Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval
Bibliographic record
Abstract
These proceedings contain the invited talks and contributed posters of the Fourth Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR 2011), held at CIKM 2011 in Glasgow, Scotland, on October 28, 2011. After successful workshops at ECIR 2008 in Glasgow, WSDM 2009 in Barcelona, and CIKM 2010 in Toronto, this year's workshop will focus on how to best formulate and use semantic annotation of information objects and information streams for information access tasks such as search, retrieval, categorization and related information refinement tasks. ESAIR 2011 will be a real workshop where researchers from these different disciplines will work together to identify natural use cases, barriers to success, and work on ways of addressing them: Use Cases: Are we looking at the right applications? What are use cases that make obvious the need for semantic annotation of information? What tasks cannot be solved by document retrieval using the traditional bag-of-words? What are the prerequisites of successful application? How can the expressive power of semantic annotation best be put to use? What is keeping searchers from exploring these powerful search requests? Annotations: Are we using the right types of data and annotation? What types of annotation are available? Are there crucial differences between author-, software-, user-, and machine-generated annotations? What are novel types of annotations that are within our grasp? What semantic theories do we need to formulate further annotation schemes? Data Curation: Are we using contextual information in the right way? Annotations may live inside documents, or be stored externally (e.g., annotated by uncontrolled authors or tools) or both (e.g., annotation with linked data). How to keep data and metadata together? Does the annotation stop somewhere, or is all social or linked data of potential use for searching or navigating? How important is source of the annotations? Are there issues with credibility or trust? Result Aggregation: Are we using the right types of results? Whereas IR focuses almost exclusively at finding individual chunks of information, DB naturally focuses on results that combine information and produce aggregated results (think of OLAP queries), and KM naturally deals with the whole information space. How can we fruitfully combine these strengths? The workshop will consist of three main parts: A keynote by Arjen de Vries to help us formulate the challenges. A boaster and poster session with 13 papers selected by the program committee from 15 submissions (87%). Each paper was reviewed by at least two members of the program committee. Break out groups on different aspects of exploiting semantic annotations, with reports being discussed in the final session.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".