Info: ** For advanced user only **
When you search the content of files with Noggle, it uses the right search iFilter plugin according to the file extension. The following free utility allows you to easily view the search filters installed on your system and the file extensions that are associated with them, as well as it allows you to easily add or remove file extensions for these filters.
Adobe does not bundle the iFilter interface in the latest version of Adobe Acrobat Reader 11.x or DC 64bit. You need to manually activate the Adobe iFilter Add-On in order to be able to index and search PDF documents.
Click here to download and install the Adobe iFilter interface: Activate Adobe iFilter Add-On (64bit, Version 11.x or DC)
You should be fine if you use older versions or have also 32bit Acrobat reader installed. If not, please update in order to also get Noggle index your pdf files.
The Adobe PDF iFilter enables indexing Adobe PDF documents using Noggle indexing clients. This allows the user to easily search for text within Adobe PDF documents. The key benefits include:
As shown below, the iFilter is either bundled with the product or provided as an add-on. 32-bit Acrobat 9.x-11.x products bundle a 32-bit PDF iFilter. 64-bit product installs require that the add-on be installed separately. If you already have an iFilter plugin from a previous install, reinstall it.
| Version | 32-bit | 64-bit | iFilter version and notes |
|---|---|---|---|
| Reader 8.x | bundled | None | Version 6. |
| Acrobat 8.x | bundled | bundled | Version 6. |
| All 9.x | bundled | Add on | Version 9. First added in 10.1. 32 bit not in 10.0-10.0.3 |
| 10.x | bundled | Add on | Version 9. Security improved with 10.1 |
| 11.x | bundled | Add on | Version 11. Updated for 11.x products and its supported platforms. |
| DC | not available | Add on | Version 11. No change for DC products and their supported platforms. |
The Noggle “KnowledgeMap,” a search result visualization tool, provides users with essential information about the structure of topics that appear within the search results. The Noggle clustering algorithm scans internal relations and linguistic patterns among all the documents according to how similar they are to the initial search request. This tool can unearth new groups or cross-document relationships, which might guide users to new, interesting areas that build upon their initial search request. Clustering is one of many methods that can be used to make searching collections of documents easier.
We have often heard users demand such clustered cross-document relationship information, likely because they become frustrated with the constantly growing document volume and fragmented data storage solutions they encounter in the cloud and other big data services.
Please review the following video tutorial:
[embedyt]http://www.youtube.com/watch?v=vZdNdJZrpn4[/embedyt]
A detailed knowledge base article on our NoggleMap search feature can be found here:
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One of the most common problems people used to encounter when searching for information is that they could not find documents specifically related to what they were looking for. Nowadays, this task is quite successfully handled by standard search applications.
Thanks to these sorts of search engines, pulling up results has become easy. However, when it comes to explaining the search results or displaying specific details on what sort of results have been returned, users’ options are much more limited. Usually, a search application displays a ranked list of documents and a snippet of their contents. These ranked lists are helpful for document retrieval, but far away from knowledge management. Information about the internal relationships among the documents in the search results is often not provided by standard search algorithms.
“Search result clustering” is defined as an automatic, non-supervised grouping of similar documents in a search hits list returned from a search engine. Clustering is one of many methods that can be used to make searching collections of documents easier.
So, the Noggle “KnowledgeMap,” a search result visualization tool, provides users with essential information about the structure of topics that appear within the search results. Furthermore, the Noggle clustering algorithm scans internal relations and linguistic patterns among all the documents according to how similar they are to the initial search request. This tool can unearth new groups or cross-document relationships, which might guide users to new, interesting areas that build upon their initial search request.
We have often heard users demand such clustered cross-document relationship information, likely because they become frustrated with the constantly growing document volume and fragmented data storage solutions they encounter in the cloud and other big data services.
To illustrate the problems with conventionally ranked search result lists, let’s imagine a user wants to find information about “security.” Therefore, he or she starts with the simple search term “security.”
First, the user selects peer libraries that might be relevant. In this example, the user has libraries from three different peers. In addition, the user selects six of his own libraries to perform the search request.
Figure 1: Search results for search term “security” on nine libraries from four different owners
Figure 1 shows that the search included 27,616 documents and returned 1,500 top-ranked documents related to “security.” Obviously, this is a very general query that leads to a large number of hits. Therefore the majority will be about information security, system security, or security policies based on a library for “Information Technology”.
A determined user patient enough to sort through results ranking 100 or lower should be able to find some hits on topics like “access control” or “service continuity.” However, one problem with ranked lists is that sometimes users need to wade through irrelevant documents to get to the ones they want.
But what about an interface that groups search results into separate semantic topics? Like network security, data security, access control, service continuity, and so on? And what if these groups were decided automatically from their own internal content—not by biased methods where someone defines what might be important?
By generating groups like this, the user will immediately get an overview of what the results contain and should be able to pick out relevant documents with much less effort.
The following figure shows how the NoggleMap feature automatically detects cross-document relations based on linguistic patterns. The left part of the screen shows the clusters and the number of documents related to that cluster. The right panel shows a visual representation of that information.
Figure 2: Clustered search results for “security” via the Noggle KnowledgeMap document clustering service
All 1,500 documents are linked to one or more of these clusters. This way, users don’t need to browse through a ranked list from the top down—they can narrow down the major cluster they are looking for and go from there.
In order to be helpful, search result clustering must organize similar results into one group. This is the primary requirement for all document clustering algorithms. But in search result clustering, the clusters labels are also extremely important. The program must accurately and concisely describe the cluster’s contents so that users can decide if the information is relevant.
Since users are often unaware of all their choices in a search, they do not always know the exact phrase they should search for. Thus, starting with a more generic search makes sense. Let the artificial intelligence of the Noggle search engine detect knowledge clusters based on the cross-document linguistic patterns. The visual guide then allows the user to quickly focus on the results of interest by visually selecting the relevant clusters.
This kind of interface for search results is implemented by applying a variety of document clustering techniques to the results returned. This is something that we call the Noggle “KnowledgeMap” and “ClusterSearch” technique.
The user can now select the cluster “Access Control” and browse the relevant documents from the initial request on “security”. And later focus in on the associated documents.
Figure 3: Document list in the security cluster “Access Control” from the overall search results
This makes document retrieval over different libraries and document search spaces much more efficient. By using “generic” search terms first, Noggle builds clusters for users, who can then narrow down their area of interest and check relevant documents there. Using Noggle this way is not just about searching for documents. Finally, it is a full, non-supervised knowledge management approach to retrieving knowledge that matters. Without the need to know exact phrases and exactly which documents they appear in.
The following live presentation showcases the document clustering for included TED Talk digital library. All maps are build by the Noggle client based on the standard application (2min.):
[embedyt] http://www.youtube.com/watch?v=YMHxWGLddjE[/embedyt]
The NoggleMap feature combines latest technolgies based on Text parsing, Microsof Azure, Apache Lucene, Carrot2 Project, Noggle pre- and post-processing algorithms and the Noggle network. Patent pending.
Further Reading:
There are different ways how Noggle helps to find documents. The main approaches can be described in the following categories:
1. Searching documents based on text queries
This is the standard way of how search requests work in the web or on google: Put your search string in the input box and noggle will present the found documents based on relevance ranking in the output window.
2. Searching documents based on KnowledgeMap clusters
This approach is used if you are not sure about the concrete term or search string you need to search for. So you start with just a generic search string which you put in the search text box. As a result, noggle will present a large list of documents which might match the generic top-level word search. Now, to narrow down your search, you can build a so-called “noggle map” which clusters found documents based into linguistic clusters. These clusters will be presented visually. Now, you can select one or more clusters which come more close to your topic you are looking for and press the “NoggleCluster” search button. Now a new search request is performed to just search for content in the selected clusters. Afterwards, the found documents will again be clustered based on linguistic patterns. This process can now be repeated to slice and dice the available content into categories which are automatically generated based on the content until you have found a cluster with documents that have a high relevance to your individual knowledge you are looking for.
3. Searching documents with the “similar like this” function
Another important way of searching documents is that you need to check “similar” documents once you have found once document of interest. So if you have found one document that matches your area of interest, you can select this document and perform a “similar like this” search request within all available libraries. This way, noggle will now check which documents have a “similar” content like the selected one and will present all documents which have a content-wise correlation to the select document. This feature is really great because it can find similar document across different libraries. So if John has a project document with interesting content, just select this document, perform a “similar like this request” and noggle will check if you other peers/libraries contain similar documents. It is similar to what you know from “Amazon” – once you have selected a book, Amazon will present a list of similar books which you might like based on the content of the books. Bring this power now directly to your desktop. Let noggle recommend documents that might be interesting for you based on the one document you selected.
4.Searching documents with the “drop-in” area
This is another use-case often needed for the knowledge worker: Think about a situation when you receive a document via eMail. Now you think “Hmmm, I think I have some similar documents with additional content, havent I or a colleague?”. Now you can drag’n drop the document from your eMail inbox directly on the “drop-in” window area in the Noggle client. Noggle will instantly run the indexing service on the document and instantly check all available libraries for “similar” documents. So within milliseconds, Noggle will present you a list with documents in your libraries, which a similar to the dragged document. Even if the document is not present in any library, it can be used to search similar documents in all libraries available. In addition, it will automatically perform an “expert” search. This means that in addition to the similar documents list, you automatically get a list of peers/experts which have a similar knowledge profile to the document your dropped on the application. And all of this happens in near-realtime instantly on your desktop.
The Noggle library functions are based on Lucene, an open source, highly scalable text search-engine library available from the Apache Software Foundation. Web sites like Wikipedia and LinkedIn have been powered by Lucene.
Noggle brings the best availabe search and indexing technology right to your desktop, the Noggle App.
Based on Lucene in the back, Noggle is able to achieve fast search responses because, instead of searching the text directly, it searches an index instead – the “noggle library”. This would be the equivalent of retrieving pages in a book related to a keyword by searching the index at the back of a book, as opposed to searching the words in each page of the book.
Noggle library tools focus mainly on text indexing and searching. It is the core element that is used to build different search capabilities. Based on Lucene, the noggle library core has many features. It:
The Noggle library core lets you index any data available in textual format. Therefore, Noggle uses pre-processing and parsing techniques to extract the plain text from different source formats like Word, PowerPoint, Excel, PDF files and other formats. Noggle can be used with almost any data source as long as textual information can be extracted from it. The first step of noggle before building the library by indexing the data is to make it available in simple text format. Noggle uses custom parsers and data converters; mainly based on the Microsoft IFilter technology.
Indexing is a process of converting text data into a format that facilitates rapid searching. A simple analogy is an index you would find at the end of a book: That index points you to the location of topics that appear in the book.
Noggle stores the input data in a data structure called an inverted index, which is stored on the file system or memory as a set of index files. Most Web search engines use an inverted index. It lets users perform fast keyword look-ups and finds the documents that match a given query. Before the text data is added to the index, it is processed by an custom noggle analyzer.
The analyzer is converting the text data into a fundamental unit of searching, which is called as term. During analysis, the text data goes through multiple operations: extracting the words, removing common words, ignoring punctuation, reducing words to root form, changing words to lowercase, etc. Analysis happens just before indexing and query parsing. Analysis converts text data into tokens, and these tokens are added as terms in the Noggle library index.
As a result, a high-performant library is created which can be shared with your peers to execute search request in milliseconds over the full content. The indexing and library building process is not only providing fast search results – it also provides relevant ranking scores back to the search results.
Once your decide to share a noggle library with one of your peers, the library will be encrypted and obfuscated once it leaves your client to the noggle network. Only the named peer is available to decrypt the library – so your library is always secure in the noggle network.
Our servers are operated in datacenters within the EU.
Please read our security guidelines for more information on security.
To make it short: No. Noggle is NOT a peer-to-peer file sharing software. You can not share file or documents directly with noggle.
Noggle sets a library management toolset on top of your documents. This library management helps to make your documents or files searchable. You can share the created library information with your private experts, partners or colleagues (“peers”). After you shared a library, your peers are able to search and locate documents that are stored on your local accessible storages – but they are not able to access the document itself. The library makes your content findable by others. And you define who is able to get your library. So your colleagues can search and find documents they dont have access to, but you want your peers being able to find documents you have. The library management toolset makes your documents findable without the need to share documents or change access rights. Once a peer has found an relevant document that is located in one of your libraries, they can request to get access to the document. But you decide, case by case, if you want to share the document itself. Noggle does not provide access to your documents for your peers.
Saied this, we use the peer-to-peer technology to create a secure managed network where each user is able to build libraries and share the library information with dedicated, named peers. This allows an easy way to share, find and locate relevant content. The managed service only provides security. The user decides and controlls everything. Noggle only provides the managed service to connect dedicated peers. There is no central instance which is doing search and returning search requests. Everything happens on the client side and everything that leaves your client is encrypted until it reaches the peer client you have defined.
Each client will not act as a server. The client only communicates with the noggle network to provide and receive encrypted library information that is shared with named peers.
Please review the following 5 min. info video:
You can download a features summary article here:
Windows 10 Version:
Noggle Windows 10 Store: Windows 10 Store Download
Windows 7:
Noggle Direct Download: Download Link 1
External Links:
CNET Download: Download Link 2