SBSGRID_Contextual_SearchQuery_Explained.docx                                                              1/4            ...
SBSGRID_Contextual_SearchQuery_Explained.docx                                                                2/41. Charact...
SBSGRID_Contextual_SearchQuery_Explained.docx                                                                        3/42....
SBSGRID_Contextual_SearchQuery_Explained.docx                                                                     4/44. Ti...
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SBSGRID Contextual Search_Query_Explained

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SBSGRID Contextual Search_Query_Explained

  1. 1. SBSGRID_Contextual_SearchQuery_Explained.docx 1/4 Contextual SearchQueries Explained SBSGRID’s Inference and Reasoning Engine allows to execute contextual questions on Linked-Data which equals “semantic graph-traversal functionality” (Dataset example uses Artists, Galleries and Artworks)Contents1. Characteristics 22. SearchQuery Example 33. SearchQuery Inference Trace 34. Time Dimension Example 4© SBSGRID.NET
  2. 2. SBSGRID_Contextual_SearchQuery_Explained.docx 2/41. Characteristics  System matches a keyword entered to a certain subject / field / category / concept as well as a time- or location-information  System returns contextual results based on multiple keywords entered that form “phrases”. These contextual connections are precise and traverse the data and its relationships distinctively  System has the ability to process values and value ranges within these phrases  System has the ability to process synonyms as well as part-of relationships  System has the ability to infer indirect or associative connections between information  Input and sequence of the query is in free form (familiar search box)  User inputs are supported through suggest lists or context-maps (multi-column suggest lists)  Presentation of results are instant (see Google Instant)  Typical “advanced search screen” approaches can be omitted - no combo boxes and drop down lists for advanced search settings - no flat combination of search attributes (but hierarchical and intelligent) - everything stays on the same page (user sees results and refined queries)  Drag and drop support (elements from the result page can be dragged into the search bar)  Ontological enrichment - Knowledge bases (geo information, taxonomies, synsets etc) can be added  System uses Linked-Data11 see http://www.linkeddata.org© SBSGRID.NET
  3. 3. SBSGRID_Contextual_SearchQuery_Explained.docx 3/42. SearchQuery ExampleSomeone enters a contextual query in a standard search box: Semantic identification of the Semantic filtering of the Modern category “Modern Painting” Painting information-space to Galleries in NYC3. SearchQuery Inference TraceThe system returns the gallery “Claire Oliver” (and others)…The following inference logic led to this result:  the gallery “Claire Oliver” is tagged with "Manhattan" and "Gallery"  the artist Helen Frankenthaler is tagged with "Claire Oliver" and "Abstract Expressionism"  "Abstract Expressionism" is tagged as subclass of "Modern Painting" (one time global rule)  "Manhattan" is tagged as subcategory of "New York City" (one time global rule)  the semantic space makes the connection and "infers" that "Claire Oliver is a [GALLERY] which shows [MODERN PAINTING] in [NEW YORK CITY]"This inference trace shows that a “contextual query” is not a search in a technical sense but a distinctive query requestwith a precise result. All explicit and implicit information that lies within the relationships of data becomes accessible.Note: Semantic search solutions work on the concept level not data level (no Linked-Data). They have an ontologicalbody put on top of a fulltext-search index. This means that the information that Abstract Expressionsim is a subclass ofModern Painting is in the system. However the relationship between Claire Oliver and Helen Frankenthaler lies withinthe data and is not covered by the ontology. Thus the inference/reasoning from above can not conducted.© SBSGRID.NET
  4. 4. SBSGRID_Contextual_SearchQuery_Explained.docx 4/44. Time Dimension ExampleThis example extends the query from above with a time dimension (“Current Exhibitions”):Now the result will be reduced to all matching galleries which are linked with an “Exhibiton” with a start- and end-dateinterval that overlaps with the current time (additional NLP logic in the system offers terms like "Current" whichautomatically maps to date information).© SBSGRID.NET

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