1. GENERATING AUTOMATIC SEMANTIC ANNOTATIONS FOR RESEARCH DATASETS AYUSH SINGHAL AND JAIDEEP SRIVASTAVA CS DEPT., UNIVERSITY OF MINNESOTA, MN, USA

2. CONTENTS  Motivation  Problem statement  Proposed approach  Data type labelling  Experiments and results  Application concept  Experiments and results  Similar dataset identification  Experiments and results  Conclusions and future work

3. MOTIVATION  Annotation is act of adding a note by way of comment or explanation.  Apart from documents, images, videos are searchable only when they have tags or annotations (i.e. content)  Recently, genomic databases, archeological databases are annotated for indexing.

4. ANNOTATING RESEARCH DATASETS  No context- hard to be searchable by popular search engines.  Make the dataset visible and informative.

5. EXAMPLE OF STRUCTURED ANNOTATION

6. PROBLEM STATEMENT  Given a data name “D” as a string of English characters, the research task is to generate semantic annotations for the dataset denoted by “D” in the following categories:  Characteristic data type  Application domain  List of similar datasets

7. PROPOSED APPROACH Research challenges  No universal schema for describing content of a dataset.  Common attribute, dataset name.  No well known structure for semantic annotation of research datasets.  Proposed structure should positively impact user’s search for datasets.

8. CONTEXT GENERATION Critical step: how to generate useful context for a dataset. Usage of the dataset in research. Research articles and journals. Get a proxy using web knowledge: Google scholar search engine. Used the top-50 results to build context for the dataset “Global context”

9. IDENTIFYING DATA TYPE LABELS  For a dataset ‘D’: Given: global context of ‘D’, a list of data types Required: data type of ‘D’  Approach: Supervised Multi-label classification Feature construction: 0. Preprocessing of global context-stop word removal etc. 1. BOW and TFIDF representation of Global context of ‘D’. 2. Dimensionality reduction by PCA- 98% of variance coverage

10. EXPERIMENTS AND RESULTS DatasetInstancesLabel countLabel densityLabel cardinality SNAP4250.341.69 UCI11040.2751.1 Ground truth: author provided data type labels. Baseline: ZeroR classifier. Evaluation metrics: typical multi-label classification metrics ( Tsoumakas et al 2010) MeasureZeroRAdaBoostMH (tfidf) Fmeasure ↑ 0.0250.172 Average Precision ↑ 0.6570.663 Macro AUC ↑ 0.50.555 MeasureZeroRAdaBoostMH (BOW) Fmeasure ↑ 0.8540.873 Average Precision ↑ 0.9080.924 Macro AUC ↑ 0.50.54 SNAP dataset UCI dataset

11. CONCEPT GENERATION  Given a dataset ‘D’, find k-descriptors (n-gram words) for the application of dataset.  Approach: Concept extraction from world knowledge (wikipedia, dbpedia)  Input feature: Global context of ‘D’.  Preprocessing of global context  Used text analytic tools (AlchemyAPI) for concept generation.  Pruning of input query terms

12. EXPERIMENTS AND RESULTS  Baseline: Context generated from the short description provided by the owner. Text pre-processing was done.  Evaluation metrics: user rating. Comparison of average user rating on UCI and SNAP dataset. UCI datasetSNAP dataset

13. IDENTIFYING SIMILAR DATASETS  Given a dataset ‘D’, find k-most similar datasets from a list of datasets.  Approach: cosine similarity between TFIDF vectors of global-context of ‘D’ and global-context of d_i in list of datasets.  Top-k selection from list ranked in descending order.

14. EXPERIMENTS AND RESULTS  Ground truth: dataset categorization provided by the dataset repository owners. Different categorization for SNAP and UCI.  Baseline: Context generated from owner’s description.  Evaluation metrics: precision@k SNAP datasetUCI dataset

15. USE CASE: SYNTHETIC QUERYING  Synthetic querying on the annotated database of research datasets.  50 queries on SNAP database and 50 queries on UCI database.  Query structure: find a dataset used for like  are random generated from their respective lists.  Evaluation metric: overlap between context of retrieved results and the input query.  Baseline: querying on Google database and extracting dataset names from the retrieved results.

16. QUANTITATIVE AND QUALITATIVE EVALUATION Comparison of Google results with annotated DB for a few samples

17. CONCLUSIONS AND FUTURE WORK  Real world datasets play an important role- testing and validation purposes.  General purpose search engines cannot find datasets due to lack of annotation.  A novel concept of structured semantic annotation of dataset- data type labels, application concepts, similar datasets.  Annotation generated using global context from the web corpus.  Data type labels identification using multi-label classifier- using web context helps to improve accuracy both for SNAP and UCI test datasets.

18. CONCLUSIONS AND FUTURE WORK  Concept generation using web context performs better than baseline based on user ratings.  Web context is not significantly helpful in identifying similar datasets for UCI and SNAP datasets.  18% improvement in accuracy over normal datasets search using Google ( for synthetic queries).  Future work: finding an overall encompassing structure of annotation ; extending analysis across different domains.

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