+ TF/IDF (Term Frequecy over Inverse Document Frequency) like
+ algorithm. In contrast to the usual defintion of TF/IDF
+ algorithms, which only considers searching in one full-text
+ index, this one works on multiple indexes at the same time.
+ More precisely,
+ Zebra does boolean queries and searches in specific addressed
+ indexes (there are inverted indexes pointing from terms in the
+ dictionary to documents and term positions inside documents).
+ It works like this:
+ <variablelist>
+ <varlistentry>
+ <term>Query Components</term>
+ <listitem>
+ <para>
+ First, the boolean query is dismantled into it's principal components,
+ i.e. atomic queries where one term is looked up in one index.
+ For example, the query
+ <screen>
+ @attr 2=102 @and @attr 1=1010 Utah @attr 1=1018 Springer
+ </screen>
+ is a boolean AND between the atomic parts
+ <screen>
+ @attr 2=102 @attr 1=1010 Utah
+ </screen>
+ and
+ <screen>
+ @attr 2=102 @attr 1=1018 Springer
+ </screen>
+ which gets processed each for itself.
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term>Atomic hit lists</term>
+ <listitem>
+ <para>
+ Second, for each atomic query, the hit list of documents is
+ computed.
+ </para>
+ <para>
+ In this example, two hit lists for each index
+ <literal>@attr 1=1010</literal> and
+ <literal>@attr 1=1018</literal> are computed.
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term>Atomic scores</term>
+ <listitem>
+ <para>
+ Third, each document in the hit list is assigned a score (_if_ ranking
+ is enabled and requested in the query) using a TF/IDF scheme.
+ </para>
+ <para>
+ In this example, both atomic parts of the query assign the magic
+ <literal>@attr 2=102</literal> relevance attribute, and are
+ to be used in the relevance ranking functions.
+ </para>
+ <para>
+ It is possible to apply dynamic ranking on only parts of the
+ PQF query:
+ <screen>
+ @and @attr 2=102 @attr 1=1010 Utah @attr 1=1018 Springer
+ </screen>
+ searches for all documents which have the term 'Utah' on the
+ body of text, and which have the term 'Springer' in the publisher
+ field, and sort them in the order of the relevance ranking made on
+ the body-of-text index only.
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term>Hit list merging</term>
+ <listitem>
+ <para>
+ Fourth, the atomic hit lists are merged according to the boolean
+ conditions to a final hit list of documents to be returned.
+ </para>
+ <para>
+ This step is always performed, independently of the fact that
+ dynamic ranking is enabled or not.
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term>Document score computation</term>
+ <listitem>
+ <para>
+ Fifth, the total score of a document is computed as a linear
+ combination of the atomic scores of the atomic hit lists
+ </para>
+ <para>
+ Ranking weights may be used to pass a value to a ranking
+ algorithm, using the non-standard BIB-1 attribute type 9.
+ This allows one branch of a query to use one value while
+ another branch uses a different one. For example, we can search
+ for <literal>utah</literal> in the
+ <literal>@attr 1=4</literal> index with weight 30, as
+ well as in the <literal>@attr 1=1010</literal> index with weight 20:
+ <screen>
+ @attr 2=102 @or @attr 9=30 @attr 1=4 utah @attr 9=20 @attr 1=1010 city
+ </screen>
+ </para>
+ <para>
+ The default weight is
+ sqrt(1000) ~ 34 , as the Z39.50 standard prescribes that the top score
+ is 1000 and the bottom score is 0, encoded in integers.
+ </para>
+ <warning>
+ <para>
+ The ranking-weight feature is experimental. It may change in future
+ releases of zebra.
+ </para>
+ </warning>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term>Re-sorting of hit list</term>
+ <listitem>
+ <para>
+ Finally, the final hit list is re-ordered according to scores.
+ </para>
+ </listitem>
+ </varlistentry>
+ </variablelist>
+
+
+<!--
+Still need to describe the exact TF/IDF formula. Here's the info, need -->
+<!--to extract it in human readable form .. MC
+
+static int calc (void *set_handle, zint sysno, zint staticrank,
+ int *stop_flag)
+{
+ int i, lo, divisor, score = 0;
+ struct rank_set_info *si = (struct rank_set_info *) set_handle;
+
+ if (!si->no_rank_entries)
+ return -1; /* ranking not enabled for any terms */
+
+ for (i = 0; i < si->no_entries; i++)
+ {
+ yaz_log(log_level, "calc: i=%d rank_flag=%d lo=%d",
+ i, si->entries[i].rank_flag, si->entries[i].local_occur);
+ if (si->entries[i].rank_flag && (lo = si->entries[i].local_occur))
+ score += (8+log2_int (lo)) * si->entries[i].global_inv *
+ si->entries[i].rank_weight;
+ }
+ divisor = si->no_rank_entries * (8+log2_int (si->last_pos/si->no_entries));
+ score = score / divisor;
+ yaz_log(log_level, "calc sysno=" ZINT_FORMAT " score=%d", sysno, score);
+ if (score > 1000)
+ score = 1000;
+ /* reset the counts for the next term */
+ for (i = 0; i < si->no_entries; i++)
+ si->entries[i].local_occur = 0;
+ return score;
+}
+
+
+where lo = si->entries[i].local_occur is the local documents term-within-index frequency, si->entries[i].global_inv represents the IDF part (computed in static void *begin()), and
+si->entries[i].rank_weight is the weight assigner per index (default 34, or set in the @attr 9=xyz magic)
+
+Finally, the IDF part is computed as:
+
+static void *begin (struct zebra_register *reg,
+ void *class_handle, RSET rset, NMEM nmem,
+ TERMID *terms, int numterms)
+{
+ struct rank_set_info *si =
+ (struct rank_set_info *) nmem_malloc (nmem,sizeof(*si));
+ int i;
+
+ yaz_log(log_level, "rank-1 begin");
+ si->no_entries = numterms;
+ si->no_rank_entries = 0;
+ si->nmem=nmem;
+ si->entries = (struct rank_term_info *)
+ nmem_malloc (si->nmem, sizeof(*si->entries)*numterms);
+ for (i = 0; i < numterms; i++)
+ {
+ zint g = rset_count(terms[i]->rset);
+ yaz_log(log_level, "i=%d flags=%s '%s'", i,
+ terms[i]->flags, terms[i]->name );
+ if (!strncmp (terms[i]->flags, "rank,", 5))
+ {
+ const char *cp = strstr(terms[i]->flags+4, ",w=");
+ si->entries[i].rank_flag = 1;
+ if (cp)
+ si->entries[i].rank_weight = atoi (cp+3);
+ else
+ si->entries[i].rank_weight = 34; /* sqrroot of 1000 */
+ yaz_log(log_level, " i=%d weight=%d g="ZINT_FORMAT, i,
+ si->entries[i].rank_weight, g);
+ (si->no_rank_entries)++;
+ }
+ else
+ si->entries[i].rank_flag = 0;
+ si->entries[i].local_occur = 0; /* FIXME */
+ si->entries[i].global_occur = g;
+ si->entries[i].global_inv = 32 - log2_int (g);
+ yaz_log(log_level, " global_inv = %d g = " ZINT_FORMAT,
+ (int) (32-log2_int (g)), g);
+ si->entries[i].term = terms[i];
+ si->entries[i].term_index=i;
+ terms[i]->rankpriv = &(si->entries[i]);
+ }
+ return si;
+}
+
+
+where g = rset_count(terms[i]->rset) is the count of all documents in this specific index hit list, and the IDF part then is
+
+ si->entries[i].global_inv = 32 - log2_int (g);
+ -->
+
projects / idzebra-moved-to-github.git / blobdiff