INFORMATION LOGISTICS VERSUS SEARCH. How context-sensitive information retrieval saves time spent reaching goals
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1 INFORMATION LOGISTICS VERSUS SEARCH How context-sensitive information retrieval saves time spent reaching goals
2 2 Information logictics versus search Table of contents Page Topic 3 Search 3 Basic methodology 4 Relevance scoring via PageRank 4 Semantic search 4 Aspect search 5 Personalized search 6 Information logistics 6 Basic methodology 7 Technical implementation 8 GIN Server as a search engine 8 Detailed functional explanations
3 3 Information logictics versus search Search Basic methodology Ever since the 1990 s, when Google s reign began, digital information access typically began with a keyword. Today, most websites and apps are accessed through search engines. The challenge of search lies in the completeness and relevance of the search results. The following illustration depicts how a user must actively ask a search engine in order to retrieve results. Searching for information thus has several disadvantages: The user must be aware that important information exists in order to explicitly search for it. Unexpected information or results remain undiscovered. The user must actively search and evaluate results, especially because he does not know whether the results are important or not. This requires time. The user must choose the right keywords or keyword combinations in order to receive relevant results. Knowledge buried in files or documents containing related terms often remain unfound, as does knowledge in documents with differing keyword combinations in the case that the user does not choose optimal keywords for his search query. Diverse search operators and search software manufacturers have tried to compensate for these search deficiencies.
4 4 Information logictics versus search Relevance scoring via PageRank Google rapidly overtook AltaVista s position as the search market leader because its PageRank algorithm provided search results with sufficient relevance to meet user expectations. This relevance scoring was based on link popularity, that is, references to a particular website from other websites contributed to each website s relevancy score. This relevancy scoring can only be applied to corporate intranets on a limited basis, leaving enterprise search largely with the state of search effectiveness that the web suffered in the 1990 s. Semantic search Users word choice in documents may vary across data and documents - a challenge which software manufacturers have responded to with the semantic search. Semantic search resolves queries according to the meaning of processed keywords, and not just according to matches against the keyword itself. For example, a user searching for car would receive results containing specific automobile brands and models as well as results containing the word car. The result is a more complete search result set - but also an increased number of results with greater ambiguity. In other words, the results are less relevant and are more difficult for the user to focus by providing additional targeted keywords. Aspect search Some software manufacturers offer an aspect search and metadata search in order to help users better fine-tune their search results. This allows the user to filter search results by metadata. This selection can be extended beyond conventional metadata such as document type or time range by including tagwords or document classification systems. This extension requires a maintained vocabulary and thesaurus list which the user should consider when saving their documents. There are two reasons why approach fails in practice: (1) Online content changes daily online and within organizations. Even a continually maintained thesaurus is often incomplete. (2) Users often have difficulty classifying their documents correctly, resulting in incomplete or inaccurate classification and tagging. Further, adding these attributes is time-consuming.
5 5 Information logictics versus search Personalized search Notable examples of personalized search engines include Google and Facebook, which use personalization to filter search results and to increase their relevance. To do this, they collect detailed statistical user profiles over user search behavior, or profile information about a user s social network and organizational interests. This approach fails in practice because a user s information requirement is not based on statistics, but rather on their situation. A person using the internet for work and at home takes on different roles, affecting their information requirements. Further, a person needs information relevant to their situation, and in a different time and place, completely different information. Search results may even be worse, because the user cannot control the bias in the search results they receive through keywords alone. Search is the problem, not the solution.
6 6 Information logictics versus search Information logistics Basic methodology Information logistics turns search upside down. It proactively provides the user with information related to their current information requirements. To do this, the digital content which the user is currently viewing is analyzed to determine the user s current context, and to provide the user with related information to this context. Compared to search-based workflow, an information logistics worfklow would look like this: Avoid search deficiencies with information logistics: Users lose no time due to search. The most important Information is immediately available when it is needed. The user receives exactly all information relevant to his current situation. The user is proactively informed about important information. The user does not lose time on classification, categorization or tagging.
7 7 Information logictics versus search Technical implementation GIN Server enables information logistics by automatically analyzing and networking all available content objects. A content object refers to a single data entry, such as a master data record from an ERP system, or a document from a DMS system. Each node in this network, or graph, defines a working context. GIN Server s semantic analysis differentiates itself from other semantic technologies: Each content relationship contains a reason and a weight. This allows a precise relevance score calculation for every content object. The content analysis is fully automatic and autonomous. That means: o o it does not require training data or training phases. it does not require manual ontology or taxonomy modeling, nor does it require rule definitions. The content analysis runs immediately, triggered by new or changed content objects. The analysis results are available shortly thereafter through GIN Server. The analysis handles structured data, such as database entries, as well as unstructured data, such as documents on a storage device. The analysis runs incrementally, enabling it to scale up across large data quantities. It executes atomically across each new content object, and does not need to recalculate the entire semantic network. GIN Server enables information logistics by automatically analyzing and networking all available content objects.
8 8 Information logictics versus search GIN Server as a search engine GIN Server is a semantic middleware solution providing an IT infrastructure for highly efficient data integration and completely automatic data analysis. Search is just one of the many functions that GIN Server offers. A direct comparison of this one feature with other search engines clearly demonstrates many differentiating advantages: GIN Server Solr Google TREX Full-text search Operators Metadata search Contextual search Moderated search Search suggestions Fuzzy search Smart ranking Reasoning Auto-tagging Auto-taxonomy Uniform Information Layer Content interaction Event handling Detailed functional explanations Modern search engines all support full-text search encompasses a combination of multiple keywords and logical operators. GIN Server provides many additional functions which other search engines support partially or not at all. Metadata search refers to filtering a search by particular metadata, such as a document type or document author. Contextual search refers to searching for content objects within a particular context, such as documents related to a particular project. GIN Server can formulate search queries according to
9 9 Information logictics versus search context, for example documents containing content related to a project in the current month. This is only possible because of the dynamically linked content network. Moderated search enables not only a search filtering by metadata, but also suggests additional keywords - for example a list of customer names. GIN Server can limit these moderated keyword suggestions according to previous successful searches, or according to the available metadata. This navigation allows optimized, rapid access to relevant information goals. Search suggestions refers to search query auto-complete recommendations containing additional keywords. Google Suggestions is one particularly well-known and popular form of search suggestions available for the internet. To derive search suggestions, GIN Server does not collect statistics on keyword requests, but rather calculates suggestions based on recommendations from relationships within the available document and information content themselves. Fuzzy search refers to the ambiguous search for a keyword. For example, the spelling of a particular keyword may vary slightly, contain a different grammatical suffix, or a different yet closely related term. This significantly expands the search domain, especially when the exact spelling of a word or name is unknown. Smart ranking refers to the relevance calculation of content objects based on the number of and relevancy of related content references. This is analogous to PageRank on the internet, where websites are linked via hyperlinks. GIN Server uses its dynamic content object linking to extend this fundamental concept not just to organizations with any data, but also to enrich it with machine learning and user behavior. This allows GIN Server to reflect content that is more important or less important to the user s own context. Reasoning refers to drawing conclusions from dynamic content relationships. Thanks to its network of qualified relationships, GIN Server is able to answer targeted queries, such as: Return all relationships involving people from the product development department with currently active customer projects that were procured through that person s role as a project manager. This allows GIN Server to answer complex questions typical of business intelligence applications. Auto-tagging refers to the automatic assignment of characteristic terms to a particular text. GIN Server enables auto-tagging by automatically extracting and linking terms from the text with other known content through modern computational linguistics. Auto-taxonomy refers to the automatic and dynamic generation of a vocabulary hierarchy describing topics and topical aspects in a data and document collection. GIN Server further allows querying against the dynamically built, enterprise-wide taxonomy as well as providing a category tree as a navigational entry point. The Uniform Information Layer refers to a dynamically derived, harmonized enterprise data model. It allows precise search across heterogenous data from hetorogenous data sources. For example, a query for contacts from Munich includes all entries across data sources containing
10 10 Information logictics versus search contact information, regardless of whether they are considered contacts or people in the different data sources. Content interaction refers to user actions executed from the search result listing directly against production data, such as editing or forwarding functionality. GIN Server does not restrict the number of interactions possible against content objects, and allows action types to be defined according to document type. This allows GIN Server to support bi-directional data and process integration - allowing new relationships and data linkages to be written back into the system. Event Handling refers to the systematic result evaluation according to business logic rules. GIN Server supports editing results on the basis of new, changed or deleted content objects. It also processes new, changed and deleted statements which describe data relationships. Finally, it offers intelligent business logic management direct access to the closed-system analysis results from new, changed or deleted content objects. These events form the basis for contentsensitive information retrieval, or event triggers for additional business processes.
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