Bibliographic record
Abstract
To survive in the 21st century, enterprises need to collaborate. Collaboration at the enterprise-level presupposes the interoperability of the underlying information systems. Access to heterogeneous information sources must be provided transparently while maintaining their autonomy. Further, the availability of nearly unlimited information calls for efficient and precise information retrieval, which can be achieved by making the semantics embedded in information sources explicit. Solving the semantic interoperability problem becomes imperative to the success of information search and retrieval applications and enterprises that rely on them. Inspired by self-organizing systems found in biology, physics, and computing, the approach of emergent semantics has been proposed as a solution to the semantic interoperability problem. Emergent semantics refers to the bottom-up construction of interoperable systems, in which semantically related peers are discovered and linked together during the normal operation of the system. Individual information source providers will provide mappings (so-called semantic bridges) between their own local and semantically related foreign information sources. Emergent Semantics in a peer-to-peer (P2P) network is the lowest common knowledge, semantically relevant concepts, among all the peers of the network. Local mappings between peers with different knowledge representations, and their correctness, are prerequisites for the creation of emergent semantics. Yet, approaches to emergent semantics often fail to distinguish between permanent and transient mapping faults. This may result in erroneously labeling peers as having incompatible knowledge representations. In turn, this can further prevent such peers from interacting with other semantically related peers . This is because, in emergent semantics, peers use past interactions to determine which peers they will interact with in future collaborations. This chapter will explore the issue of semantic mapping faults. This issue has not received enough attention in the literature. Specifically, it will focus on the effect of non-permanent semantic mapping faults on both inclusiveness of semantic emergence and robustness of applications and systems that use semantic mappings. A fault-tolerant emergent semantics algorithm with the ability to resist transient semantic mapping faults is also provided. The contributions of this chapter are: (a) an analysis of the impact of the semantic mapping faults on the inclusiveness of semantic knowledge sharing in P2P systems, (b) a preliminary solution to the problems created by semantic mapping faults in P2P semantic knowledge sharing systems, and (c) a qualitative analysis of the causal links between fault causes and fault types. The rest of this chapter is organized as follows. Section II provides broad discussion and literature review about semantic interoperability problem among heterogeneous information source. Section III defines what we mean by a semantic mapping fault and the types of faults. Section IV lists sources of semantic mapping faults. Section V classifies temporal semantic mapping faults. Section VI describes the emergent semantics approach. Section VII presents an algorithm to eliminate the harmful effects of transient mapping faults on emergent semantics (fault-tolerant emergent semantics). Section VIII concludes the chapter and Section IX identifies directions for future work.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".