Most information retrieval technologies are designed to facilitate information discovery. However, much knowledge work involves finding and re-using previously seen information. We describe the design and evaluation of a system, called Stuff I’ve Seen (SIS), that facilitates information re-use. This is accomplished in two ways. First, the system provides a unified index of information that a person has seen, whether it was seen as email, web page, document, appointment, etc. Second, because the information has been seen before, rich contextual cues can be used in the search interface. The system has been used internally by more than 230 employees. We report on both qualitative and quantitative aspects of system use. Initial findings show that time and people are important retrieval cues. Users find information more easily using SIS, and use other search tools less frequently after installation.

MyLifeBits is a project to fulfill the Memex vision first posited by Vannevar Bush in 1945. It is a system for storing all of one's digital media, including documents, images, sounds, and videos.

this paper we describe the tools Haystack provides to Semantic Web developers for building end user applications based on RDF

Passive capture lets people record their experiences without having to operate recording equipment, and without even having to give recording conscious thought. The advantages are increased capture, and improved participation in the event itself. However, passive capture also presents many new challenges. One key challenge is how to deal with the increased volume of media for retrieval, browsing, and organizing. This paper describes the SenseCam device, which combines a camera with a number of sensors in a pendant worn around the neck. Data from SenseCam is uploaded into a MyLifeBits repository, where a number of features, but especially correlation and relationships, are used to manage the data.

We posit that a semistructured data model o#ers the right balance of rich structure and flexible (or lack of) schema allowing naive end users to record information in whatever form makes it easy for them to manage. We describe our Haystack system, which exposes the richness and flexibility of the data model while o#ering the user natural, traditional interfaces that shield them from the specifics of schemas, tuples, and database queries. We outline research challenges that remain to be addressed.

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† HP Labs As the scope of personal data grows, it becomes increasingly difficult to find what we need when we need it. Desktop search tools provide a potential answer, but most existing tools are incomplete solutions: they index content, but fail to capture dynamic relationships from the user’s context. One emerging solution to this is contextenhanced search, a technique that reorders and extends the results of content-only search using contextual information. Within this framework, we propose using strict causality, rather than temporal locality, the current state of the art, to direct contextual searches. Causality more accurately identifies data flow between files, reducing the false-positives created by context-switching and background noise. Further, unlike previous work, we conduct an online user study with a fully-functioning implementation to evaluate user-perceived search quality directly. Search results generated by our causality mechanism are rated a statistically-significant 17 % higher on average over all queries than by using content-only search or context-enhanced search with temporal locality. 1

Because the ultimate purpose of the Semantic Web is to help users locate, organize, and process information, we strongly believe that it should be grounded in the information access method humans are most comfortable with---natural language. However, the Resource Description Framework (RDF), the foundation of the Semantic Web, was designed to be easily processed by computers, not humans. To render RDF friendlier to humans, we propose to augment it with natural language annotations, or metadata written in everyday language. We argue that natural language annotations are not only intuitive and effective, but can also accelerate the pace with which the Semantic Web is being adopted. We demonstrate the use of natural language annotations from within Haystack, an end user Semantic Web platform that also serves as a testbed for our ideas. In addition to a prototype Semantic Web question answering system, we describe other opportunities for marrying natural language and Semantic Web technology.

The `SemanticLIFE' system is designed to store, manage and retrieve ones lifetime's information entities. It enables the acquisition and storage of data while giving annotations to email messages, browsed web pages, phone calls, images, contacts, life events and other resources. `SemanticLIFE' also provides intuitive and effective search mechanism based upon stored semantics. This paper presents the framework for the system to devise lifetime data store and search using recognized standards. We are aiming at a system which supports the long term memory by associating metadata with content and ontologies. The ultimate goal of our project is to build a Personal Information Management system over a Human Lifetime using ontologies as a basis for the representation of its content.

Despite numerous proposals for its creation, the semantic web has yet to achieve widespread adoption. Recently, some researchers have argued that participation in the semantic web is too difficult for "ordinary" people, limiting its growth and popularity.