Ontology-Based Crime News Semantic Retrieval System
Bibliographic record
Abstract
Every day, the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis. Crime news exists on the Internet in unstructured formats such as books, websites, documents, and journals. From such homogeneous data, it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies. Keyword-based Information Retrieval (IR) systems rely on statistics to retrieve results, making it difficult to obtain relevant results. They are unable to understand the user's query and thus face word mismatches due to context changes and the inevitable semantics of a given word. Therefore, such datasets need to be organized in a structured configuration, with the goal of efficiently manipulating the data while respecting the semantics of the data. An ontological semantic IR system is needed that can find the right investigative information and find important clues to solve criminal cases. The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries. In this paper, we develop an ontology-based semantic IR system that leverages the latest semantic technologies including resource description framework (RDF), semantic protocol and RDF query language (SPARQL), semantic web rule language (SWRL), and web ontology language (OWL). We have conducted two experiments. In the first experiment, we implemented a keyword-based textual IR system using Apache Lucene. In the second experiment, we implemented a semantic system that uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries. The keyword-based system has filtered results with 51% accuracy, while the semantic system has filtered results with 95% accuracy, leading to significant improvements in the field and opening up new horizons for researchers.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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; both teacher heads agree on what is shown here.
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".