Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

January 19, 2011

Curation is the New Search is the New Curation – Post

Filed under: Indexing,Search Engines,Search Interface,Searching — Patrick Durusau @ 1:22 pm

Curation is the New Search is the New Curation

Paul Kedrosky sees a return to curation as the next phase in searching. In part because search algorithms can be gamed…, but read the post. He has an interesting take on the problem.

The one comment I would add is that curation will mean not everything is curated.

Should it be?

What criteria would you use for excluding material to be curated from your index of (insert your favorite topic)?

Proposition: It is an error to think everything that can be searched is worth indexing (or curation).

Topic-based Index Partitions for Efficient and Effective Selective Search

Filed under: Clustering,Search Interface,Searching — Patrick Durusau @ 11:10 am

Topic-based Index Partitions for Efficient and Effective Selective Search Authors: Anagha Kulkarni and Jamie Callan

Abstract:

Indexes for large collections are often divided into shards that are distributed across multiple computers and searched in parallel to provide rapid interactive search. Typically, all index shards are searched for each query. This paper investigates document allocation policies that permit searching only a few shards for each query (selective search) without sacrificing search quality. Three types of allocation policies (random, source-based and topic-based) are studied. K-means clustering is used to create topic-based shards. We manage the computational cost of applying these techniques to large datasets by defining topics on a subset of the collection. Experiments with three large collections demonstrate that selective search using topic-based shards reduces search costs by at least an order of magnitude without reducing search accuracy.

What is unclear to me is whether mapping shards across independent and distinct collections that have topic-based shards would be as effective?

That would depend on the similarity of the shards but that is measurable. Not to mention mappable by a topic map.

It would be interesting if large collections started offering topic-based shard APIs to their contents.

Such that a distributed query could search shards that have been mapped as being relevant to a particular query.

Control-F

Filed under: Interface Research/Design,Search Interface — Patrick Durusau @ 6:24 am

Dan Russell of Google, notes in Why is search sometimes easy and sometimes hard? Understanding serendipity and expertise in the mind of the searcher, teaching someone to use Control-F to search for text on a page, out performs 16 other changes they made to a search interface. An improvement of 12% in time-to-result measure.

Now for the sad news:

  • 90% of all US internet users do NOT know how to Control-F
  • 50% of all US teachers do NOT know how to Control-F

Two questions:

  1. Does your topic map improve time-to-result by 12% or better?
  2. Do your users know how to use Control-F?

*****
PS: This is a great presentation. I have other comments on it but wanted to single this one out for your attention.

January 16, 2011

The Changing Face of Search – Post

Filed under: Information Retrieval,Search Interface,Searching — Patrick Durusau @ 11:26 am

The Changing Face of Search, Tony Rusell-Rose along with Udo Kruschwitz, and Andy MacFarlane have penned a post about changes they see coming to search.

The entire article is worth your time but one part stood out for me:

… Personalisation does not mean that users will be required to explicitly declare their interests (this is exactly what most users do not want to do!); instead, the search engine tries to infer users’ interests from implicit cues, e.g. time spent viewing a document, the fact that a document that has been selected in preference to another ranked higher in the results list, and so on. Personalised search results can be tailored to individual searchers and also to groups of similar users (“social networks”). (emphasis in original)

[Users don’t want] to explicitly declare their interests.

This has implications for topic map authoring.

Similar to users resisting building explicit document models and/or writing in markup. (Are you listening RDF/RDFa fans?)

Complaining that users don’t want to learn markup, explicitly declare their subjects, or use pre-written RDF vocabularies, is not a solution.

Effective topic map (or other semantic) authoring solutions are going to infer subjects and assist users in correcting its inferences.

December 8, 2010

Barriers to Entry in Search Getting Smaller – Post

Filed under: Indexing,Interface Research/Design,Search Engines,Search Interface,Searching — Patrick Durusau @ 9:49 am

Barriers to Entry in Search Getting Smaller

Jeff Dalton, Jeff’s Search Engine Caffè, makes a good argument that the barriers to entering the search market are getting smaller.

Jeff observes that blekko can succeed with a small number of servers only because its search demand is low.

True, but how many intra-company or litigation search engines are going to have web-sized user demands?

Start-ups need not try to match Google in its own space, but can carve out interesting and economically rewarding niches of their own.

Particularly if those niches involve mapping semantically diverse resources into useful search results for their users.

For example, biomedical researchers probably have little interest in catalog entries that happen to match gene names. Or any of the other common mis-matches offered by entire web search services.

In some ways, search the entire web services have created their own problem and then attempted to solve it.

My research interests are in information retrieval broadly defined so a search engine limited to library schools, CS programs (their faculty and students), the usual suspects for CS collections, library/CS/engineering organizations, with semantic mapping, would suit me just find.

Noting that the semantic mis-match problem persists even with a narrowing of resources, but the benefit of each mapping is incrementally greater.

Questions:

  1. What resources are relevant to your research interests? (3-5 pages, web or other citations)
  2. Create a Google account to create your own custom search engine and populate it with your resources.
  3. Develop and execute 20 queries against your search engine and Google, Bing and one other search engine of your choice. Evaluate and report the results of those queries.
  4. Would semantic mapping such as we have discussed for topic maps be more or less helpful with your custom search engine versus the others you tried? (3-5 pages, no citations)

October 30, 2010

8 Keys to Findability

Filed under: Information Retrieval,Interface Research/Design,Search Engines,Search Interface,Searching — Patrick Durusau @ 8:56 am

8 Keys to Findability mentions in closing:

The average number of search terms is about 1.7 words, which is not a lot when searching across millions of documents. Therefore, a conversation type of experience where users can get feedback from the results and refine their search makes for the most effective search results.

I have a different take on that factoid.

The average user needs only 1.7 words to identify a subject of interest to them.

Why the gap between 1.7 words and the number of words required for “effective search results?”

Why ask?

Returning millions of “hits” is on 1.7 words is meaningless.

Returning the ten most relevant “hits” on 1.7 words is a G***** killer.

October 27, 2010

How does search behavior change as search becomes more difficult?

Filed under: Interface Research/Design,Search Interface,Searching — Patrick Durusau @ 4:38 am

How does search behavior change as search becomes more difficult? Authors: Anne Aula, Rehan M. Khan, Zhiwei Guan Keywords: behavioral signals, difficult search tasks, search engines, search strategies, web search

Abstract:

Search engines make it easy to check facts online, but finding some specific kinds of information sometimes proves to be difficult. We studied the behavioral signals that suggest that a user is having trouble in a search task. First, we ran a lab study with 23 users to gain a preliminary understanding on how users’ behavior changes when they struggle finding the information they’re looking for. The observations were then tested with 179 participants who all completed an average of 22.3 tasks from a pool of 100 tasks. The large-scale study provided quantitative support for our qualitative observations from the lab study. When having difficulty in finding information, users start to formulate more diverse queries, they use advanced operators more, and they spend a longer time on the search result page as compared to the successful tasks. The results complement the existing body of research focusing on successful search strategies.

Seeking clues to trigger the offering of help/suggestions when users are having difficulty with a search.

For topic maps, a similar line of research could be on what properties trigger recognition of particular subjects for a given audience.

  1. How would you design research to test what properties trigger subject recognition?
  2. How would the results of such research impact your design of a topic map interface?
  3. Would you offer/hide information based on self-identification of users? Why/why not?

October 25, 2010

The Short Comings of Full-Text Searching

Filed under: Information Retrieval,Interface Research/Design,Search Engines,Search Interface,Searching — Patrick Durusau @ 6:31 am

The Short Comings of Full-Text Searching by Jeffrey Beall from the University of Colorado Denver.

  1. The synonym problem.
  2. Obsolete terms.
  3. The homonym problem.
  4. Spamming.
  5. Inability to narrow searches by facets.
  6. Inability to sort search results.
  7. The aboutness problem.
  8. Figurative language.
  9. Search words not in web page.
  10. Abstract topics.
  11. Paired topics.
  12. Word lists.
  13. The Dark Web.
  14. Non-textual things.

Questions:

  1. Watch the slide presentation.
  2. Can you give three examples of each short coming? (excluding #5 and #6, which strike me as interface issues, not searching issues)
  3. How would you “solve” the word list issue? (Don’t assume quantum computing, etc. There are simpler answers.)
  4. Is metadata the only approach for “non-textual things?” Can you cite 3 papers offering other approaches?

October 21, 2010

Research: What is the Interaction Cost in Information Visualization?

Filed under: Information Retrieval,Interface Research/Design,Navigation,Search Interface — Patrick Durusau @ 4:39 am

Research: What is the Interaction Cost in Information Visualization? by Enrico Bertini, came to us via Sam Hunting.

A summary of Heidi Lam’s A Framework of Interaction Costs in Information Visualization but both will repay the time spent reading/studying them.

However intuitive it may seem to its designers, no “semantic” interface is any better than it is perceived to be by its users.

Questions:

  1. After reading Lam’s article, evaluate two interfaces, one familiar to you and one you encounter as a first-time user.
  2. Using Lam’s framework, how do you evaluate the interfaces?
  3. What aspects of those interfaces would you most like to test with users?
  4. Design a test for two aspects of one of your interfaces. (project*)
  5. Care to update Lam’s listing of papers listing interactivity issues? (project)

* Warning: Test design is partially an art, partially a science and partially stumbling around in semantic darkness. Just so you are aware that done properly, this project will require extra work.

October 20, 2010

Variations/FRBR: Variations as a Testbed for the FRBR Conceptual Model

Filed under: Dataset,FRBR,Search Interface,Searching — Patrick Durusau @ 3:18 am

FRBRized data in XML for free download!

Approximately 80,000 bibliographic records for musical recordings and 105,000 or so for scores.

Be sure to take a look at the search interface and submit suggestions.

From the post:

The Variations/FRBR [1] project at Indiana University has released bulk downloads of metadata for the sound recordings presented in our Scherzo [2] music discovery system in a FRBRized XML format. The downloadable data includes FRBR Work, Expression, Manifestation, Person, and Corporate Body records, along with the structural and responsibility relationships connecting them. While this is still an incomplete representation of FRBR and FRAD, we hope that the release of this data will aid others that are studying or working with FRBR. This XML data conforms to the “efrbr” set of XML Schemas [3] created for this project.

The XML data may be downloaded from http://vfrbr.info/data/1.0/index.shtml, and comments/questions may be directed to vfrbr@dlib.indiana.edu.

One caveat to those who seek to use this data: we plan to continue improving our FRBRization algorithm into the future and have not yet implemented a way to keep entity identifiers consistent between new data loads. Therefore we cannot at this time guarantee the Work with the identifier http://vfrbr.info/work/30001, for example, will have the same identifier in the future. Therefore this data at this time should be considered highly experimental.

Many thanks to the Institute of Museum and Library Services for funding the V/FRBR project.

Also, if you’re interested in FRBR, please do check out our experimental discovery system: . We’re very interested in your feedback!

Jenn

[1] V/FRBR project home page (http://vfrbr.info); FRBR report
(http://www.ifla.org/en/publications/functional-requirements-for-bibliographic-records)

[2] Scherzo (http://vfrbr.info/search)

[3] V/FRBR project XML Schemas (http://vfrbr.info/schemas/1.0/index.shtml)

Information shamelessly stolen from Last Week in FRBR #33.

October 19, 2010

The effect of audience design on labeling, organizing, and finding shared files (unexpected result – see below)

Filed under: Information Retrieval,Interface Research/Design,Search Interface,Searching,Semantic Diversity — Patrick Durusau @ 5:33 am

The effect of audience design on labeling, organizing, and finding shared files Authors: Emilee Rader Keywords: audience design, common ground, file labeling and organizing, group information management

Abstract:

In an online experiment, I apply theory from psychology and communications to find out whether group information management tasks are governed by the same communication processes as conversation. This paper describes results that replicate previous research, and expand our knowledge about audience design and packaging for future reuse when communication is mediated by a co-constructed artifact like a file-and-folder hierarchy. Results indicate that it is easier for information consumers to search for files in hierarchies created by information producers who imagine their intended audience to be someone similar to them, independent of whether the producer and consumer actually share common ground. This research helps us better understand packaging choices made by information producers, and the direct implications of those choices for other users of group information systems.

Examples from the paper:

  • A scientist needs to locate procedures and results from an experiment conducted by another researcher in his lab.
  • A student learning the open-source, command-line statistical computing environment R needs to find out how to calculate the mode of her dataset.
  • A new member of a design team needs to review requirements analysis activities that took place before he joined the team.
  • An intelligence analyst needs to consult information collected by other agencies to assess a potential threat.

Do any of those sound familiar?

Unexpected result:

In general, Consumers performed best (fewest clicks to find the target file) when the Producer created a hierarchy for an Imagined Audience from the same community, regardless of the community the Consumer community. Consumers had the most difficulty when searching in hierarchies created by a Producer for a dissimilar Imagined Audience.

In other words, imagining an audience is a bad strategy. Create a hierarchy that works for you. (And with a topic map you could let others create hierarchies that work for them.)

(Apologies for the length of this post but unexpected interface results merit the space.)

October 13, 2010

Exploiting knowledge-in-the-head and knowledge-in-the-social-web: effects of domain expertise on exploratory search in individual and social search environments

Filed under: Information Retrieval,Interface Research/Design,Librarian/Expert Searchers,Search Interface,Searching — Patrick Durusau @ 5:05 am

Exploiting knowledge-in-the-head and knowledge-in-the-social-web: effects of domain expertise on exploratory search in individual and social search environments Authors: Ruogu Kang, Wai-Tat Fu, Thomas George Kannampallil Keywords: domain expertise, exploratory search, search behavior

Abstract:

Our study compared how experts and novices performed exploratory search using a traditional search engine and a social tagging system. As expected, results showed that social tagging systems could facilitate exploratory search for both experts and novices. We, however, also found that experts were better at interpreting the social tags and generating search keywords, which made them better at finding information in both interfaces. Specifically, experts found more general information than novices by better interpretation of social tags in the tagging system; and experts also found more domain-specific information by generating more of their own keywords. We found a dynamic interaction between knowledge-in-the-head and knowledge-in-the-social-web that although information seekers are more and more reliant on information from the social Web, domain expertise is still important in guiding them to find and evaluate the information. Implications on the design of social search systems that facilitate exploratory search are also discussed.

Every librarian should have the first page of this article posted to their office door, every library school on the local bulletin board.

Think about it. Expert searchers (read librarians) find better information than novices and can serve as guides to better information.

More research is needed on how to bridge that gap in search interfaces.

In libraries I think it is now called a “reference interview.”

(Please email, tweet, etc. this post to your librarian friends.)

Effects of popularity and quality on the usage of query suggestions during information search

Filed under: Information Retrieval,Search Interface,Searching — Patrick Durusau @ 4:44 am

Effects of popularity and quality on the usage of query suggestions during information search Authors: Diane Kelly, Amber Cushing, Maureen Dostert, Xi Niu, Karl Gyllstrom Keywords: query popularity, query quality, query recommendation, query suggestion, search behavior, social search, usage

Abstract:

Many search systems provide users with recommended queries during online information seeking. Although usage statistics are often used to recommend queries, this information is usually not displayed to the user. In this study, we investigate how the presentation of this information impacts use of query suggestions. Twenty-three subjects used an experimental search system to find documents about four topics. Eight query suggestions were provided for each topic: four were high quality queries and four were low quality queries. Fake usage information indicating how many other people used the queries was also provided. For half the queries this information was high and for the other half this information was low. Results showed that subjects could distinguish between high and low quality queries and were not influenced by the usage information. Qualitative data revealed that subjects felt favorable about the suggestions, but the usage information was less important for the search task used in this study.

Another small sample study but raises questions that successful interfaces will consider.

Where successful means used effectively and seen by users as effective. The latter being the most important measure.

Reactive information foraging for evolving goals

Filed under: Interface Research/Design,Navigation,Search Interface,Searching — Patrick Durusau @ 4:28 am

Reactive information foraging for evolving goals Authors: Joseph Lawrance, Margaret Burnett, Rachel Bellamy, Christopher Bogart, Calvin Swart Keywords: field study, information foraging theory, programming

Abstract:

Information foraging models have predicted the navigation paths of people browsing the web and (more recently) of programmers while debugging, but these models do not explicitly model users’ goals evolving over time. We present a new information foraging model called PFIS2 that does model information seeking with potentially evolving goals. We then evaluated variants of this model in a field study that analyzed programmers’ daily navigations over a seven-month period. Our results were that PFIS2 predicted users’ navigation remarkably well, even though the goals of navigation, and even the information landscape itself, were changing markedly during the pursuit of information.

In case you are wondering, “PFIS2 (Programmer Flow by Information Scent 2).”

A study of user information seeking behavior over seven (7) months following two (2) professional programmers.

Provocative work but it would be more convincing if the study sample were larger.

October 3, 2010

Automatic generation of research trails in web history

Filed under: Interface Research/Design,Search Interface,Searching,Trails — Patrick Durusau @ 7:23 am

Automatic generation of research trails in web history Authors: Elin Rønby Pedersen, Karl Gyllstrom, Shengyin Gu, Peter Jin Hong Keywords: activity based computing, automatic clustering, ethnography, semantic clustering, task browser, web history

Abstract:

We propose the concept of research trails to help web users create and reestablish context across fragmented research processes without requiring them to explicitly structure and organize the material. A research trail is an ordered sequence of web pages that were accessed as part of a larger investigation; they are automatically constructed by filtering and organizing users’ activity history, using a combination of semantic and activity based criteria for grouping similar visited web pages. The design was informed by an ethnographic study of ordinary people doing research on the web, emphasizing a need to support research processes that are fragmented and where the research question is still in formation. This paper motivates and describes our algorithms for generating research trails.

Research trails can be applied in several situations: as the underlying mechanism for a research task browser, or as feed to an ambient display of history information while searching. A prototype was built to assess the utility of the first option, a research trail browser.

What is a map if it isn’t an accumulated set of research trails?

In the early stages of what it means to create, share and extend trails into information sets.

Will you be one of the explorers who creates research trails into information sets as they pass the into the giga, tera and petabyte ranges and beyond?

Exploratory information search by domain experts and novices

Filed under: Authoring Topic Maps,Classification,Interface Research/Design,Search Interface,Searching — Patrick Durusau @ 7:18 am

Exploratory information search by domain experts and novices Authors: Ruogu Kang, Wai-Tat Fu Keywords: domain expertise, exploratory search, social search

Abstract:

The arising popularity of social tagging system has the potential to transform traditional web search into a new era of social search. Based on the finding that domain expertise could influence search behavior in traditional search engines, we hypothesized and tested the idea that domain expertise would have similar influence on search behavior in a social tagging system. We conducted an experiment comparing search behavior of experts and novices when they searched using a tradition search engine and a social tagging system. Results from our experiment showed that experts relied more on their own domain knowledge to generate search queries, while novices were influenced more by social cues in the social tagging system. Experts were also found to conform to each other more than novices in their choice of bookmarks and tags. Implications on the design of future social information systems are discussed.

Empirical validation of the idea that expert searchers (dare I say librarians?) can improve the search results for “novice” searchers.

A line of research that librarians need to take up and expand to combat budget cuts by the uninformed.

Note that experts suffer from the “vocabulary” problem just like novices, just in more sophisticated ways.

October 2, 2010

Facilitating exploratory search by model-based navigational cues

Filed under: Interface Research/Design,Search Engines,Search Interface,Searching — Patrick Durusau @ 4:34 am

Facilitating exploratory search by model-based navigational cues Authors: Wai-Tat Fu, Thomas G. Kannampallil, Ruogu Kang Keywords: exploratory learning, knowledge exchange, semantic imitation, SNIF-ACT, social tagging

Abstract:

We present an extension of a computational cognitive model of social tagging and exploratory search called the semantic imitation model. The model assumes a probabilistic representation of semantics for both internal and external knowledge, and utilizes social tags as navigational cues during exploratory search. We used the model to generate a measure of information scent that controls exploratory search behavior, and simulated the effects of multiple presentations of navigational cues on both simple information retrieval and exploratory search performance based on a previous model called SNIF-ACT. We found that search performance can be significantly improved by these model-based presentations of navigational cues for both experts and novices. The result suggested that exploratory search performance depends critically on the match between internal knowledge (domain expertise) and external knowledge structures (folksonomies). Results have significant implications on how social information systems should be designed to facilitate knowledge exchange among users with different background knowledge.

Not all users require (or can use) the same clues.

Something to think about when designing the interface, for topic maps or elsewhere.

DocuBrowse: faceted searching, browsing, and recommendations in an enterprise context

Filed under: Authoring Topic Maps,Interface Research/Design,Search Engines,Search Interface,Searching — Patrick Durusau @ 4:11 am

DocuBrowse: faceted searching, browsing, and recommendations in an enterprise context Authors: Andreas Girgensohn, Frank Shipman, Francine Chen, Lynn Wilcox Keywords: document management, document recommendation, document retrieval, document visualization, faceted search

Abstract:

Browsing and searching for documents in large, online enterprise document repositories are common activities. While internet search produces satisfying results for most user queries, enterprise search has not been as successful because of differences in document types and user requirements. To support users in finding the information they need in their online enterprise repository, we created DocuBrowse, a faceted document browsing and search system. Search results are presented within the user-created document hierarchy, showing only directories and documents matching selected facets and containing text query terms. In addition to file properties such as date and file size, automatically detected document types, or genres, serve as one of the search facets. Highlighting draws the user’s attention to the most promising directories and documents while thumbnail images and automatically identified keyphrases help select appropriate documents. DocuBrowse utilizes document similarities, browsing histories, and recommender system techniques to suggest additional promising documents for the current facet and content filters.

Watch the movie of this interface in action at the ACM page.

Then imagine it with collaboration and subject identity.

Towards a reputation-based model of social web search

Filed under: Authoring Topic Maps,Collaboration,Interface Research/Design,Mapping,Search Engines,Search Interface,Searching — Patrick Durusau @ 3:48 am

Towards a reputation-based model of social web search Authors: Kevin McNally, Michael P. O’Mahony, Barry Smyth, Maurice Coyle, Peter Briggs Keywords: collaborative web search, heystaks, reputation model

Abstract:

While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks (www.heystaks.com), a system that provides a novel approach to collaborative web search. Designed to work with mainstream search engines such as Google, HeyStaks supports searchers by harnessing the experiences of others as the basis for result recommendations. Moreover, a key contribution of our work is to propose a reputation system for HeyStaks to model the value of individual searchers from a result recommendation perspective. In particular, we propose an algorithm to calculate reputation directly from user search activity and we provide encouraging results for our approach based on a preliminary analysis of user activity and reputation scores across a sample of HeyStaks users.

The reputation system posed by the authors could easily underlie a collaborative approach to creation of a topic map.

Think collections not normally accessed by web search engines, The National Archives (U.S.) and similar document collections.

Reputation + trails + subject identity = Hard to Beat.

See www.heystaks.com as a starting point.

October 1, 2010

Tell me more, not just “more of the same”

Filed under: Authoring Topic Maps,Entity Resolution,Information Retrieval,Search Interface,Similarity,Text Analytics — Patrick Durusau @ 6:12 am

Tell me more, not just “more of the same” Authors: Francisco Iacobelli, Larry Birnbaum, Kristian J. Hammond Keywords: dimensions of similarity, information retrieval, new information detection

Abstract:

The Web makes it possible for news readers to learn more about virtually any story that interests them. Media outlets and search engines typically augment their information with links to similar stories. It is up to the user to determine what new information is added by them, if any. In this paper we present Tell Me More, a system that performs this task automatically: given a seed news story, it mines the web for similar stories reported by different sources and selects snippets of text from those stories which offer new information beyond the seed story. New content may be classified as supplying: additional quotes, additional actors, additional figures and additional information depending on the criteria used to select it. In this paper we describe how the system identifies new and informative content with respect to a news story. We also how that providing an explicit categorization of new information is more useful than a binary classification (new/not-new). Lastly, we show encouraging results from a preliminary evaluation of the system that validates our approach and encourages further study.

If you are interested in the automatic extraction, classification and delivery of information, this article is for you.

I think there are (at least) two interesting ways for “Tell Me More” to develop:

First, persisting entity recognition with other data (such as story, author, date, etc.) in the form of associations (with appropriate roles, etc.).

Second, and perhaps more importantly, to enable users to add/correct information presented as part of a mapping of information about particular entities.

SocialSearchBrowser: A novel mobile search and information discovery tool

Filed under: Authoring Topic Maps,Interface Research/Design,Search Interface,Searching,Similarity — Patrick Durusau @ 5:13 am

SocialSearchBrowser: A novel mobile search and information discovery tool Authors: Karen Church, Joachim Neumann, Mauro Cherubini and Nuria Oliver Keywords: Mobile search, social search, social networks, location-based services, context, field study, user evaluation

Abstract:

The mobile Internet offers anytime, anywhere access to a wealth of information to billions of users across the globe. However, the mobile Internet represents a challenging information access platform due to the inherent limitations of mobile environments, limitations that go beyond simple screen size and network issues. Mobile users often have information needs which are impacted by contexts such as location and time. Furthermore, human beings are social creatures that often seek out new strategies for sharing knowledge and information in mobile settings. To investigate the social aspect of mobile search, we have developed SocialSearchBrowser (SSB), a novel proof-of-concept interface that incorporates social networking capabilities with key mobile contexts to improve the search and information discovery experience of mobile users. In this paper, we present the results of an exploratory field study of SSB and outline key implications for the design of next generation mobile information access services.

Interesting combination of traditional “ask a search engine” with even more traditional “ask your friend’s” results. Sample is too small to say what issues might be encountered with wider use but definitely a step in an interesting direction.

September 11, 2010

Google’s Instant And User Expectations

Filed under: Search Interface,Searching,Topic Map Software,Topic Maps,Usability — Patrick Durusau @ 5:35 am

Google’s Instant will change user expectations for search interfaces. Any interface that is less responsive will be viewed as less capable. Quality of results will have a minor impact on user ratings of an interface. (I am projecting the results of future surveys analyzing the failure of less responsive interfaces.)

“Instant” display of the names of topics is certainly one useful response to Google’s Instant.

Or display of relationships to other topics.

Or, displaying merging results as property values are selected.

Google’s Instant has raised the bar. Will your topic map interface met the challenge?

August 31, 2010

One of These Things

Filed under: Authoring Topic Maps,Interface Research/Design,Search Interface,Subject Identity,Topic Maps — Patrick Durusau @ 8:57 am

One of These Things could be a theme song for topic maps.

It is also a good idea for a topic map authoring interface.

Say you get ten (10) “hits” back from a search. Add a “checkbox” to each “hit.” Unchecked means same as other unchecked “hits.” Checked means different from the unchecked “hits.”

The “same subject” judgment becomes a collective one of all the users of the search interface. Different “hits” are going to be unchecked in any search return.

Semantic input = Human input.

June 12, 2010

MURAKAMI Harumi

Filed under: Interface Research/Design,Researchers,Search Interface — Patrick Durusau @ 3:48 pm

MURAKAMI Harumi focuses on knowledge sharing and integration of library catalogs.

ReaD An alternative listing to dblp. DBLP lists four (4) publications, ReaD list six (6) plus fifty (50) papers and notes.

dblp

Homepage

Harumi’s (given name, MURAKAMI is the family name) work on Subject World (Japanese only) (my post on Subject World includes English language references) caught my attention because of its visualization of heterogeneous terminology in a library OPAC setting.

Since I am innocent of any Japanese, I am interested in hearing reactions from those fluent in Japanese to the visualization interface. This could also be an opportunity to explore how visualization preferences do or don’t differ across cultural lines.

May 25, 2010

A Mapmaker’s Manifesto

Filed under: Maps,Search Engines,Search Interface,Searching,Subject Identity,Usability — Patrick Durusau @ 3:48 pm

Search Patterns by Peter Moreville and Jeffrey Callender should be on your must read list. Their “Mapmaker’s Manifesto” will give you an idea of why I like the book:

  1. Search is a problem too big to ignore.
  2. Browsing doesn’t scale, even on an IPhone.
  3. Size matters. Linear growth compels a step change in design.
  4. Simple, fast, and relevant are table stakes.
  5. One size won’t fit all. Search must adapt to context.
  6. Search in iterative, social, and multisensory.
  7. Increments aren’t enough. Even Google must innovate or die.
  8. It’s not just about findability. It’s not just about the Web.
  9. The challenge is radically multidisciplinary.
  10. We must engage engineers and executives in design.
  11. We can learn from the past. Library science is still relevant.
  12. We can learn from behavior. Interaction design affords actionable results.
  13. We can learn from one user. Analytics is enriched by ethnography.
  14. Some patterns, we should study and reuse.
  15. Some patterns, we should break like a bad habit.
  16. Search is a complex adaptive system.
  17. Emergence, cocreation, and self-organization are in play.
  18. To discover the seeds of change, go outside.
  19. In science, fiction, and search, the map invents the territory.
  20. The future isn’t just unwritten—it’s unsearched.

I also like Search Patterns because the authors’ concede there are vast unknowns as opposed to saying: “If you just use our (insert paradigm/syntax/ontology/language) then all those nasty problems go away.”

I think we need to accept their invitation to face the vast unknowns head on.

May 6, 2010

How Customers Think

Filed under: Marketing,Search Interface — Patrick Durusau @ 8:34 pm

Gerald Zaltman’s How Customers Think is a non-technical summary of psychological and neurological research on consumer behavior and how that can influence marketing.

Quality of the product is not what determines product success (or failure).

May 3, 2010

Search User Interfaces: Chapter 1 (Part 1)

Filed under: Full-Text Search,Information Retrieval,Search Engines,Search Interface,Searching — Patrick Durusau @ 7:59 pm

Chapter 1, The Design of Search User Interfaces of Hearst’s Search User Interfaces, surveys searching and related issues from a user interface perspective.

I needed the reminders about the need for simplicity in search interfaces and the shift in search interface design. (sections 1.1 – 1.2) If you think you have a “simple” interface for your topic map, read those two sections. Then read them again.

Design principles for user interface design (sections 1.3 – 1.4) is a good overview and contrast between user centered design and developers deciding what users need design. (Which one did you use?)

Feedback from search interfaces (section 1.5) ranges from the use of two dimensional representation of items as icons (against) to highlighting query terms, sorting and query term suggestions (generally favorable).

Let’s work towards having interfaces that are as attractive to users as our topic map applications are good at semantic integration.

April 25, 2010

Topic Maps Roots?

Filed under: Mapping,Search Interface,Topic Map Software,Topic Maps — Patrick Durusau @ 9:26 am

Have you read: Hypermedia exploration with interactive dynamic maps by Mountaz Zizi and Michel Beaudouin-Lafon?

They define “interactive dynamic maps (IDMs),” which consist of:

topic maps, which provide visual abstractions of the semantic content of a web of documents and document maps, which provide visual abstractions of subsets of documents. (emphasis in original)

The authors speak of creating a thesaurus, user control over query expansion, using queries to create new maps, treating maps as documents, sharing maps among users, etc. Plus have screen shots of working software, SHADOCS.

The authors do not cite ISO 13250. The year? 1995 ISO/IEC 13250 became an ISO standard in 1999.

They don’t have roles and role players, etc., nor an explicit notion of subject identity, but where are we with regard to user control over query expansion for example? Or creating new maps with queries? (Was that possible with Robert Barta’s last draft for TMQL?)

Those who do not learn from history are doomed to re-invent it, maybe. (apologies to Santayana)

April 22, 2010

A Missing Step?

Filed under: Information Retrieval,Interface Research/Design,Search Interface,Topic Maps,Usability — Patrick Durusau @ 7:36 am

I happened across a guide to study and writing research papers that I had as an undergraduate. Looking back over it, I noticed there is a step in the research process that is missing from search engines. Perhaps by design, perhaps not.

After choosing a topic, you did research, then in a variety of print resources to gather material for the paper. As you gathered it, you wrote down each piece of information on a note card along with the full bibliographic information for the source.

When you were writing a paper, you did not consult the original sources but rather your sub-set of those sources that were on your note cards.

In group research projects, we exchanged note cards so that everyone had access to the same sub-set of materials that we had found.

Bibliographic software mimics the note card based process but my question is why is that capacity missing from search interfaces?

That seems to be a missing step.  I don’t know if it is missing by design, i.e., it is cheaper to let everyone look for the same information over and over, or if it is missing in anticipation of bibliographic software filling the gap.

Search interfaces need to offer ways for us to preserve and share our research results with others.

Topic maps would be a good way to offer that sort of capability.

April 21, 2010

Interfaces and Topic Maps

Filed under: Interface Research/Design,Search Interface,Searching,Topic Map Software — Patrick Durusau @ 6:02 pm

When I posted the note about Marti Heart’s new book, Search User Interfaces, in Interfaces and Topic Maps I was thinking about it being relevant for software interfaces to topic maps.

After stewing on it for several days and a close read of Chapter 1, I think it has broader application for topic maps.

Topic maps present information about subjects using a single representative for each subject. And those representatives can record properties and associations entered using different identifications.

That sounds like an interface to me. It presents all the considerations of any “interface” in the usual sense of the word. Does it match the intended user’s understanding of the domain? Is the information of interest to the user? Does it help/hinder the user making use of the information?

The Hearst volume is relevant to topic mappers for two reasons:

First, in the conventional sense of the “user interface” to software.

Second, as a guide to exploring how users understand their worlds.

Both are important to keep in mind when constructing topic software as well as topic maps themselves.

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