giving order by space and time- querying and enriching...

Giving Order by Space and Time- Querying and EnrichingPersonal Information Using Spatiotemporal Contexts

Yingjie Hu

Space and Time KnowledgeOrganization Lab

Department of Geography

Outline

� Introduction

� Related Work

� Context-aware Personal Information Management

� Conclusions and Future Work

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Introduction

� Personal information is the information used by people tocomplete their daily activities� Todo lists, emails, appointments, pictures, documents, videos...� Can be related to work (e.g., documents for a meeting) or to personal

activities (e.g., photos about a vacation)� Can be on your local device (e.g., a document on your laptop) or

available on the Web (e.g., a webpage about a conference)

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Introduction

� Each of us is constantly creating and maintaining a certainamount of personal information

� With the prevalence of inexpensive mass storage devices, thevolume and variety of the personal information are increasingrapidly, revealing the limitations of traditional personalinformation mangement systems.

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Introduction

� Users need to constantly maintain a folder structure, andmemorize the path to the files.

� Desktop search tools are based on keywords, and can’tunderstand the relations between files (e.g., PIM.pdf is apaper for the ACM GIS conference).

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Related Work

� Personal Information Management (PIM) is defined as boththe practice and the study of the activities people perform inorder to acquire, organize, maintain and retrieve informationfor everyday use (W. Jones and J. Teevan, 2007; W. Jonesand D. Maier, 2003).

� Existing PIM tools: MyLifeBits, Haystack, PlacelessDocuments...

� Semantic desktop

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Related Work

� Semantic desktop adopts the technologies from the SemanticWeb to organize personal information

� Personal data are organized based on ”concepts” instead offolders.

� It records the user’s actions (e.g. Opening a document) on thedesktop, and associate those actions with related files andsoftware applications.

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Related Work

� However, the capabilities of the Semantic desktop arerestricted to the information universe: it lacks anunderstanding between the user’s actions in the informationuniverse and his/her activities in the physical world.

� Semantic desktop can only organize the data on the localdesktop, while the information people need may come fromboth the web and the local device.

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Context-aware Personal Information Management

� Personal information is always created, acquired, used, andsaved under particular spatiotemporal contexts in the physicalworld.

� Such contexts can be introduced into PIM to give spatial andtemporal meaning to personal data.

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� Extracting contexts from location-based social network

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� Organizing personal information using spatialtemporalcontexts, and enriching local data with geographic informationfrom the Web of Linked Data.

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� Software implementation

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� Advanced queries enabled by context-aware PIM� Show me the webpages I browsed when I was drinking a coffee at

Arbor.� Show me all the files I opened when I was travelling in Vienna.� Show me the files I used in the restaurant which serves lobster.� ...

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� Contexts which cover a large time span, e.g. a conference

� ACM GIS conference: submission deadline was Jun 25, 2012,conference was from Nov. 6th to 9th 2012.

� Related information is available on the Web of Linked Data,and can be integrated with the local device to organizepersonal information.

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� Organizing the information related to a conference

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� Software implementation

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� Advanced queries enabled by context-aware PIM� Show me the publications of the conference participant whose first

name is ”Peter”.� Show me the website of the car rental company which I used when

planning my trip to the conference.� Show me all the conferences I have participated in Europe.� ...

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Conclusions

� Personal information is always acquired, created, used, andsaved under particular spatiotemporal contexts.

� We integrate such contexts with personal devices to organizepersonal information.

� Location-based social network and volunteered geographicinformation from the Web of Linked Data are employed toprovide contexts.

� Ontologies are designed to associate the user’s activities in thephysical world with his/her actions in the information universe.

� Software implementations are developed as proof-of-concept.

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Future Work

� Our work so far focuses on personal data fusion andorganization.

� A next step will be to use machine learning algorithms to doreasoning: mine the hidden knowledge based on the contextsand the user’s personal files.

� Providing a personalized SPARQL endpoint which supportsmore flexible queries.

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Acknowledgement

� Krzysztof Janowicz

� Mike Goodchild

� Grant McKenzie

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� Questions and comments?

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