Search engines use software robots to survey the Web and build their databases. Web documents are retrieved and indexed. When you enter a query at a search engine website, your input is checked against the search engine's keyword indices. The best matches are then returned to you as hits.

There are two primary methods of text searching--keyword and concept. Keyword searching is far more common. Determining the concept behind a particular web page continues to be a challenge for search engine companies.

Keyword Searching

This is the typical form of text search on the Web. Most search engines do their text query and retrieval using keywords.

Unless the author of the Web document specifies the keywords for her document (this is possible by using meta tags), it's up to the search engine to determine them. Essentially, this means that search engines pull out and index words that are believed to be significant. Words that are mentioned towards the top of a document and words that are repeated several times throughout the document are more likely to be deemed important.

Most search engines now index every word on every page. Others index only part of the document, such as the title, headings, subheadings, hyperlinks to other sites, and the first 20 lines of text.

Full-text indexing system generally pick up every word in the text except commonly occurring stop words such as "a," "an," "the," "is," "and," "or," and "www." AltaVista claims to index all words, even the articles, "a," "an," and "the." Some of the search engines discriminate upper case from lower case; others store all words without reference to capitalization.

The Problem With Keyword Searching

Keyword searches have a tough time distinguishing between words that are spelled the same way, but mean something different (i.e. hard cider, a hard stone, a hard exam, and the hard drive on your computer). This often results in hits that are completely irrelevant to your query. Some search engines also have trouble with so-called stemming--i.e., if you enter the word "big," should they return a hit on the word, "bigger?" What about singular and plural words? What about verb tenses that differ from the word you entered by only an "s," or an "ed"?

Search engines also cannot return hits on keywords that mean the same, but are not actually entered in your query. A query on heart disease would not return a document that used the word "cardiac" instead of "heart."

Concept-based searching

Unlike keyword search systems, concept-based search systems try to determine what you mean, not just what you say. In the best circumstances, a concept-based search returns hits on documents that are "about" the subject/theme you're exploring, even if the words in the document don't precisely match the words you enter into the query.

Excite is currently the best-known general-purpose search engine site on the Web that relies on concept-based searching.

This is also known as clustering -- which essentially means that words are examined in relation to other words found nearby.

How does it work?

There are various methods of building clustering systems, some of which are highly complex, relying on sophisticated linguistic and artificial intelligence theory that we won't even attempt to go into here. Excite sticks to a numerical approach. Excite's software determines meaning by calculating the frequency with which certain important words appear. When several words or phrases that are tagged to signal a particular concept appear close to each other in a text, the search engine concludes, by statistical analysis, that the piece is "about" a certain subject.

For example, the word heart, when used in the medical/health context, would be likely to appear with such words as coronary, artery, lung, stroke, cholesterol, pump, blood, attack, and arteriosclerosis. If the word heart appears in a document with others words such as flowers, candy, love, passion, and valentine, a very different context is established, and the search engine returns hits on the subject of romance.

Warning: This often works better in theory than in practice. Concept-based indexing is a good idea, but it's far from perfect. The results are best when you enter a lot of words, all of which roughly refer to the concept you're seeking information about.

Refining Your Search

Most sites offer two different types of searches--"basic" and "advanced." In a "basic" search, you just enter a keyword without sifting through any pulldown menus of additional options. Depending on the engine, though, "basic" searches can be quite complex.

Advanced search refining options differ from one search engine to another, but some of the possibilities include the ability to search on more than one word, to give more weight to one search term than you give to another, and to exclude words that might be likely to muddy the results. You might also be able to search on proper names, on phrases, and on words that are found within a certain proximity to other search terms.

Many search engines now automatically recognize company names and can direct a searcher to a corporate website when such a name is entered as a query. Phrase recognition is also becoming more common; i.e., you might expect to get relevant hits for the term Cold War if you enter it without the quotation marks that typically denote a phrase. (In the past, you simply would have received all documents with the words "cold" and "war" in them.

Some search engines also allow you to specify what form you'd like your results to appear in, and whether you wish to restrict your search to certain fields on the internet (i.e., Usenet or the Web) or to specific parts of Web documents (i.e., the title or URL).

Many, but not all search engines allow you to use so-called Boolean operators to refine your search. These are the logical terms AND, OR, NOT, and the so-called proximal locators, NEAR and FOLLOWED BY.

Boolean AND means that all the terms you specify must appear in the documents, i.e., "heart" AND "attack." You might use this if you wanted to exclude common hits that would be irrelevant to your query.

Boolean OR means that at least one of the terms you specify must appear in the documents, i.e., bronchitis, acute OR chronic. You might use this if you didn't want to rule out too much.

Boolean NOT means that at least one of the terms you specify must not appear in the documents. You might use this if you anticipated results that would be totally off-base, i.e., nirvana AND Buddhism, NOT Cobain.

Not quite Boolean + and - Some search engines use the characters + and - instead of Boolean operators to include and exclude terms.

NEAR means that the terms you enter should be within a certain number of words of each other. FOLLOWED BY means that one term must directly follow the other. ADJ, for adjacent, serves the same function. A search engine that will allow you to search on phrases uses, essentially, the same method (i.e., determining adjacency of keywords).

Phrases: The ability to query on phrases is very important in a search engine. Those that allow it usually require that you enclose the phrase in quotation marks, i.e., "space the final frontier."

Capitalization: This is essential for searching on proper names of people, companies or products. Unfortunately, many words in English are used both as proper and common nouns--Bill, bill, Gates, gates, Oracle, oracle, Lotus, lotus, Digital, digital--the list is endless.

All the search engines have different methods of refining queries. The best way to learn them is to read the help files on the search engine sites and practice!

Here are some links to the help files that Spidap finds most useful:

• AltaVista Advanced Search Help

Relevancy Rankings

Most of the search engines return results with confidence or relevancy rankings. In other words, they list the hits according to how closely they think the results match the query. However, these lists often leave users shaking their heads on confusion, since, to the user, the results might not relevant.

Why does this happen? Basically it's because search engine technology has not yet reached the point where humans and computers understand each other well enough to communicate clearly.

Most search engines use search term frequency as a primary way of determining whether a document is relevant. If you're researching diabetes and the word "diabetes" appears multiple times in a Web document, it's reasonable to assume that the document will contain useful information. Therefore, a document that repeats the word "diabetes" over and over is likely to turn up near the top of your list.

If your keyword is a common one, or if it has multiple other meanings, you could end up with a lot of irrelevant hits. And if your keyword is a subject about which you desire information, you don't need to see it repeated over and over--it's the information about that word that you're interested in, not the word itself.

Some search engines consider both the frequency and the positioning of keywords to determine relevancy, reasoning that if the keywords appear early in the document, or in the headers, this increases the likelihood that the document is on target. For example, Lycos ranks hits according to how many times your keywords appear in their indices of the document and in which fields they appear (i.e., in headers, titles or text). It also takes into consideration whether the documents that emerge as hits are frequently linked to other documents on the Web, reasoning that if other folks consider them important, you should, too.

If you use the advanced query form on AltaVista, you can assign relevance weights to your query terms before conducting a search. Although this takes some practice, it essentially allows you to have a stronger say in what results you will get back.

As far as the user is concerned, relevancy ranking is critical, and becomes more so as the sheer volume of information on the Web grows. Most of us don't have the time to sift through scores of hits to determine which hyperlinks we should actually explore. The more clearly relevant the results are, the more we're likely to value the search engine.