Skip to main content Accessibility help
×
  1. Home
  2. Browse subjects
  3. Computer Science
  4. Knowledge Management, Databases and Data Mining
  5. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

1835 results in Knowledge Management, Databases and Data Mining

Page 54 of 92

  • Foreword

  • Edited by Marc Forster
  • Book:
    Information Literacy in the Workplace
    Published by:
    Facet
    Published online:
    08 June 2018
    Print publication:
    30 April 2017, pp xiii-xvi

  • Summary

    I was delighted to be asked to write the Foreword to this book, which bridges an important gap between the Information Literacy work researchers and practitioners do in higher education, with the support that is needed in the workplace. Better collaboration between information professionals in all sectors has long been aspired to, but it is only relatively recently that work is being done to join the dots between Information Literacy initiatives in schools, higher education and the workplace. Understanding how people's ‘information landscapes’ (Lloyd, 2010) shift as they transition to a new environment is hugely relevant and this book offers us new perspectives.

    I was particularly pleased to be invited to write this Foreword, as I am indebted to Marc Forster for a number of reasons. Most recently he has been a valuable addition to the CILIP Information Literacy Group's committee. And it was through his doctoral work that I first properly engaged with the research methodology, phenomenography, a methodology that originated in the field of education but has been used increasingly in Information Literacy research (Yates, Partridge and Bruce, 2012) in recent years. A qualitative methodology, it is concerned with the variation in the way phenomena are ‘experienced’. Understanding of the variations in the way Information Literacy is experienced and so given ‘meaning’, often in terms of the personal or collaborative knowledge it develops, helps us see how Information Literacy contributes to work, study and other aspects of our daily lives. I was already familiar with the work of Christine S. Bruce and the seven faces of Information Literacy; however, through Marc's work I saw how looking at themes and variations of complexity within experiences of Information Literacy translated into the work that I did on a day-to-day basis. Interacting with another person's work is an enlightening, highly reflective and iterative process and one that can fundamentally change our understanding of a subject.

  • 10 - The ‘hidden’ value of Information Literacy in the workplace context: how to unlock and create value

  • Edited by Marc Forster
  • Book:
    Information Literacy in the Workplace
    Published by:
    Facet
    Published online:
    08 June 2018
    Print publication:
    30 April 2017, pp 131-148

  • Summary

    This chapter will discuss:

  • • how there are many knowledge workers in the business world who are not traditionally considered information professionals but use information as part of their day-to-day work.

  • • how information professionals might play a business-driven, strategic yet pragmatic role in the enhancement of the Information Literacy (IL) capabilities of knowledge workers of all types and levels.

    • Introduction

    The job titles of information professionals are numerous and varied: from strategist, information/knowledge manager, intranet/internet manager, professional support lawyer, to CRM administrator, researcher, analyst, marketing and communication manager, product manager, training manager and many more. In this chapter, information professionals are defined as those key employees of whatever label who consciously consider how best to help the company leverage information and/or create information systems, service and marketplace to create value for their employees, clients or business stakeholders. There are many knowledge workers who are not traditionally considered information professionals but use information as part of their day-to-day work.

    As the advancement of technology empowers all knowledge workers with affordable digital tools at their fingertips, information professionals are increasingly providing coaching and training services on the effective use of information systems (where the information is stored) or communication tools (where ideas and information are exchanged). They offer advice on how to create, share, search, organize, visualize, analyse and present information. Simply speaking, they show themselves to be those pivotal individuals who introduce ‘Information Literacy’ to the workplace context.

    So how important is IL in the workplace context? This chapter examines knowledge workers in different roles, functions and levels, who work under different operating models and company cultures, to understand if and when IL adds value to the business, and if it does, how might information professionals play a business-driven, strategic yet pragmatic role to enhance knowledge workers’ IL capabilities.

  • 2 - How is Information Literacy experienced in the workplace?

  • Edited by Marc Forster
  • Book:
    Information Literacy in the Workplace
    Published by:
    Facet
    Published online:
    08 June 2018
    Print publication:
    30 April 2017, pp 11-28

  • Summary

    This chapter will discuss:

  • • how researchers have sought to understand Information Literacy (IL), including how it is ‘experienced’ in the real world.

  • • how IL can be understood as something experienced in a range of contexts meaningful to the individual and relevant to her/his contributive engagement with the workplace.

  • • how IL experience can be investigated to show the kinds of purposeful knowledge development activities that it facilitates and that are key to the functioning workplace.

    • Introduction

    According to the information theoretician Luciano Floridi, knowledge is information which is both significant and true in its meaning (Floridi, 2010); a sufficiency of which, when brought into relationship with existing knowledge (learning), leads to increased understanding and more effective decision making (Megill, 2012; Hoyt, Bailey and Yoshihashi, 2012). Information Literacy operates at the information–knowledge crux: seeking, sorting, evaluating information; managing and presenting true information within new structures and relationships of knowledge. It is experienced by someone who can ‘synthesis[e] …. information and data to create new knowledge’ (SCONUL, 2011, 11) and so promote learning.

    The findings derived from a study of the experience of IL in the nursing profession (Forster 2015a) appeared to show that IL is always an experience of the development of the knowledge, and hence the knowledge-based decision-making abilities, that nurses need in the specific contexts of their practice. The findings from this study, together with an analysis of the findings of several previous studies in IL experience (e.g. Boon, Johnston and Webber, 2007; Diehm and Lupton, 2012; Williams, 2007; Lloyd, 2006; Lupton, 2008; Limberg, 1999; Yates, Partridge and Bruce, 2009), as well as consideration of recent work by Bruce and various colleagues (Bruce and Hughes, 2010; Bruce, Hughes and Somerville, 2012), suggested that this may be true of IL experience in general. IL experience always appears to involve the development of knowledge that is meaningful in specific contexts. In the workplace, IL experiences generate the knowledge that contributes to a greater understanding, and to the successful undertaking, of designated roles and tasks (Forster, 2015a). Such experiences include an awareness of the information required in each workplace context, and the varying sources, processes and ‘coparticipatory practices’ (Lloyd, 2010) needed to develop knowledge as the context varies.

  • 7 - QueryAnswering

  • Franz Baader, Technische Universität, Dresden, Ian Horrocks, University of Oxford, Carsten Lutz, Universität Bremen, Uli Sattler, University of Manchester
  • Book:
    An Introduction to Description Logic
    Published online:
    13 June 2017
    Print publication:
    07 April 2017, pp 168-204

  • Summary

    An important application of ontologies is to provide semantics and domain knowledge for data. Traditionally, data has been stored and managed inside relational database systems (aka SQL databases) where it is organised according to a pre-specified schema that describes its structure and meaning. In recent years, though, less and less data comes from such controlled sources. In fact, a lot of data is now found on the web, in social networks and so on, where typically neither its structure nor its meaning is explicitly specified; moreover, data coming from such sources is typically highly incomplete. Ontologies can help to overcome these problems by providing semantics and background knowledge, leading to a paradigm that is often called ontology-mediated querying. As an example, consider data about used-car offers. The ontology can add knowledge about the domain of cars, stating for example that a grand tourer is a kind of sports car. In this way, it becomes possible to return a car that the data identifies as a grand tourer as an answer to a query which asks for finding all sports cars. In the presence of data, a fundamental description logic reasoning service is answering database queries in the presence of ontologies. Since answers to full SQL queries are uncomputable in the presence of ontologies, the prevailing query language is conjunctive queries (CQs) and slight extensions thereof such as unions of conjunctive queries (UCQs) and positive existential queries. Conjunctive queries are essentially the select-from-where fragment of SQL, written in logic.

    In this chapter, we study conjunctive query answering in the presence of ontologies that take the form of a DL TBox. In particular, we show how to implement this reasoning service using standard database systems such as relational (SQL) systems and Datalog engines, taking advantage of those systems’ efficiency and maturity. Since database systems are not prepared to deal with TBoxes, we need a way to “sneak them in”. While there are several approaches to achieve this, here we will concentrate on query rewriting: given a CQ q to be answered and a TBox T, produce a query qT such that, for any ABox A, the answers to q on A and T are identical to the answers to qT given by a database system that stores A as data.

  • 8 - Ontology Languages and Applications

  • Franz Baader, Technische Universität, Dresden, Ian Horrocks, University of Oxford, Carsten Lutz, Universität Bremen, Uli Sattler, University of Manchester
  • Book:
    An Introduction to Description Logic
    Published online:
    13 June 2017
    Print publication:
    07 April 2017, pp 205-227

  • Summary

    As discussed in Section 1.2, DL systems have been used in a range of application domains, including configuration, software information and documentation systems and databases, where they have been used to support schema design, schema and data integration, and query answering. More recently, DLs have played a central role in the semantic web [Hor08], having been adopted as the basis for ontology languages such as OIL, DAML+OIL and OWL [HPSvH03]. This has rapidly become the most prominent application of DLs, and DL knowledge bases are now often referred to as ontologies.

    In computer science, an ontology is a conceptual model specified using some ontology language; this idea was succinctly captured by Gruber in his definition of an ontology as “an explicit specification of a conceptualisation” [Gru93]. Early ontology languages were often based on frames, but as in the case of early DLs, a desire to provide them with precise semantics and well-defined reasoning procedures increasingly led to ontology languages becoming logic-based. The OIL ontology language was something of a compromise: it had a frame-based syntax, but complemented this with a formal semantics based on a mapping to SHIQ. In DAML+OIL and OWL the DL-based semantics were retained, but the frame-based syntax of OIL was replaced with a structure much closer to DL-style axioms.

    In Section 8.1 we will discuss OWL in more detail, examining its relationship to RDF and to SROIQ, its syntax (or rather syntaxes), some features that go beyond what is typically found in a DL, and its various profiles or sub-languages. In Section 8.2 we will look at some interesting examples of OWL tools and applications.

    The OWL ontology language

    OWL is a semantic web ontology language developed by the World Wide Web Consortium (W3C), an international community that defines Web technologies. W3C follows a consensus-driven process for the publication of specification documents for Web technologies, in particular Recommendations, which are considered Web standards. OWL was first standardised in 2004, and then revised in 2012, with the revision being denoted OWL 2. Although using a variety of more “Web-friendly” syntaxes based, e.g., on XML and RDF, the basic structure of OWL corresponds closely with that of a DL, and includes such familiar constructs as existential and value restrictions, (qualified) number restrictions, inverse roles, nominals and role hierarchies (see Chapter 2).

  • 2 - A Basic Description Logic

  • Franz Baader, Technische Universität, Dresden, Ian Horrocks, University of Oxford, Carsten Lutz, Universität Bremen, Uli Sattler, University of Manchester
  • Book:
    An Introduction to Description Logic
    Published online:
    13 June 2017
    Print publication:
    07 April 2017, pp 10-49

  • Summary

    In this chapter, we introduce and explain the basic notions of Description Logic, including syntax, semantics and reasoning services, and we explain how the latter are used in applications.

    The concept language of the DLALC

    In this section, we will describe the central notions of Description Logic first on an intuitive level and then on a more precise level. As a running example, we use the domain of university courses and teaching, and we will use a conceptualisation given informally, in graphical form, in Figure 2.1. Please note that this is one way of viewing university teaching – which might be very different from the reader's way of viewing it. Also, as it is an informal representation, different readers may interpret arrows in different ways; that is, our representation does not come with a well-defined semantics that would inform us in an unambiguous way how to interpret the different arrows. In the next sections, we will describe our way of viewing university teaching in a DL knowledge base, thereby establishing some constraints on the meaning of terms like “Professor” and “teaches” used in Figure 2.1 and throughout this section.

    In Description Logic, we assume that we want to describe some abstraction of some domain of interest, and that this abstraction is populated by elements. We use three main building blocks to describe these elements:

    1. • Concepts represent sets of elements and can be viewed as unary predicates. Concepts are built from concept names and role names (see below) using the constructors provided by the DL used. The set a concept represents is called its extension. For example, Person and Course are concept names, and m is an element in the extension of Person and c6 is in the extension of Course. To make our life a bit easier, we often use “is a” as an abbreviation for “is in the extension of” as, for example, in “m is a Person”.

    2. • Role names stand for binary relations on elements and can be viewed as binary predicates. If a role r relates one element with another element, then we call the latter one an r-filler of the former one. For example, if m teaches c6, then we call c6 a teaches-filler of m.

  • 3 - A Little Bit of Model Theory

  • Franz Baader, Technische Universität, Dresden, Ian Horrocks, University of Oxford, Carsten Lutz, Universität Bremen, Uli Sattler, University of Manchester
  • Book:
    An Introduction to Description Logic
    Published online:
    13 June 2017
    Print publication:
    07 April 2017, pp 50-68

  • Summary

    The main purpose of this chapter is to show that sets of models of ALC concepts or knowledge bases satisfy several interesting properties, which can be used to prove expressivity and decidability results. To be more precise, we will introduce the notion of bisimulation between elements of ALC interpretations, and prove that ALC concepts cannot distinguish between bisimilar elements. On the one hand, we will use this to show restrictions of the expressive power of ALC: number restrictions, inverse roles and nominals cannot be expressed within ALC. On the other hand, we will employ bisimulation invariance of ALC to show that ALC has the tree model property and satisfies closure under disjoint union of models. We will also show that ALC has the finite model property, though not as a direct consequence of bisimulation invariance. These properties will turn out to be useful in subsequent chapters and of interest to people writing knowledge bases: for example, ALC's tree model property implies that it is too weak to describe the ring structure of many chemical molecules since any ALC knowledge base trying to describe such a structure will also have acyclic models. In the present chapter, we will only use the finite model property (or rather the stronger bounded model property) to show a basic, not complexity-optimal decidability result for reasoning in ALC. For the sake of simplicity, we concentrate here on the terminological part of ALC, i.e., we consider only concepts and TBoxes, but not ABoxes.

    To obtain a better intuitive view of the definitions and results introduced below, one should recall that interpretations of ALC can be viewed as graphs, with edges labelled by roles and nodes labelled by sets of concept names. More precisely, in such a graph

    1. • the nodes are the elements of the interpretation and they are labeled with all the concept names to which this element belongs in the interpretation;

    2. • an edge with label r between two nodes says that the corresponding two elements of the interpretation are related by the role r.

  • 5 - Complexity

  • Franz Baader, Technische Universität, Dresden, Ian Horrocks, University of Oxford, Carsten Lutz, Universität Bremen, Uli Sattler, University of Manchester
  • Book:
    An Introduction to Description Logic
    Published online:
    13 June 2017
    Print publication:
    07 April 2017, pp 106-139

  • Summary

    In Chapter 4, we looked at concrete algorithms for reasoning in ALC and some of its extensions. In this chapter, we are taking a more abstract viewpoint and discuss the computational complexity of reasoning, which essentially is the question of how efficient we can expect any reasoning algorithm for a given problem to be, even on very difficult (“worst-case”) inputs. Although we will concentrate on the basic reasoning problems satisfiability and subsumption for the sake of simple exposition, all results established in this chapter also apply to the corresponding KB consistency problem. In fact, there are very few relevant cases in which the computational complexity of satisfiability and of KB consistency diverge. We start with ALC and show that the complexity of satisfiability and of subsumption depend on the TBox formalism that is used: without TBoxes and with acyclic TBoxes, it is PSPACE-complete while general TBoxes raise the complexity to EXPTIME-complete. Then we consider two extensions of ALC, ALCOI and ALCOIQ, and show that satisfiability and subsumption are more difficult in these DLs: in ALCOI, satisfiability and subsumption are EXPTIME-complete already without TBoxes. We show only hardness to illustrate the increase in complexity. In ALCOIQ, reasoning even becomes NEXPTIME-complete (without TBoxes). Again, we show only hardness. Finally, we consider two extensions of ALC that render reasoning undecidable: role value maps and a certain concrete domain based on the natural numbers and incrementation.

    Before starting to analyse the computational complexity of DLs, let us recall some basics of complexity theory. A complexity class is a set of problems that share some relevant computational property such as being solvable within the same resource bounds. For example, PTIME is the class of all problems that can be solved by a deterministic Turing machine in time polynomial in the size of the input. In this chapter, we will mainly be concerned with the following standard complexity classes, which we order according to set inclusion:

    The reader is referred to standard textbooks on complexity theory for the exact definition of these classes [AB09, Sip97, Pap94]. It is commonly believed that the inclusions shown above are all strict, but proofs have not yet been found.

  • 1 - Introduction

  • Franz Baader, Technische Universität, Dresden, Ian Horrocks, University of Oxford, Carsten Lutz, Universität Bremen, Uli Sattler, University of Manchester
  • Book:
    An Introduction to Description Logic
    Published online:
    13 June 2017
    Print publication:
    07 April 2017, pp 1-9

  • Summary

    This is, to the best of our knowledge, the first textbook dedicated solely to Description Logic (DL), a very active research area in logic-based knowledge representation and reasoning that goes back to the late 1980s and that has a wide range of applications in knowledge-intensive information systems. In this introductory chapter we will sketch what DLs are, how they are used and where they come from historically. We will also explain how to use this book.

    What are DLs and where do they come from?

    Description logics (DLs) are a family of knowledge representation languages that can be used to represent knowledge of an application domain in a structured and well-understood way. The name description logics is motivated by the fact that, on the one hand, the important notions of the domain are represented by concept descriptions, i.e., expressions that are built from atomic concepts (unary predicates) and atomic roles (binary predicates) using the concept and role constructors provided by the particular DL; on the other hand, DLs differ from their predecessors, such as semantic networks and frames, in that they are equipped with a logic-based semantics which, up to some differences in notation, is actually the same semantics as that of classical first-order logic.

    Description logics typically separate domain knowledge into two components, a terminological part called the TBox and an assertional part called the ABox, with the combination of a TBox and an ABox being called a knowledge base (KB). The TBox represents knowledge about the structure of the domain (similar to a database schema), while the ABox represents knowledge about a concrete situation (similar to a database instance). TBox statements capturing knowledge about a university domain might include, e.g., a teacher is a person who teaches a course, a student is a person who attends a course and students do not teach, while ABox statements from the same domain might include Mary is a person, CS600 is a course and Mary teaches CS600. As already mentioned, a crucial feature of DLs is that such statements have a formal, logic-based semantics.

  • 3 - Families of Dissimilarity between Nodes

  • François Fouss, Université Catholique de Louvain, Belgium, Marco Saerens, Université Catholique de Louvain, Belgium, Masashi Shimbo
  • Book:
    Algorithms and Models for Network Data and Link Analysis
    Published online:
    05 July 2016
    Print publication:
    12 July 2016, pp 102-142

  • Summary

    Introduction

    This chapter is a follow-up to the previous chapter. It presents more advanced material involving recent attempts to define useful distances and similarities between nodes of a graph. While meaningful in many contexts and popular, the shortest-path distance does not convey information about the degree of connectivity between the nodes. In some occasions, we would like a distance that also captures the information about their connection rate, with a high connectivity being considered as an indication that the two nodes are close in some sense (e.g., they can easily exchange information). In other words, the presence of many indirect paths (as opposed to direct links) between nodes also suggests some kind of proximity between them.

    As seen in the previous chapter, the resistance distance and the commute-time distance capture this property. However, we also saw that these quantities suffer from the fact that, when the graph becomes larger, they converge to a meaningless limit function (see [790, 792] or very recently [370], and the previous chapter, Section 2.5.3). This effect was called “being lost in space” in [790] and is related to the fact that a simple random walk mixes before hitting its target [370].

    This means that both the shortest-path distance and the (Euclidean) commutetime distance have some inconvenient flaws, at least in the case of large graphs, and depending on the application. In some sense, they can be considered as two extremes of a continuum, considering only the length at one end, and considering only connectivity (and without taking care of the length) at the other end.

    In this context, several researchers recently proposed towork with parametric dissimilarities or distances interpolating between the shortest-path distance and the commutetime distance [20, 155, 157, 292, 459, 833]. They all depend on a continuous parameter and therefore define “families of distances.” At one limit of the value of the parameter, these quantities converge to the shortest-path distance while at the other end, they converge to the commute-time distance. They therefore “interpolate” between the two distances. The idea is that when the parametric, interpolated, distance is not too far from the shortest-path distance, it integrates the degree of connectivity between the nodes into the distance while not being too sensitive to the effect of “being lost in space” [790].

  • 6 - Labeling Nodes: Within-Network Classification

  • François Fouss, Université Catholique de Louvain, Belgium, Marco Saerens, Université Catholique de Louvain, Belgium, Masashi Shimbo
  • Book:
    Algorithms and Models for Network Data and Link Analysis
    Published online:
    05 July 2016
    Print publication:
    12 July 2016, pp 235-275

  • Summary

    Introduction

    This chapter introduces some techniques to assign a class label to an unlabeled node, based on the knowledge of the class of some labeled nodes as well as the graph structure. This is a form of the task known as supervised classification in the machine learning and pattern recognition communities. Consider for example the case of a patents network [554] where each patent is a node and there is a directed link between two patents i and j if i cites j. In addition to the resulting graph structure, some information related to the nodes could be available, for instance, the industrial area of the patent (chemicals, information and communication technologies, drugs and medicals, electrical and electronics, etc.). Assume that the industrial area is known for some patents (labeled nodes) but not yet known for some other nodes (unlabeled nodes). The within-network classification or node classification task [89] aims to infer the label of the unlabeled nodes from the labeled ones and the graph structure.

    As discussed in [553], within-network classification falls into the semisupervised classification paradigm [2, 10, 152, 844, 847]. The goal of semisupervised classification is to learn a predictive function using a small amount of labeled samples together with a (usually large) amount of unlabeled samples, the labels being missing or unobserved for these samples. Semisupervised learning tries to combine these two sources of information (labeled + unlabeled data) to build a predictive model in a better way than simply using the labeled samples alone, and thus simply ignoring the unlabeled samples. Indeed, in general, labeled data are expensive (think, for example, about an expert who has to label the cases manually), whereas unlabeled data are ubiquitous, for example, web pages. Hence, trying to exploit the distribution of unlabeled data during the estimation process can prove helpful. Among popular semisupervised algorithms, we find co-training, expectation-maximization algorithms, transductive inference, and so on – for a comprehensive survey of the topic see, for example, [844, 847].

    However, to be effective, semisupervised learning algorithms on a graph rely on some strong assumptions about the distribution of these labels. The main assumption is that neighboring nodes are likely to belong to the same class and thus to share the same class label.

Page 54 of 92