Graph Summarization

The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie

RDF Digest: Ontology Exploration Using Summaries

Abstract. Ontology summarization aspires to produce an abridged version of the original ontology that highlights its most representative concepts. In this paper, we present RDF Digest, a novel platform that automatically produces and visualizes summaries of RDF/S Knowledge Bases (KBs). A summary is a valid RDFS document/graph that includes the most representative concepts of the schema, adapted to the corresponding instances. To construct this graph our algorithm exploits the semantics and the structure of the schema and the distribution of the corresponding data/instances. A novel feature of our platform is that it allows summary exploration through extensible summaries. The aim of this demonstration is to dive in the exploration of the sources using summaries and to enhance the understanding of the various algorithms used. Introduction Given the explosive growth in both data size and schema complexity, data sources are becoming increasingly difficult to understand and use. Ontologies often have extremely complex schemas which are difficult to comprehend, limiting the exploration and the exploitation potential of the information they contain. Besides schema, the large amount of data in those sources increase the effort required for exploring them. Over the latest years, various techniques have been provided on constructing overviews on ontologies [1-4], maintaining however the more important ontology elements. These overviews are provided by means of an ontology summary. Ontology summarization [4] is defined as the process of distilling knowledge from an ontology in order to produce an abridged version. While summaries are useful, creating a "good" summary is a non-trivial task. A summary should be concise, yet it needs to convey enough information in order to enable a decent understanding of the original schema. Moreover, the summarization should be coherent and should provide an extensive coverage of the entire ontology. So far, although a reasonable number of research works tried to address the problem of summarization from different angles, a solution that simultaneously exploits the semantics of the schemas and the data instances is still missing. In this demonstration, we focus on RDF/S KBs and demonstrate for the first time the implementation of the algorithms introduced i

Coverage-Based Summaries for RDF KBs

As more and more data become available as linked data, the need for efficient and effective methods for their exploration becomes apparent. Semantic summaries try to extract meaning from data, while reducing its size. State of the art structural semantic summaries, focus primarily on the graph structure of the data, trying to maximize the summary’s utility for query answering, i.e. the query coverage. In this poster paper, we present an algorithm, trying to maximize the aforementioned query coverage, using ideas borrowed from result diversification. The key idea of our algorithm is that, instead of focusing only to the “central” nodes, to push node selection also to the perimeter of the graph. Our experiments show the potential of our algorithm and demonstrate the considerable advantages gained for answering larger fragments of user queries.acceptedVersionPeer reviewe

RDF graph summarization: principles, techniques and applications (tutorial)

International audienceThe explosion in the amount of the RDF on the Web has lead to the need to explore, query and understand such data sources. The task is challenging due to the complex and heterogeneous structure of RDF graphs which, unlike relational databases, do not come with a structure-dictating schema. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; the summarization goal, and the main computational tools employed for summarizing graphs, are the main factors behind this diversity. This tutorial presents a structured analysis and comparison existing works in the area of RDF summarization; it is based upon a recent survey which we co-authored with colleagues [3]. We present the concepts at the core of each approach, outline their main technical aspects and implementation. We conclude by identifying the most pertinent summarization method for different usage scenarios, and discussing areas where future effort is needed

Instance-Based Lossless Summarization of Knowledge Graph With Optimized Triples and Corrections (IBA-OTC)

Knowledge graph (KG) summarization facilitates efficient information retrieval for exploring complex structural data. For fast information retrieval, it requires processing on redundant data. However, it necessitates the completion of information in a summary graph. It also saves computational time during data retrieval, storage space, in-memory visualization, and preserving structure after summarization. State-of-the-art approaches summarize a given KG by preserving its structure at the cost of information loss. Additionally, the approaches not preserving the underlying structure, compromise the summarization ratio by focusing only on the compression of specific regions. In this way, these approaches either miss preserving the original facts or the wrong prediction of inferred information. To solve these problems, we present a novel framework for generating a lossless summary by preserving the structure through super signatures and their corresponding corrections. The proposed approach summarizes only the naturally overlapped instances while maintaining its information and preserving the underlying Resource Description Framework RDF graph. The resultant summary is composed of triples with positive, negative, and star corrections that are optimized by the smart calling of two novel functions namely merge and disperse . To evaluate the effectiveness of our proposed approach, we perform experiments on nine publicly available real-world knowledge graphs and obtain a better summarization ratio than state-of-the-art approaches by a margin of 10% to 30% with achieving its completeness, correctness, and compactness. In this way, the retrieval of common events and groups by queries is accelerated in the resultant graph

Semantic Web for Everyone: Exploring Semantic Web Knowledge Bases via Contextual Tag Clouds and Linguistic Interpretations

The amount of Semantic Web data is huge and still keeps growing rapidly today. However most users are still not able to use a Semantic Web Knowledge Base (KB) effectively as desired to due to the lack of various background knowledge. Furthermore, the data is usually heterogeneous, incomplete, and even contains errors, which further impairs understanding the dataset. How to quickly familiarize users with the ontology and data in a KB is an important research challenge to the Semantic Web community.The core part of our proposed resolution to the problem is the contextual tag cloud system: a novel application that helps users explore a large scale RDF(Resource Description Framework) dataset. The tags in our system are ontological terms (classes and properties), and a user can construct a context with a set of tags that defines a subset of instances. Then in the contextual tag cloud, the font size of each tag depends on the number of instances that are associated with that tag and all tags in the context. Each contextual tag cloud serves as a summary of the distribution of relevant data, and by changing the context, the user can quickly gain an understanding of patterns in the data. Furthermore, the user can choose to include different RDFS entailment regimes in the calculations of tag sizes, thereby understanding the impact of semantics on the data. To resolve the key challenge of scalability, we combine a scalable preprocessing approach with a specially-constructed inverted index and co-occurrence matrix, use three approaches to prune unnecessary counts for faster online computations, and design a paging and streaming interface. Via experimentation, we show how much our design choices benefit the responsiveness of our system. We conducted a preliminary user study on this system, and find novice participants felt the system provided a good means to investigate the data and were able to complete assigned tasks more easily than using a baseline interface.We then extend the definition of tags to more general categories, particularly including property values, chaining property values, or functions on these values. With a totally different scenario and more general tags, we find the system can be used to discover interesting value space patterns. To adapt the different dataset, we modify the infrastructure with new indexing data structure, and propose two strategies for online queries, which will be chosen based on different requests, in order to maintain responsiveness of the system.In addition, we consider other approaches to help users locate classes by natural language inputs. Using an external lexicon, Word Sense Disambiguation (WSD) on the label words of classes is one way to understand these classes. We propose our novel WSD approach with our probability model, derive the problem formula into small computable pieces, and propose ways to estimate the values of these pieces. For the other approach, instead of relying on external sources, we investigate how to retrieve query-relevant classes by using the annotations of instances associated with classes in the knowledge base. We propose a general framework of this approach, which consists of two phases: the keyword query is first used to locate relevant instances; then we induce the classes given this list of weighted matched instances.Following the description of the accomplished work, I propose some important future work for extending the current system, and finally conclude the dissertation

Advanced Knowledge Technologies at the Midterm: Tools and Methods for the Semantic Web

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.In a celebrated essay on the new electronic media, Marshall McLuhan wrote in 1962:Our private senses are not closed systems but are endlessly translated into each other in that experience which we call consciousness. Our extended senses, tools, technologies, through the ages, have been closed systems incapable of interplay or collective awareness. Now, in the electric age, the very instantaneous nature of co-existence among our technological instruments has created a crisis quite new in human history. Our extended faculties and senses now constitute a single field of experience which demands that they become collectively conscious. Our technologies, like our private senses, now demand an interplay and ratio that makes rational co-existence possible. As long as our technologies were as slow as the wheel or the alphabet or money, the fact that they were separate, closed systems was socially and psychically supportable. This is not true now when sight and sound and movement are simultaneous and global in extent. (McLuhan 1962, p.5, emphasis in original)Over forty years later, the seamless interplay that McLuhan demanded between our technologies is still barely visible. McLuhan’s predictions of the spread, and increased importance, of electronic media have of course been borne out, and the worlds of business, science and knowledge storage and transfer have been revolutionised. Yet the integration of electronic systems as open systems remains in its infancy.Advanced Knowledge Technologies (AKT) aims to address this problem, to create a view of knowledge and its management across its lifecycle, to research and create the services and technologies that such unification will require. Half way through its sixyear span, the results are beginning to come through, and this paper will explore some of the services, technologies and methodologies that have been developed. We hope to give a sense in this paper of the potential for the next three years, to discuss the insights and lessons learnt in the first phase of the project, to articulate the challenges and issues that remain.The WWW provided the original context that made the AKT approach to knowledge management (KM) possible. AKT was initially proposed in 1999, it brought together an interdisciplinary consortium with the technological breadth and complementarity to create the conditions for a unified approach to knowledge across its lifecycle. The combination of this expertise, and the time and space afforded the consortium by the IRC structure, suggested the opportunity for a concerted effort to develop an approach to advanced knowledge technologies, based on the WWW as a basic infrastructure.The technological context of AKT altered for the better in the short period between the development of the proposal and the beginning of the project itself with the development of the semantic web (SW), which foresaw much more intelligent manipulation and querying of knowledge. The opportunities that the SW provided for e.g., more intelligent retrieval, put AKT in the centre of information technology innovation and knowledge management services; the AKT skill set would clearly be central for the exploitation of those opportunities.The SW, as an extension of the WWW, provides an interesting set of constraints to the knowledge management services AKT tries to provide. As a medium for the semantically-informed coordination of information, it has suggested a number of ways in which the objectives of AKT can be achieved, most obviously through the provision of knowledge management services delivered over the web as opposed to the creation and provision of technologies to manage knowledge.AKT is working on the assumption that many web services will be developed and provided for users. The KM problem in the near future will be one of deciding which services are needed and of coordinating them. Many of these services will be largely or entirely legacies of the WWW, and so the capabilities of the services will vary. As well as providing useful KM services in their own right, AKT will be aiming to exploit this opportunity, by reasoning over services, brokering between them, and providing essential meta-services for SW knowledge service management.Ontologies will be a crucial tool for the SW. The AKT consortium brings a lot of expertise on ontologies together, and ontologies were always going to be a key part of the strategy. All kinds of knowledge sharing and transfer activities will be mediated by ontologies, and ontology management will be an important enabling task. Different applications will need to cope with inconsistent ontologies, or with the problems that will follow the automatic creation of ontologies (e.g. merging of pre-existing ontologies to create a third). Ontology mapping, and the elimination of conflicts of reference, will be important tasks. All of these issues are discussed along with our proposed technologies.Similarly, specifications of tasks will be used for the deployment of knowledge services over the SW, but in general it cannot be expected that in the medium term there will be standards for task (or service) specifications. The brokering metaservices that are envisaged will have to deal with this heterogeneity.The emerging picture of the SW is one of great opportunity but it will not be a wellordered, certain or consistent environment. It will comprise many repositories of legacy data, outdated and inconsistent stores, and requirements for common understandings across divergent formalisms. There is clearly a role for standards to play to bring much of this context together; AKT is playing a significant role in these efforts. But standards take time to emerge, they take political power to enforce, and they have been known to stifle innovation (in the short term). AKT is keen to understand the balance between principled inference and statistical processing of web content. Logical inference on the Web is tough. Complex queries using traditional AI inference methods bring most distributed computer systems to their knees. Do we set up semantically well-behaved areas of the Web? Is any part of the Web in which semantic hygiene prevails interesting enough to reason in? These and many other questions need to be addressed if we are to provide effective knowledge technologies for our content on the web

Interactivity, Fairness and Explanations in Recommendations

More and more aspects of our everyday lives are influenced by automated decisions made by systems that statistically analyze traces of our activities. It is thus natural to question whether such systems are trustworthy, particularly given the opaqueness and complexity of their internal workings. In this paper, we present our ongoing work towards a framework that aims to increase trust in machine-generated recommendations by combining ideas from three separate recent research directions, namely explainability, fairness and user interactive visualization. The goal is to enable different stakeholders, with potentially varying levels of background and diverse needs, to query, understand, and fix sources of distrust.acceptedVersionPeer reviewe

Semantically en enhanced information retrieval: an ontology-based aprroach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, enero de 2009Bibliogr.: [227]-240 p