Adding Relevance to XML

XML query languages proposed so far are limited to Boolean retrieval in the sense that query results are sets of qualifying XML elements or subgraphs. This search paradigm is intriguing for “closed” collections of XML documents such as e-commerce catalogs, but we argue that it is inadequate for searching the Web where we would prefer ranked lists of results based on relevance estimation. IR-style Web search engines, on the other hand, are incapable of exploiting the additional information made explicit in the structure, element names, and attributes of XML documents. In this paper we present a compact query language, coined XXL for “flexible XML search language”, that reconciles both search paradigms by combining XML graph pattern matching with relevance estimations and producing ranked lists of XML subgraphs as search results. The paper describes the language design, sketches implementation issues, and presents preliminary experimental results. XML query languages proposed so far are limited to Boolean retrieval in the sense that query results are sets of qualifying XML elements or subgraphs. This search paradigm is intriguing for “closed” collections of XML documents such as e-commerce catalogs, but we argue that it is inadequate for searching the Web where we would prefer ranked lists of results based on relevance estimation. IR-style Web search engines, on the other hand, are incapable of exploiting the additional information made explicit in the structure, element names, and attributes of XML documents. In this paper we present a compact query language, coined XXL for “flexible XML search language”, that reconciles both search paradigms by combining XML graph pattern matching with relevance estimations and producing ranked lists of XML subgraphs as search results. The paper describes the language design, sketches implementation issues, and presents preliminary experimental results