Enabling Data Analytics from Knowledge Graphs @ ISWC 2017 Doctoral Consortium
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The document discusses the challenges of data analytics from scientific data due to issues with metadata and data integration. It explores the advantages of using knowledge graphs (KGs) to enhance data accessibility and improve the efficiency of data preparation processes for data scientists. The research aims to establish methodologies and tools that bridge the gap between scientific KGs and conventional data analysis tools.
Enabling Data Analytics from Knowledge Graphs @ ISWC 2017 Doctoral Consortium
- 1. Enabling Data Analytics from Knowledge Graphs Henrique Santos Universidade de Fortaleza, Fortaleza, CE, Brazil The 16th International Semantic Web Conference (ISWC 2017) – Doctoral Consortium Vienna, Austria – 22 October 2017
- 2. 22 October 2017Henrique Santos - Enabling Data Analytics from Knowledge Graphs2 The 16th International Semantic Web Conference (ISWC 2017) – Vienna, Austria – Doctoral Consortium Problem statement ● Datasets are the most common source of scientifc data for data analysis ● Lack of metadata, not clean, can’t be directly combined or compared ● Knowledge Graphs for scientifc data are on the rise ● Many approaches, multiple uses: but data scientists are still using datasets ● Consequence: data preparation takes around 80% of the time of the whole analytical process (PATIL, 2012) ● How to maintain enough metadata related to scientifc data? ● How to exploit that knowledge to foster data analytics activities? ● How integrate data from scientifc KGs with regular data tools like R, Python or BI softwares?
- 3. 22 October 2017Henrique Santos - Enabling Data Analytics from Knowledge Graphs3 The 16th International Semantic Web Conference (ISWC 2017) – Vienna, Austria – Doctoral Consortium Related work ● W3C’s CSV on the Web ● Scientifc ontologies ● SSN – Semantic Sensor Network ● VSTO – Virtual Solar-Terrestrial Observatory ● HAScO – Human-Aware Science Ontology ● Indicators ● GCI Ontology ● Scientifc Knowledge graphs ● Gene Ontology, Bio2RDF, The Graph of Things
- 4. 22 October 2017Henrique Santos - Enabling Data Analytics from Knowledge Graphs4 The 16th International Semantic Web Conference (ISWC 2017) – Vienna, Austria – Doctoral Consortium Research questions & Hypothesis Q1 Can ontologies be used to successfully bridge the knowledge gap between acquired scientifc data and data users? If so, how? Q2 Will data users and applications beneft from the use of knowledge behind each scientifc data point? Q3 How to provide data access for scientifc KGs in a way that can be consumed by routine data tools while making use of the attached data knowledge to facilitate analytics? H1 The reuse of scientifc data ontologies with proper extensions and their alignments to domain ontologies can mitigate the current loss of knowledge during data acquisition H2 Providing data points together with their knowledge (e.g. provenance, contextual knowledge) to data users and applications can facilitate data analytics compared to current dataset usage. H3 A hybrid RDF serialization format that suits the needs of existing data tools but also is able to convey knowledge can be used to serialize data from KGs together with its associated metadata. H4 A query API for scientifc KGs can be used to output data together with its associated metadata in a better way than current tools for querying RDF data for data tools.
- 5. 22 October 2017Henrique Santos - Enabling Data Analytics from Knowledge Graphs5 The 16th International Semantic Web Conference (ISWC 2017) – Vienna, Austria – Doctoral Consortium Approach Data annotation KG building KG browsing KG serialization Intelligent applications C HAScO VSTO-I HACitO prov:Activityprov:Activity hasco: Studyhasco: Study hasco: DataAcquisition hasco: DataAcquisition vstoi: Deployment vstoi: Deployment xsd:dateTime xsd:dateTime isData AcquisitionOf hasDeployment prov: startedAtTime prov: endedAtTime vstoi: Instrument vstoi: Instrument vstoi: Platform vstoi: Platform vstoi: Detector vstoi: Detector hasDetectorhasInstrument hasPlatform C ● Automatic data visualization ● Data cleansing ● Infer semantic diference between data points ● ...
- 6. 22 October 2017Henrique Santos - Enabling Data Analytics from Knowledge Graphs6 The 16th International Semantic Web Conference (ISWC 2017) – Vienna, Austria – Doctoral Consortium Preliminary results SANTOS, H. et al. Contextual Data Collection for Smart Cities. In: Proceedings of the Sixth Workshop on Semantics for Smarter Cities. Bethlehem, PA, USA. 2015. SANTOS, H. et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards. In: The Semantic Web - Proceedings of the 14th Extended Semantic Web Conference (ESWC 2017). Portorož, Slovenia. 2017. Data annotation KG building KG browsing KG serialization Intelligent applications
- 7. 22 October 2017Henrique Santos - Enabling Data Analytics from Knowledge Graphs7 The 16th International Semantic Web Conference (ISWC 2017) – Vienna, Austria – Doctoral Consortium Evaluation plan KG evaluation (H1): state-of-the-art KG evaluation approaches discussed in (PAULHEIM, 2017). Metadata evaluation (H2): gathering data analytics use cases and assessing how the associated metadata facilitates the use of the data. KG querying & serialization (H3, H4): tests with data scientists and feld specialists acting as users of our proposed KG and processes. Using their data (preferably from diferent studies and sources), we intend to build a scientifc KG adding the relevant metadata and then provide them tools for querying the data and preparing datasets for their routine data analytics. Then, questionnaires will be applied to measure how much our approach has eased their tasks in contrast with their regular processes.
- 8. 22 October 2017Henrique Santos - Enabling Data Analytics from Knowledge Graphs8 The 16th International Semantic Web Conference (ISWC 2017) – Vienna, Austria – Doctoral Consortium Relevancy ● We expect this research to bring straight benefts to data scientists and feld specialists, by providing specifcations and tools that we claim will ease their data preparation tasks ● KG serialization technique will promote interoperability between scientifc data in KGs and existing non-semantic data tools which we believe will broaden the use of KGs to even more knowledge areas
- 9. 22 October 2017Henrique Santos - Enabling Data Analytics from Knowledge Graphs9 The 16th International Semantic Web Conference (ISWC 2017) – Vienna, Austria – Doctoral Consortium Refections ● Promoting data analytics from scientifc data in KGs is still in its early stages ● Difcult to query the needed data ● Lack of methods and tools to easily cope data tools with data from KGs ● Knowledge exploitation to foster data analysis is minimal ● Our contributions ● KG specifcation aligned with data analytics requirements ● Data fle format able to convey both data and metadata ● Method for data access and retrieval in scientifc KGs based on user queries ● Our resources ● Indicator and domain ontologies for developed use-cases ● Implementations of the proposed method for data access
- 10. 22 October 2017Henrique Santos - Enabling Data Analytics from Knowledge Graphs10 The 16th International Semantic Web Conference (ISWC 2017) – Vienna, Austria – Doctoral Consortium hos@edu.unifor.br @hansidm http://henriquesantos.org Enabling Data Analytics from Knowledge Graphs Henrique Santos Thank you for your attention Advisor: Prof. João José Vasco Peixoto Furtado, Docteur