Mimi nina kazi ya hati ya mapitio ya usanifu wiki hii na inapendekeza, miongoni mwa mambo mengine, that the client consider using the thesaurus to help improve the end user search experience. Having never done this myself, I wanted to do a quick hands-on test so that my suggestion is authentic.

Ilikuwa ni ya kushangaza vigumu kufikiri jinsi ya kufanya, ingawa ni, kwa kweli, quite easy. There’s a pretty good bit of information on the thesaurus (kuangalia hapa na hapa, kwa mfano). Hata hivyo, wale maandiko ni aidha WSS 2.0 / SPS 2003 oriented or they don’t actually spell out what do to after you’ve made your changes in the thesaurus. They provide a great overview and fair bit of detail, lakini siyo ya kutosha kuvuka mstari kumaliza.

Hatua hizi kazi kwa ajili yangu:

1. Make the changes to the thesaurus. (Angalia hapa chini kwa maelezo muhimu)
2. Go to the server and restart the "Office SharePoint Server Search" huduma.

ncha ya kofia Mr. J. D. Wade (bio). He provided the key bit about restarting the search service and rescued me from endless, time consuming and unnecessary iisresets and full index crawls. This episode inathibitisha, mara nyingine tena, kwamba Twitter is the awesome. (Kufuata yangu juu ya Twitter hapa. I follow any SharePoint person that follows me).

I don’t know if this functionality is available in WSS. If it is or is not, tafadhali kuacha maoni au email yangu na mimi itabidi update hii post.

Muhimu kumbuka: There’s conflicting information on which XML thesaurus file to change. There’s this notion of "tsneu.xml" as being the "neutral" Thesaurus. I wasted some time working with that one. Katika kesi yangu, I needed to change the "tsenu.xml" ziko chini ya faili folder ya ID programu yenyewe: \\win2003srv c $ Mpango Files Ofisi ya Microsoft Servers 12.0 Takwimu Ofisi Server Maombi 3c4d509a-75c5-481c-8bfd-099a89554e17\Config. I assume that in a multi-farm situation, ungependa kufanya mabadiliko haya kila mahali server swala anaendesha.

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Nimekuwa kumaliza hadi siku 2 ya mafunzo FAST katika jua Needham, MA, na mimi nina kupasuka kwa mawazo (ambayo mema yote ya mafunzo ya madarasa kufanya kwangu). One particular aspect of FAST has me thinking and I wanted to write it down while it was still fresh and normal day-to-day "stuff" pushed it out of my head.

We SharePoint WSS 3.0 / MOSS implementers frequently face a tough problem with any reasonably-sized SharePoint project: How do we get all the untagged data loaded into SharePoint such that it all fits within our perfectly designed information architecture?

Often enough, this isn’t such a hard problem because we scope ourselves out of trouble: "We don’t care about anything more than 3 months old." "We’ll handle all that old stuff with keyword search and going-forward we’ll do it the RIGHT way…" Etc.

Lakini, what happens if we can’t scope ourselves out of trouble and we’re looking at 10’s of thousands or 100’s of thousands (or even millions) of docs — the loading na tagging of which is our devout wish?

FAST might be the answer.

FAST’s search process includes a lot of moving parts but one simplified view is this:

• A crawler process looks for content.
• It finds content and hands it off to a broker process that manages a pool of document processors.
• Broker process hands it off to one of the document processors.
• The document processor analyzes the document and via a pipeline process, analyzes the bejeezus out of the document and hands it off to an index builder type process.

On the starship FAST, we have a lot of control over the document processing pipeline. We can mix and match about 100 pipeline components and, most interestingly, we can write our own components. Like I say, FAST is analyzing documents every which way but Sunday and it compiles a lot of useful information about those documents. Those crazy FAST people are clearly insane and obsessive about document analysis because they have tools and/or strategies to REALLY categorize documents.

Hivyo … using FAST in combination with our own custom pipeline component, we can grab all that context information from FAST and feed it back to MOSS. It might go something like this:

• Document is fed into FAST from MOSS.
• Normal crazy-obsessive FAST document parsing and categorization happens.
• Our own custom pipeline component drops some of that context information off to a database.
• A process of our own design reads the context information, makes some decisions on how to fit that MOSS document within our IA and marks it up using a web service and the object model.

Bila shaka, no such automated process can be perfect but thanks to the obsessive (and possibly insane-but-in-a-good-way FAST people), we may have a real fighting shot at a truly effective mass load process that does more than just fill up a SQL database with a bunch of barely-searchable documents.

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I had reason today to play about with the codeplex faceted search project today.

Ni imekuwa karibu kwa wakati, lakini mimi alisita kushusha na matumizi yake kwa sababu za kawaida (hasa ukosefu wa muda), plus outright fear 🙂

Kama wewe ni kuangalia kuboresha tafuta wako na kuchunguza chaguzi mpya, download it and install it when you have an hour or so of free time. I followed the installation manual’s instructions and it took me less than 20 minutes to have it installed and working. It provides value minute zero.

It does look pretty hard to extend. The authors provide a detailed walk-through for a complex BDC scenario. I may be missing it, but I wish they would also provide a simpler scenario involving one of the pre-existing properties or maybe adding one new managed property. I shall try and write that up myself in the next period of time.

Bottom line — in minutes, you can install, configure it, use it and add some pretty cool functionality to your vanilla MOSS search and be a hero 🙂

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Kwenye jukwaa MSDN tafuta, mara nyingi watu kuuliza swali kama hili:

"I have a document named ‘Programming Guide’ but when I search for ‘Pro’ tafuta asipate."

Inawezekana si kujisikia kama ni, but that amounts to a wildcard search. The MOSS/WSS user interface does not support wildcard search out of the box.

Kama kuchimba katika sehemu ya mtandao tafuta, utapata checkbox, "Enable search term stemming". Stemming is a human-language term. It’s not a computer language substring() aina ya kazi.

Hizi ni baadhi mashina:

• "fish" is a stem to "fishing"
• "major" is a stem to "majoring"

Haya si inatokana:

• "maj" is not a stem to "major"
• "pro" is not a stem to "programmer"

The WSS/MOSS search engine does support wild card search through the API. Here is one blog article that describes how to do that: http://www.dotnetmafia.com/blogs/dotnettipoftheday/archive/2008/03/06/how-to-use-the-moss-enterprise-search-fulltextsqlquery-class.aspx

3 chama bidhaa, Ontolica, provides wild card search. I have not used that product.

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