the social learning process. Three distinct but interdependent forms of knowledge sharing and creation were identified in networking. Operational networking helped people manage the current project responsibilities while strategic networking opened pathways to the future. The third form of networking boosted the personal development of project participants even when the cooperation was not continuing. The results show that there can be more accurate models in knowledge management research if the viewpoint is shifted to broader contexts where people normally interact.

This paper analyses selected literature on basic-level categories, explores related theories and discusses theoretical explanations of the phenomenon of basic-level categories. A substantial body of research has proposed that basic-level categories are the first categories formed during perception of the environment, the first learned by children and those most used in language. Experimental studies suggest that high-level (or superordinate) categories lack informativeness because they are represented by only a few attributes and low-level (or subordinate) categories lack cognitive economy because they are represented by too many attributes. Studies in library and information science have demonstrated the prevalence of basic-level categories in knowledge organization and representation systems such as thesauri and in image indexing and retrieval; and it has been suggested that the universality of basic-level categories could be used for building crosswalks between classificatory systems and user-centred indexing. However, while there is evidence of the pervasiveness of basic-level categories, they may actually be unstable across individuals, domains or cultures and thus unable to support broad generalizations. This paper discusses application of Heidegger’s notion of handiness as a framework for understanding the relational nature of basic-level categories.

Two paradigms currently exist for information search. The first is the library paradigm, which has been largely automated and is the prevailing paradigm in today’s web search. The second is the village paradigm, and although it is older than the library paradigm, its automation has not been considered, yet certain elements of its key aspects have been automated, as in the cases of the Q&A communities or novel services such as Quora. The increasing popularity and availability of online social networks and question-answering communities have encouraged revisiting of the automation of the village paradigm owing to new helpful developments, primarily that people are more connected with their acquaintances on the internet and their contact lists are available. In this survey, we study how the village paradigm is today partially automated: we consider the selection of candidates for answering questions, answering questions automatically and helping candidates to decide what questions to answer. Other aspects are also considered, for example, the automation of a reward system. We conclude that a next step towards the automation of the village paradigm involves intelligent agents that can leverage a P2P (peer-to-peer) social network, which will create new and interesting issues deeply entwined with social networks in the form of information processing by agents in parallel and side by side with people.

The Journal of Information Science does not normally carry book reviews, but when the Editor received a copy of Martin Frické’s book, Logic and the Organization of Information, he thought it was too interesting to ignore. It succinctly encapsulates years of accumulated research and practice in the field, while also adding ‘logic’ into the mix. He asked me if I could think of a suitable way of acknowledging the work, and I proposed a collection of short pieces by prominent people in the information science community. This is not, then, a review; more a salute to the author for making explicit the fundamental relationship between the ancient disciplines of logic and knowledge organization.