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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)

Contents

1. Characteristics 2

2. SearchQuery Example 3

3. SearchQuery Inference Trace 3

4. Time Dimension Example 4

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1. 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-Data1

1 see http://www.linkeddata.org

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2. SearchQuery Example

Someone enters a contextual query in a standard search box:

3. SearchQuery Inference Trace

The 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 request

with 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 ontological

body put on top of a fulltext-search index. This means that the information that Abstract Expressionsim is a subclass of

Modern Painting is in the system. However the relationship between Claire Oliver and Helen Frankenthaler lies within

the data and is not covered by the ontology. Thus the inference/reasoning from above can not conducted.

Semantic identification of the

category “Modern Painting”

Semantic filtering of the Modern

Painting information-space to

Galleries in NYC

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4. Time Dimension Example

This 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-date

interval that overlaps with the current time (additional NLP logic in the system offers terms like "Current" which

automatically maps to date information).