Exploring XML web collections with DescribeX

Abstract

As Web applications mature and evolve, the nature of the semistructured data that drives these applications also changes. An important trend is the need for increased flexibility in the structure of Web documents. Hence, applications cannot rely solely on schemas to provide the complex knowledge needed to visualize, use, query and manage documents. Even when XML Web documents are valid with regard to a schema, the actual structure of such documents may exhibit significant variations across collections for several reasons: the schema may be very lax (e.g., RSS feeds), the schema may be large and different subsets of it may be used in different documents (e.g., industry standards like UBL), or open content models may allow arbitrary schemas to be mixed (e.g., RSS extensions like those used for podcasting). For these reasons, many applications that incorporate XPath queries to process a large Web document collection require an understanding of the actual structure present in the collection, and not just the schema.To support modern Web applications, we introduce DescribeX, a powerful framework that is capable of describing complex XML summaries of Web collections. DescribeX supports the construction of heterogenous summaries that can be declaratively defined and refined by means of axis path regular expression (AxPREs). AxPREs provide the flexibility necessary for declaratively defining complex mappings between instance nodes (in the documents) and summary nodes. These mappings are capable of expressing order and cardinality, among other properties, which can significantly help in the understanding of the structure of large collections of XML documents and enhance the performance of Web applications over these collections. DescribeX captures most summary proposals in the literature by providing (for the first time) a common declarative definition forThe authors appear in alphabetical order. 11:2• M. P. Consens et al.them. Experimental results demonstrate the scalability of DescribeX summary operations (summary creation, as well as refinement and stabilization, two key enablers for tailoring summaries) on multi-gigabyte Web collections.

Summarizing semantic graphs: a survey

“…While the theoretical complexity of such queries is O(M k ), with M the number of edges and k the size of the most complex query involved, efficient graph query processors, with the help of good indexing, achieve much better performance in practice. [18] considers the summarization of large Web document collections. While the main structure in this case consists of trees, they may also feature reference edges which turn the dataset into a global graph.…”

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

“…Yu and Jagadish [25] consider the problem of summarizing huge XML schemas for presentation to a user, but do not include the content of documents in their summaries. DescribeX [4] allows to create summaries of huge collections of XML documents, focusing on both schema and content. Huang et al [10] compute snippets of an XML document to return as result of a query.…”

“…A standard example of this model is the RDF 4 data format with its SPARQL 5 query language, where a query can be viewed as a sub-graph pattern that is matched with the knowledge base to produce the results. 6 4 http://www.w3.org/RDF 5 http://www.w3.org/TR/rdf-sparql-query 6 Formally, data in RDF consists of triples: subject-predicate-object, but this is mathematically equivalent to the multi-graph model described above Structured query languages for knowledge graphs such as SPARQL allow for semantic search and are very expressive, however they are quite complex for unexperienced users and they also assume some prior knowledge about the domain (e.g.…”

RDF graph summarization for first-sight structure discovery

“…Then, for every edge labeled p which goes from s to o in the graph, the summary has an edge labeled p from the summary node corresponding to the equivalence class of s, to the node corresponding to the equivalence class of p. Such summaries, also called quotient summaries, have many good properties, mainly due to the existence of a graph homomorphism from the original graph into its summary. Quotient summaries proposed for general graphs include [21,30,22,23,10,11].…”