Mining User Intents to Compose Services for End-Users
Bibliographic record
Abstract
End-users repetitively perform various on-line tasks and invoke multiple web services for their re-occurring activities, such as planning a trip. Usually, end-users have to complete different tasks in order to achieve a goal, and look through large volumes of services to find the best ones that satisfy their constraints, such as a budget limit. Current approaches on service composition require programming skills and domain knowledge to accomplish goals. Moreover, existing approaches lack an automatic way to analyze end-users' goals and extract relevant tasks for achieving goals. In this paper, we provide a lightweight service composition framework for end-users with limited technical background. Our framework analyzes endusers' goals expressed in natural languages to mine tasks (e.g., plan a trip) and non-functional constraints (e.g., budget <; 500). Our framework extracts task models from textual descriptions of tasks (e.g., eHow, a How-to instruction website) to guide the selection of services and recommend web services that can finish tasks and satisfy constraints. We have designed and developed a prototype as a proof of concept. We conduct case studies to evaluate the effectiveness of our framework. Our framework can identify tasks with a precision of 93% and a recall of 77%, and extract non-functional constraints with a precision of 89% and a recall of 76%. A user study shows that our framework is helpful for end-users to compose services.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".