This process is generally known as information retrieval. You'll probably find this online book helpful.
Existing libraries
Here are two existing solutions that can be fully integrated into an application without requiring a separate process (I believe both will compile with VC++).
Xapian is mature and may do much of what you need, from indexing to ranked retrieval. Separate HTML parsing would be required because, AFAIK, it does not parse html (it has a companion program Omega, which is a front end for indexing web sites).
Lucene is a index/searching Apache library in Java, with an official pre-release C version lucy, and an unofficial C++ version CLucene.
Implementing information retrieval
If the above options are not viable for some reason, here's some info on the individual steps of building and using an index. Custom solutions can go from simple to very sophisticated, depending what you need for your application. I've broken the process into 5 steps
- HTML processing
- Text processing
- Indexing
- Retrieval
- Ranking
HTML Processing
There are two approaches here
Stripping The page you referred to discusses a technique generally known as stripping, which involves removing all the html elements that won't be displayed and translating others to their display form. Personally, I'd preprocess using perl and index the resulting text files. But for an integrated solution, particularly one where you want to record significance tags (e.g. <h1>, <h2>), you probably want to role your own. Here is a partial implementation of a C++ stripping routine (appears in Thinking in C++ , final version of book here), that you could build from.
Parsing A level up in complexity from stripping is html parsing, which would help in your case for recording significance tags. However, a good C++ HTML parser is hard to find. Some options might be htmlcxx (never used it, but active and looks promising) or hubbub (C library, part of NetSurf, but claims to be portable).
If you are dealing with XHTML or are willing to use an HTML-to-XML converter, you can use one of the many available XML parsers. But again, HTML-to-XML converters are hard to find, the only one I know of is HTML Tidy. In addition to conversion to XHTML, its primary purpose is to fix missing/broken tags, and it has an API that could possibly be used to integrate it into an application. Given XHTML documents, there are many good XML parsers, e.g. Xerces-C++ and tinyXML.
Text Processing
For English at least, processing text to words is pretty straight forward. There are a couple of complications when search is involved though.
Stop words are words known a priori not to provide a useful distinction between documents in the set, such as articles and propositions. Often these words are not indexed and filtered from query streams. There are many stop word lists available on the web, such as this one.
Stemming involves preprocessing documents and queries to identify the root of each word to better generalize a search. E.g. searching for "foobarred" should yield "foobarred", "foobarring", and "foobar". The index can be built and searched on roots alone. The two general approaches to stemming are dictionary based (lookups from word ==> root) and algorithm based. The Porter algorithm is very common and several implementations are available, e.g. C++ here or C here. Stemming in the Snowball C library supports several languages.
Soundex encoding One method to make search more robust to spelling errors is to encode words with a phonetic encoding. Then when queries have phonetic errors, they will still map directly to indexed words. There are a lot of implementations around, here's one.
Indexing
The map word ==> page data structure is known as an inverted index. Its inverted because its often generated from a forward index of page ==> words. Inverted indexes generally come in two flavors: inverted file index, which map words to each document they occur in, and full inverted index, which map words to each position in each document they occur in.
The important decision is what backend to use for the index, some possibilities are, in order of ease of implementation:
- SQLite or Berkly DB - both of these are database engines with C++ APIs that integrated into a project without requiring a separate server process. Persistent databases are essentially files, so multiple index sets can be search by just changing the associated file. Using a DBMS as a backend simplifies index creation, updating and searching.
- In memory data structure - if your using a inverted file index that is not prohibitively large (memory consumption and time to load), this could be implemented as a
std::map<std::string,word_data_class>, using boost::serialization for persistence.
- On disk data structure - I've heard of blazingly fast results using memory mapped files for this sort of thing, YMMV. Having an inverted file index would involve having two index files, one representing words with something like
struct {char word[n]; unsigned int offset; unsigned int count; };, and the second representing (word, document) tuples with just unsigned ints (words implicit in the file offset). The offset is the file offset for the first document id for the word in the second file, count is the number of document ids associate with that word (number of ids to read from the second file). Searching would then reduce to a binary search through the first file with a pointer into a memory mapped file. The down side is the need to pad/truncate words to get a constant record size.
The procedure for indexing depends on which backend you use. The classic algorithm for generating a inverted file index (detailed here) begins with reading through each document and extending a list of (page id, word) tuples, ignoring duplicate words in each document. After all documents are processed, sort the list by word, then collapsed into (word, (page id1, page id2, ...)).
The mifluz gnu library implements inverted indexes w/ storage, but without document or query parsing. GPL, so may not be a viable option, but will give you an idea of the complexities involved for an inverted index that supports a large number of documents.
Retrieval
A very common method is boolean retrieval, which is simply the union/intersection of documents indexed for each of the query words that are joined with or/and, respectively. These operations are efficient if the document ids are stored in sorted order for each term, so that algorithms like std::set_union or std::set_intersection can be applied directly.
There are variations on retrieval, wikipedia has an overview, but standard boolean is good for many/most application.
Ranking
There are many methods for ranking the documents returned by boolean retrieval. Common methods are based on the bag of words model, which just means that the relative position of words is ignored. The general approach is to score each retrieved document relative to the query, and rank documents based on their calculated score. There are many scoring methods, but a good starting place is the term frequency-inverse document frequency formula.
The idea behind this formula is that if a query word occurs frequently in a document, that document should score higher, but a word that occurs in many documents is less informative so this word should be down weighted. The formula is, over query terms i=1..N and document j
score[j] = sum_over_i(word_freq[i,j] * inv_doc_freq[i])
where the word_freq[i,j] is the number of occurrences of word i in document j, and
inv_doc_freq[i] = log(M/doc_freq[i])
where M is the number of documents and doc_freq[i] is the number of documents containing word i. Notice that words that occur in all documents will not contribute to the score. A more complex scoring model that is widely used is BM25, which is included in both Lucene and Xapian.
Often, effective ranking for a particular domain is obtained by adjusting by trial and error. A starting place for adjusting rankings by heading/paragraph context could be inflating word_freq for a word based on heading/paragraph context, e.g. 1 for a paragraph, 10 for a top level heading. For some other ideas, you might find this paper interesting, where the authors adjusted BM25 ranking for positional scoring (the idea being that words closer to the beginning of the document are more relevant than words toward the end).
Objective quantification of ranking performance is obtained by precision-recall curves or mean average precision, detailed here. Evaluation requires an ideal set of queries paired with all the relevant documents in the set.