Exploring XML web collections with DescribeX
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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 for 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it