Semantic Wiki: Automating the Read, Write, and Reporting functions Chuck Rehberg, Semantic Insights

Slide 2 Approach Automate the capture of content and knowledge and extraction of information of interest to the community in a semantic form – based on training and reading styles. Provide semantic search, access, across a corpus of content that has previously been read to create information of interest. For example a plug-in. (IRS tax info, for example, or other G2C or G2B needs.) Produce reports (in various styles, formats) from information of interest based on training and writing and reporting styles

Slide 3 Training the system to read To Automate the Reading process you need: –A Reading Style States how to linguistically identify your specific Concepts –An Ontology Defines your specific Concepts and how those Concepts are related –A Dictionary is useful too

Slide 4 Reading Styles and Ontologies A “Reading Style” contains the linguistic patterns used to identify the links between natural language text and the Concepts in the Ontology We provide three approaches to creating a Reading Style and Ontology –Manual Approach –Semi-Automatic Approach –Automated Approach Each of these approaches begins with the selection of one or more training documents

Slide 5 Reading Style: Manual Approach Starting with a training document The user indicates the concepts of interest by highlighting words or phrases, whole sentences or groups of sentences. The system analyzes the linguistic structure within and between sentences, and: –Identifies or Creates newly discovered Concepts in the Ontology –Adds relationships and characteristics of those Concepts –Links the key words or phrases to Concepts in the Ontology

Slide 6 Reading Styles: Semi-Automated Approach Provide a list of key words or phrases representing concepts of interest Identify one or more documents containing those key words The system reads through the documents and identifies each sentence or group of sentences containing the key words As with the manual approach, the system analyzes the linguistic relationships, and creates the Ontology and Reading Style.

Slide 7 Reading Style: Automated Approach Identify one or more documents containing Concepts of Interest (as yet not specified) The system reads through the documents and identifies possible key words or phrases of interest (Concepts) in each sentence or group of sentences. As with the Semi-Automated Approach the system analyzes the linguistic relationships, and creates the Ontology and Reading Style.

Slide 8 Automated Document Reading The system generates a new Document Reader from your Reading Style and Ontology. You identify the document corpus to read The Document Reader harvests and stores the Information of Interest from the document corpus

Slide 9 Automated Report Generation Starting with the results of reading –You may select a subset of the information read on which to report –You then apply a report template and writing style to generate a fresh report covering the information read Reports can do many things, for example; –Extract business rules from policy documentation –Identify complex relationships between any set of concepts

Slide 10 Report Specification Reports may include –Document structuring (sections, subsections, headers, etc,.) –Narrative prose –Lists –Tables and Diagrams –Glossaries, dictionaries, references The form, content, and presentation of a report are specified using Report Templates, and both Presentation and Writing Styles.

Slide 11 Different Points of View The need to Harmonize Different Points of View arises when: –Different Reading Styles are mapped to the same Ontology –Different Reading Styles are mapped to different Ontologies –A single Reading Style developed over time by one or more people come to have conflicting mappings

Slide 12 Harmonizing when Reading Styles are mapped to the same Ontology By analyzing how Concepts are mapped between the different Reading Styles the system can: –Detect different understandings (semantic conflict) –Detect common understandings (semantic overlap)

Slide 13 Harmonizing when Reading Styles are mapped to different Ontologies Assist the users in identifying where the same words or phrases –Map to essentially the same concept in each Ontology –Map to different concepts –Have overlapping mapping to concepts

Slide 14 Harmonizing within a single Reading Style By analyzing how each Concept is mapped within a single Reading Style the system can identify when: –A single Concept is identified by multiple different linguistic constructs (many ways of saying the same thing) –A single linguistic construct identifies more than one Concept (multiple meanings for the same language)

Slide 15 Reading Wikis and other information sources Standard information source encodings include: –pdf, html, doc, ppt, txt, wiki –Results from standard web search engines Additional information source encodings can be added as plug-ins

Slide 16 Narrowing down Report Content The results of reading may generate a large amount of IOI. To identify the useful subset of information the user may –Identify the specific set of concepts to include/exclude –Pose a Natural Language Query

Slide 17 Interactive Query Dialogue Another form of reporting is to interactively query the results by using natural language queries –For example; What relationships exist between President Bush and Herbert A. Allen III? The answers come back according to the chosen Writing Style. *Note: Without the addition of Reasoning Rules the results are limited only to what was read in the documents read.

Slide 18 Building on the IOI through Reasoning Rules So far we have heard about Reading Rules Another type of rules are, Reasoning Rules –Reasoning Rules operate on IOI to produce new IOI –The resulting IOI can represent conclusions, inferences, trends, etc. –This new IOI is available for query, selection, and reporting as before

Slide 19 Concept Demo SIRA LITE – A browser plug-in that invokes a SIRA reader to “read” one or more web pages and produce a filtered report containing the information of interest Note: Due to where we are in product development, this demo is not live.

Slide 20 Questions