Semantic input for consumers
As yet there are no perfect methods for consumers to enter semantic data. Entering free text is certainly convenient, but we just don't have "perfect" natural language processing software, yet.
The common forms of consumer data for which semantic data are desirable include:
- email messages
- email address books
- blog posts
- Twitter "tweets" and other forms of micro-blogging
- IM instant messages
- cell phone calls
- text messages
- digital camera pictures
- transaction data, including credit card transactions and online ecommerce forms.
Unless the consumer is an "English geek", it is unlikely that they will be willing to create structured sentence diagrams to express the meaning of even simple statements.
The full range of methods for semantic input include:
- Natural language processing (NLP) for text and audio.
- Controlled vocabularies (e.g., Structured English)
- Text mining.
- Full semantic map editing (e.g., ala sentence diagrams)
- Detection of object references in free text (e.g., proper names and nick names for people, places, and things), possibly based on customizable dictionaries.
- Form-based input, including drop-down lists for direct selection of semantics
- Transaction device data (e.g., GPS location, date and time, etc.)
- Transaction information (e.g., online ecommerce data)
- Background review and re-entry by trained "semantic coder" (e.g., in an "offshore" market.)
- Feedback and enhance - mine consumer input for apparent concepts and ambiguity, annotate the original, and allow consumer to approve and select between alternatives and "hints"
I am sure that there are a variety of other methods, existing, proposed, or not yet imagined, but these are a starting point for discussion, as well as an illustration of how much more research and innovation are needed.
I have been trying to avoid a reliance on full-bore NLP, but the simple truth is that it may in fact be the best foundation.