Entity Summarisation with Limited Edge Budget on Undirected and Directed Knowledge Graphs

The paper concerns a novel problem of summarising entities with limited presentation budget on entity-relationship knowledge graphs and propose an efficient algorithm for solving this problem. The algorithm has been implemented in two variants: undirected and directed, together with a visualisation tool. Experimental user evaluation of the algorithm was conducted on real large semantic knowledge graphs extracted from the web. The reported results of experimental user evaluation are promising and encourage to continue the work on improving the algorithm.

Ranking clustered Keyword Search On Semi structured data

No Abstrac

Generating Concise and Readable Summaries of XML Documents

XML has become the de-facto standard for data representation and exchange, resulting in large scale repositories and warehouses of XML data. In order for users to understand and explore these large collections, a summarized, bird's eye view of the available data is a necessity. In this paper, we are interested in semantic XML document summaries which present the "important" information available in an XML document to the user. In the best case, such a summary is a concise replacement for the original document itself. At the other extreme, it should at least help the user make an informed choice as to the relevance of the document to his needs. In this paper, we address the two main issues which arise in producing such meaningful and concise summaries: i) which tags or text units are important and should be included in the summary, ii) how to generate summaries of different sizes.%for different memory budgets. We conduct user studies with different real-life datasets and show that our methods are useful and effective in practice

Entity Summarisation with Limited Edge Budget on Undirected and Directed Knowledge Graphs

The paper concerns a novel problem of summarising entities with limited presentation budget on entity-relationship knowledge graphs and propose an efficient algorithm for solving this problem. The algorithm has been implemented in two variants: undirected and directed, together with a visualisation tool. Experimental user evaluation of the algorithm was conducted on real large semantic knowledge graphs extracted from the web. The reported results of experimental user evaluation are promising and encourage to continue the work on improving the algorithm.

MA Algorithm to Generate Semantic Web Related Clustered Hierarchy for Keyword Search

Keyword search in XML documents is based on the notion of lowest common ancestors in the labeled trees model of XML documents. It has recently gained a lot of research interest in the database community. In this paper we propose the Modified Active (MA) algorithm which is an improvement over the Active clustering algorithm. The algorithm takes into consideration the entity aspect of the nodes to find the level of the node pertaining to a keyword input by the user. A portion of the Bibliography database is used to experimentally evaluate the Modified Active algorithm. Evaluation results show that MA algorithm generates clusters faster than the Active algorithm and this increases the efficiency of the system

Generating Preview Tables for Entity Graphs

Users are tapping into massive, heterogeneous entity graphs for many applications. It is challenging to select entity graphs for a particular need, given abundant datasets from many sources and the oftentimes scarce information for them. We propose methods to produce preview tables for compact presentation of important entity types and relationships in entity graphs. The preview tables assist users in attaining a quick and rough preview of the data. They can be shown in a limited display space for a user to browse and explore, before she decides to spend time and resources to fetch and investigate the complete dataset. We formulate several optimization problems that look for previews with the highest scores according to intuitive goodness measures, under various constraints on preview size and distance between preview tables. The optimization problem under distance constraint is NP-hard. We design a dynamic-programming algorithm and an Apriori-style algorithm for finding optimal previews. Results from experiments, comparison with related work and user studies demonstrated the scoring measures' accuracy and the discovery algorithms' efficiency.Comment: This is the camera-ready version of a SIGMOD16 paper. There might be tiny differences in layout, spacing and linebreaking, compared with the version in the SIGMOD16 proceedings, since we must submit TeX files and use arXiv to compile the file