How To Use A Search Engine

How to... use search engines effectively

By Margaret Adolphus

The perfect search engine does non exist. Not only is information increasing exponentially, but search behaviour is becoming always more enervating. And so, at the point when theoretical perfection is achieved, another layer of information becomes bachelor, and people find new ways to search.

This is good news for the developers of search engines, specially for the behemoth Google, which controls 78 per cent of the market.

But for the residuum of united states, it's as hard to proceed abreast of developments in search engines as it is those in Web 2.0 applications. This commodity is an attempt to summarize some recent trends.

What do the experts recollect?

Given their importance in the market, it seems advisable to start with Google. Speaking in December 2009, Matt Cutts predicted a number of trends (Skipease, 2009):

• Segmentation of search – Google would try and categorize information more, for example Google Book Search, (US) government search, blog search, etc.
• Semantic Web – Google search engine is condign more sophisticated, taking account of synonyms, page structure and user intent.
• Searching the cloud – as people become more confident to shop information on "cloud" hard drives, there will be a need to search these.
• Existent-time – searching what people are writing at the moment to take hold of the latest buzz and become really up to the minute data.
• Mobile search – as nosotros use mobiles for data, we volition need search tools to search them, so mobile websites will need to exist formatted for searchability.

Writing from the perspective of the tertiary quarter of 2010, these trends appear spot on, however, they fail to mention a primal business picked up past ii data professional commentators: the demand to organize information.

Data consultant Ellyssa Kroski wants search engines to reduce information overload:

"To admission the vast content stores of the read/write Web, these search tools make use of structured and linked data, real-time search, personalization, and more focused filtering techniques. If you're a fan of buzzwords, y'all might say nosotros've entered Web 3.0, a new era that is motivated by the need to more effectively organize, filter, and access information online" (Kroski, 2009).

And Phil Bradley, listening to someone else's vision of a perfect search engine, muses that his vision is of a tool that would filter, sieve and collate information rather than only present it (Bradley, 2010).

And then, what are the chief trends, and practice they brand information technology easier to find information?

Real-time and social search

By now, social search engines – which search across the social Web – are well established. People search engines are a particularly interesting evolution especially for potential recruiters or those involved in relationship direction.

Two useful people search sites are 123people and kgbpeople. Both sites give a large amount of data:

• social network sites,
• spider web pages,
• documents,
• blog mentions,
• photos.

123people organizes things so that all links announced on one folio, whereas kgbpeople has a tabbed structure with tabs at the peak of the page linking to social networks, search engines (where results are shown against the individual search engine), photos/video/audio, and personal.

No one doing a serious search tin avoid the blogosphere, and there are a number of ways of searching blogs. Google provides the option of limiting search results past media blazon (including blogs – see carte in top left paw corner).

Boardreader searches forums, and Icerocket searches over the Web, the blogosphere, Twitter, MySpace, news, images and BigBuzz, with blogs the default pick. It received the thumbs upwards from Phil Bradley (Bradley, 2009), who commends it for its value in providing a quick overview of social media, pulling everything into one place.

However, the almost exciting development with search engines is the ability to search in "existent time", i.due east. the present moment, so that yous can observe out what people are talking about now.

What distinguishes a real-time search engine is that information technology continues to search after results are revealed, so that items continue to drop into your results page. Examples include Twazzup, Scoopler (now defunct), and Collecta.

Twitter is a particularly good fashion of searching for upward to the minute content and at that place are a number of Twitter search engines. Twitter Search works past instant indexing: whereas tradition search engines search archived content, Twitter enters updates into its database as shortly equally they are tweeted. It also has some useful advanced search options: y'all tin search within a engagement range, to and from a particular person, and specifically for links.

The just drawback to Twitter Search is that it only searches within Twitter's time range. Snapbird, withal, enables you to search across Twitter's ten-day history, or in detail friends' accounts. There are many other Twitter search engines: see the article "l+ ways to search Twitter" (Peters, 2010).

Semantic and computational search

Semantic search means that the software does not crawl randomly through its index of web pages searching for the input term, merely rather queries the item against its own structured information. In other words, there is some intelligence in the search: data is organized in a structured style, by humans, confronting the software'southward metadata.

Such an approach is exemplified by Wolfram|Alpha (WA), launched in May 2009. WA describes itself as a "computational knowledge engine" and works differently from standard search engines. It checks every search query against a database of facts which have been compiled past its team, basing its respond on algorithms.

The long-term aim is to brand all cognition computable and attainable to everyone. Co-ordinate to its website,

"Our goal is to build on the achievements of science and other systematizations of knowledge to provide a single source that can be relied on by everyone for definitive answers to factual queries" (Wolfram|Alpha, 2010).

The idea is to save users time in two main ways:

1. It displays the resulting information cleanly within the interface on the page, so there is no demand to click in and out of results pages. Thus while searching "France" in Google would bring up references to Wikipedia, French hotels, etc., an AW search brings up a whole range of facts most the country, including maps, statistics, economic indicators, etc.
2. It provides answers, not sources of answers. If you want, for example, to catechumen $30,000 into UK sterling, it will display the answer rather than directing you to currency converting sites, and helpfully as well provide a graph showing commutation history.

WA's main drawback is the size of its database: at 10 terabytes last October (Higgins, 2009), it is smaller than Google. According to its website (Wolfram|Blastoff, 2010) it holds 10+ trillion pieces of data and 50,000+ types of algorithms and models. At that place are still significant gaps, yet, and WA would be the outset to admit that the site has a long way to go.

From a reference librarian's signal of view, it is a good place to search for basic facts, for example nearly a country. It is also particularly stiff on scientific and mathematical information.

Figure ane. The Wolfram|Alpha search engine, showing the results of a query for "silver, gold', which provides comparative information for the two elements (© Wolfram|Alpha)

While WA undoubtedly leads the style in computational search, information technology is not lone, specially with regard to use of underlying factual databases.

Microsoft's Bing was as well launched in May 2009, and similar WA, claims to be able provide direct answers to questions. Bing finds these answers from ii underlying databases that Microsoft took over: one relating to travel and shopping, and the other, the semantic-based Powersearch, which indexes Wikipedia.

Bing describes itself as a "decision engine", helping users brand central decisions and providing instant answers. For example, a search "London to Johannesburg" brings upwardly a list of sites providing flight information.

The search engine Inquire (Ask Jeeves in Britain) has long relied on a database of questions and answers, and was recently relaunched equally a natural language search engine, which tin generate results both automatically and based on a human being edited database of responses.

And Google, claims Matt Cutts (Skipease, 2009), is becoming increasingly sophisticated and semantically empowered: it can factor in synonyms, phrase structure and user intent.

Not all, however, favour these new database search methods. Pandia Search Engine News points out that in that location is a flaw in thinking that sites like Wolfram|Alpha and Ask can save fourth dimension by answering the user's questions. Non all questions accept ane respond, and information may best be gleaned by going to a number of sources (Pandia, 2010). This is particularly so for very recent information, or where narrative information as in a news story, or subjective information (as in reviews of a hotel) is required.

Organizing data

The beingness of more and more than search engines, as well as more than information, makes searching more than time-consuming. That is why the efforts of search engines to help researchers organize data are welcome.

Displaying results

I of the most irritating things about search is time spent going back and along from the list of the results to the actual pages, particularly when these don't have what you are looking for. A number of sites help surmount this problem, either by providing more information or by presenting it in a structured style.

Bing, for example, allows y'all to scroll your mouse over the edge of the entries, revealing "more than on the page" from that particular site.

Figure 2. Bing's interface, showing how it displays results

Bing also gives suggested related searches in a column to the left (not shown higher up). Some other useful feature is the power to view video thumbnails within the search results in the video pick.

Google squared (GS) provides search results in a filigree structure, which is good for searching categories of items. Search for dog breeds, for example, and GS comes up with a list of breeds against images, information on life expectancy, size and country of origin, and it is also possible to add items. Search for a single, but complex, item, such as a state, it helps you structure the results by suggesting categories.

Some other intriguing Google application, all the same in the lab, is Google News Timeline, which enables you to view the news chronologically, according to twenty-four hours and time, with a grid structure so you can see how a particular story is developing.

Figure 3. Results for the Australian full general election 2010, every bit seen in Google News Timeline

The news site www.newsmap.jp offers another visual view of the news, colour coding stories co-ordinate to earth, national, business organization, engineering, sport, entertainment, and health.

Metasearch engines

Metasearch engines, or MSEs, which save time by allowing you lot to search over several sites, are inappreciably new. Withal, the ability to search several together – specially if you lot can have a say in what search engines yous search – becomes more important with more search engines around.

Useful research into which MSE to employ is provided by Sadeghi (2009), who compares the effectiveness of a number of metasearch engines: Cluster, Dogpile, Excite, Mamma, MetaCrawler, Search.com, Webcrawler and Webfetch. Sadeghi evaluated these tools by measuring the average closeness of the ranked results with the underlying search engines, using a number of queries, and then comparison the MSEs with one another to run across which gave the all-time outcome. The findings revealed that Dogpile performed all-time.

Searching beyond the surface – the deep Web and federated search

Traditional search engines but skim a very small portion of the Web. The Web is a bit like an iceberg: the bit yous see is modest, only in that location is a big amount below the surface. It has been estimated that the office that is invisible to ordinary search engines is as high every bit 90 per cent.

This is because traditional search engine technology relies on web crawlers (also referred to as "spiders" or "robots"), which explore the Web by following links. Nevertheless, some links, particularly of databases, are nigh dead ends because you need to enter a search term on the front folio of the database.

The consequence is that any search which requires some bookish noesis or serious research volition be difficult, because such knowledge tends to be stored in PDF documents in databases. Search engines have tried to address this trouble by persuading the database owners to adapt their requirements, with varying results.

Nevertheless, the about significant technological advance in the search of databases is federated search. Federated search allows the user to search multiple databases simultaneously, rather in the same fashion that MSEs practise for search engines. The information compages, however, is totally dissimilar.

When the user inputs a search query, the query is fanned out by means of a number of software connectors, which crusade the search to be re-executed in other places. The results are sent back to the original federated search engine's server, and then presented to the user, which might be relevance ranked.

Federated search is not, even so, without its bug, chief of which is the toll of the software "connectors", although as publishers prefer mutual standards this will be less of an issue. Another problem is data overload, with too many results, and the need to invent a new engineering science for relevance ranking equally that for Google does not work (Warnick, 2010).

What is potentially more than serious, however, is contempo research which indicates that people are bypassing "advisedly-crafted discovery systems" (Ciber Report, 2009, quoted in Joint, 2010) to find simpler search solutions.

According to University Higher London's Heart for Information Behaviour and Evaluation of Research (CIBER), a mere four months after Science Direct content was opened up to Google, a third of the traffic to the quondam'southward physics journals came by that route.

It is difficult to avoid the determination from this inquiry that people are wanting a one-terminate-shop solution, and if Google can adjust this, what more can federated search offer? Certainly the latter's time to come will depend on conscientious user behaviour research, and consequent development of features that give the user the experience he or she is looking for.

One resource which has get popular for its ability to simplify search is Summon, launched in 2009 by Serials Solutions. Summon attempts to replicate the simplicity of a Google web search while providing admission to library and other high quality resources.

Summon's technology architecture is powered non by federated search, merely past a massive single index that pre-harvests content from 94,000 journals and 6,800 content providers. It tin can deliver relevance-ranked, media neutral results in less than a second.

Ane university (One thousand Valley State University, Michigan) found that students' employ of library resources increased considerably after implementation of Summon. Some other (the Academy of Michigan) analysed "personas" (personality profiles of typical users) then surveyed the community, deciding that the Summon service best fitted their needs.

Figure four. Screenshots showing Grand Valley State Academy's version of Summon (© One thousand Valley State Academy)

Many academic and enquiry search tools still utilise federated technology, withal. For case, WorldWideScience.org is a huge global science gateway which can search beyond 400 million databases in 65 different countries. Information technology was launched in 2007 by the British Library and the US Department of Free energy's Part of Scientific and Technical Data (OSTI).

WorldWideScience.org gets over the "too much data" problem past providing a left-hand console which clusters the results by topic, author, publication and engagement.

Figure v. WorldWideScience.org's search interface

Some other of OSTI's products, the E-print Network, relies on a combination of alphabetize crawling and federated search. The two technologies run in parallel, with some filtering of sites that do not meet the required quality, which makes E-print Network a very loftier quality tool. It's an approach which Warnick (2010) suggests may be the future of quality search products.

1 of the problems with searching databases is that near quality search tools are only found in academic libraries. One solution to the problem of the independent researcher is offered by DeepDyve, which provides admission to database items which yous can then rent for a brusque while, thus avoiding the college buy price.

Not employing federated search, only in Web 2.0 fashion depending on the good will of users, is the Deep Web Wiki. Hither volunteers contribute and describe useful sites which may not be popular enough to be included in search engines, as well every bit databases.

One of the remaining limitations in federated search is linguistic communication: tools may be limited to searching databases with English titles and abstracts. However, in June 2010, Multilingual WorldWideScience was launched at the International Council for Scientific and Technical Information (ICSTI) annual conference in Helsinki.

The software uses existent-time translation to offer multi-lingual search. A query tin can be typed in 1 language, and and then translated into the linguistic communication of the database; similarly, results can be translated back into the language of the searcher. Now real-time searching and translating is possible into English, Chinese, Japanese, Korean, French, German, Spanish, Portuguese and Russian.

Other search trends

There are a number of other trends in search engine engineering science, notably segmentation, personalization and custom search.

Search segmentation

Many search engines confine themselves to a particular type of communication, or media. We take already seen examples of search engines that specialize in people, blogs, "real-time", Twitter etc.; other search engines search PDFs and eastward-books, audio and music, and video and movies.

When reporting on Google images' revamp, Phil Bradley listed a number of other sites devoted to images (Bradley, 2010b),:

• Nachfoto for real-time image search.
• Coloralo for cartoons and colouring pages.
• Panoramio for geobased images.
• Seeklogo for logos.
• Tag Milky way which allows you to search images past tag, and which has a delightful interface showing a number of planets circling round a sun.
• flickrCC for creative commons images at Flickr.

For a list of search engines for different media, run across "100+ Culling Search Engines You Should Know" which, equally its proper noun suggests, provides names of lots of unlike search engines, organized past category.

And Bing, points out library guru Mary Ellen Bates, has as i of its advanced search options the facility to search for a detail file format in a page. Use the syntax "contains: files_type" and you can discover pages with the subject of your search in a particular format (Bates 2010).

Personalized and custom search

Personalized search, the ability of a search engine to answer to queries on the ground of user search behaviour, and their profile if they have one, has been going since effectually 2007 (Koch, 2007).

Custom search too offers the possibility, via human intervention at the user end, of creating a search engine with pre-selected resources. Librarians at Western Oregon Library have been using Google Custom Search Engine to create research guides effectually detail subjects, which they have found particularly useful, equally many of their students are non-traditional returners to education who are easily baffled past the vast amounts of information on the Web (Monge and Forbes, 2009).

Summary

In this article, we have looked at social, real-time, semantic, multi-, federated, segmented and personalized search, all of which are trends in the current search scene.

One strong overarching trend is the perceived need for human intervention in search. Web crawlers on their own are non sufficient; there is a need for some sort of arrangement of knowledge whether in databases of research papers, or of facts which can exist queried against an algorithm. It may be that the future lies in a combination of database blazon searches with more than random crawling of the Web.

Another is the realization that no one search engine can adjust all search requirements. While Google is unlikely to lose market share, serious searchers will still be selective and expect to dissimilar search engines for different requirements.

Personally, I will continue to use 123people for people searches, Collecta for web log searches, Wolfram|Alpha for facts almost a country, Deep Dyve or Deep Spider web Wiki for more serious bookish searches, and Google or Dogpile for more than general searches. Now as never before, it is important to be familiar with the different search engines and their capabilities.

References

Bates, K.E. (2010), Bates Info Tip "Bing gets smart", email sent 7 July 2010.

Bradley, P (2009), "Take fun filling in the blanks", Internet Q&A, Library and Data Update, March 2009.

Bradley, P. (2010a), "Google Open up House report", http://philbradley.typepad.com/phil_bradleys_weblog/2010/07/google-open…, accessed eleven July 2010.

Koch, P. and Koch, S. (2007), "The search engine scene in 2015", Pandia Search Engine News, available at http://world wide web.pandia.com/sew/353-search-2015.html, accessed eight August 2010.

Kroski, Due east. (2009), "Search engine wars redux | Stacking the tech", Library Periodical,http://www.libraryjournal.com/article/CA6669698.html?&rid=1105906703&and so…, accessed eleven August 2010.

Monge, R. and Forbes, C. (2009), "Google custom search engine and library instruction", presentation to Cyberspace Librarian International, fifteen-sixteen October 2009, London, United kingdom of great britain and northern ireland, bachelor at http://conferences.infotoday.com/documents/82/B202_Forbes.pdf, accessed xi Baronial 2010.

Pandia (2010), "A soft spot for the Ask search engine", Pandia Search Engine News, bachelor at http://world wide web.pandia.com/sew/3059-a-soft-spot-for-the-ask-search-engine.ht…, accessed nineteen August 2010.

Peters, J. (2010), "50+ ways to search Twitter", Social Media Today, Apr fifteen, 2010, available at http://socialmediatoday.com/SMC/189327, accessed 2010-08-11 13:34:52

Segradi, H. (2009), "Assessing metasearch engine performance", Online Information Review, Vol. 33 No. half dozen.

Skipease (2009), "Google's Matt Cutts discusses search engine trends for 2010" available at http://world wide web.skipease.com/blog/google-news-tips/google-search-engine-tren…, accessed viii August 2010.

Warnick, W. (2010), "Federated search as a transformational engineering enabling cognition discovery: the role of WorldWideScience.org", Interlending & Document Supply, Vol. 38 No. 2.

Wolfram|Alpha (2010), "About Wolfram|Blastoff", available at http://www.wolframalpha.com/most.html.

How To Use A Search Engine,

Source: https://www.emeraldgrouppublishing.com/archived/research/guides/management/new_search_engines.htm

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