semantic web and artificial memory perspectives on technology deri galway galway, july 2004 lars...

Semantic Web and Artificial Memory Perspectives on Technology

DERI GALWAYGalway, July 2004

Lars Ludwig

July 2004

LARS.LUDWIG@DERI.ORG

2

• Thesis 1: Continuing Document Management / a document based (Semantic) Web gets us ever further into trouble (no information normalization, extra-effort for machine-readability (annotation), serial content(-views) of networked information insufficient structure).

• Thesis 2: The (Semantic) Web can only unfold its potential by unleashing personal knowledge management.

• Thesis 3: In order to finally become successful, knowledge management has to stop focusing on working results (documents) and to start supporting the personal process of knowledge acquisition (learning, collecting information).

Artificial Memory ConceptSome Queer Theses on Knowledge Management I

July 2004

LARS.LUDWIG@DERI.ORG

3

• Thesis 4: Semantic Web/Ontology Management technology could make it possible to explicate, structure, and store even knowledge formerly known to be purely a matter of personal memory, conversation, and example (tacit knowledge).

• Thesis 5: DMSs/CMSs merely treat the symptoms of dealing with 'unstructured information‘.DWHs merely treat the symptoms of proprietary, semantically insufficient and insufficiently explicit meta data of applications.

• Thesis 6: Document Management System, Data Warehouse System, and ‘Artificial Memories’ could be integrated into and enhanced by a single semantically enabled enterprise knowledge base.

Artificial Memory ConceptSome Queer Theses on Knowledge Management II

July 2004

LARS.LUDWIG@DERI.ORG

4

Artificial Memory ConceptDefinition

- Ontology-based - Ontology-enhancing (establishes reverse/user-driven ontology engineering) personal knowledge base- Aims to reflect and also shape memory structures- Is to back semantically enabled (business) communication and collaboration- Replaces and possibly enhances information/knowledge structures (esp. Documents via instance serialization) Seen from a cognitive perspective, an AM should allow for - Thought-accompanying storing (store as you think and anything that you are thinking of [information chunks]) and - Thought-accompanying retrieval of knowledge (get whatever you are thinking of)

July 2004

LARS.LUDWIG@DERI.ORG

5

Artificial Memory ConceptGoals and Vision – Personal Dimension

• Mirror, increasingly support, and extent memory

• Thereby become a general and common means to enhance and increase knowledge

• Help to structure perception and thought

• Function as a tool to generate, merge, and evolve ontology concepts and rules

• Assist in problem-solving

• Manage the ever more overwhelming information flood

July 2004

LARS.LUDWIG@DERI.ORG

6

Artificial Memory ConceptGoals and Vision – Human Collaboration

• Interface in teaching and learning (e.g., knowledge assessments, knowledge repetition, knowledge transfer)

• Enable (semi-)automatic ontology-supported conversations and knowledge exchange to enhance collaboration

• Accompany, replace or extend potentially less structured kinds of knowledge exchange like human conversation, documents, e-mail etc.

July 2004

LARS.LUDWIG@DERI.ORG

7

Artificial Memory ConceptGoals and Vision – Collaboration with Machines

• Instruct • Artificial agents • Robots • Programms • or machines in general (replacing proprietary machine

interfaces)

• Assist and be assisted by natural language processing (NLP)

July 2004

LARS.LUDWIG@DERI.ORG

8

Artificial Memory ConceptGoals and Vision – Business and Society

• Enrich Enterprise Memories by knowledge transfer from Artificial Memories

• Enrich Artificial Memories by information transfer from Enterprise Memories

• Integrate Artificial Memory into business processes

• Integrate Artificial Memory into society/social processes

July 2004

LARS.LUDWIG@DERI.ORG

9

Artificial Memory ConceptGoals and Vision – Technology

• Create new fields of application

• (further) Integrate Semantic Web technology (SemanticWeb services) into Artificial Memory

• Improve usability of Semantic Web technology

• Improve human-readability of ontology-based knowledge bases

• Integrate/interface experience through artificial perception (long-term)

July 2004

LARS.LUDWIG@DERI.ORG

10

Artificial Memory versus Semantic Web ConceptTwo Sides, One Coin

• SW vision:

• "A new form of Web content that is meaningful to computers will unleash a revolution of new possibility." Tim Berners-Lee

• Annotated Web pages (structured and unstructured data on the same Web page)

• AM vision:

• Lead to a new kind of knowledge handling that will facilitate Web content that is meaningful to computers. Reaching beyond realms of the knowledge to be presently found on the Internet, it is meant to feed the Semantic Web

• Combined SW-AM vision:

• Web pages of annotations, that is, made of serialized instances/concepts (structured data, and unstructured data structured at thought-granularity level [can be annotated])

July 2004

LARS.LUDWIG@DERI.ORG

11

Artificial Memory versus Semantic Web ConceptDifferences SW vs AM I

• what's new: Semantic( )Web Services, ontology based Web searching SW

OLSP - Online Semantic Processing (parallel to OLAP), coherent sub-document level knowledge management AM

• cognitive model: object-orientation (fixed structure, thing-granularity structured & unstructed data on Web pages) SW information chunks (fluent structure, thought/term granularity structured & structured unstructured data)AM

• ontology agreement level: > 1 person (inter-subject consensus) SW >= 1 person (intra-subject consensus V (intra-s. c. Λ inter-s. c.) AM

July 2004

LARS.LUDWIG@DERI.ORG

12

Artificial Memory versus Semantic Web ConceptDifferences SW vs AM II

• knowledge management perspective:

focus on knowledge objects SW focus on knowledge subjects AM

minimal ontological commitment SW

maximum/personal ontological commitment AM

denormalized knowledge (annotated documents) SW

normalized knowledge (artificial memory knowledge base / Web) AM

July 2004

LARS.LUDWIG@DERI.ORG

13

Artificial Memory versus Semantic Web ConceptDifferences SW vs AM III

• main stress: machine-readability SW

human-readability AM

• main potential: web of semantically collaborating applications SW

web of semantically enhanced human collaboration AM

• main difference: no direct data entry on Semantic Web SW strives for highest possible data entry and data retrieval usability (thought-/work-accompanying) AM

• main flaw (as far as I see): does not sufficiently take humans on the equation SW does not sufficiently take machines on the equation AM

July 2004

LARS.LUDWIG@DERI.ORG

14

Artificial Memory versus Semantic Web ConceptDifferences SW vs AM IV

• chance:merge, further develop, and mitigate these concepts into a kind of 'Humanoid-Machinoid Web' as the central (mutually fair) inferface between human and artificial intelligence

July 2004

LARS.LUDWIG@DERI.ORG

15

GoalsPossible (short-term) Research Topics

• Integrate SW-AM vision into a strategy and innovation scenario• Research ontology/AM-based collaboration• Research the reverse ontology engineering process needed

for knowledge management• Research the interdependence between AM and enterprise

memory (EM); sketch feasible business scenarios • Research information exchange between AM and EM

July 2004

LARS.LUDWIG@DERI.ORG

16

GoalsNext steps?

• Create enterprise strategy/innovation portal use-case• Sketch ontology-based strategy and innovation scenario (full

picture)• Sketch use-case (restricted picture)

• Integrate into an strategy/innovation loop• Strategy/Innovation ontology population/learning • (Semi-automatic) Information needs specification• Automatic data collection from WWW• Use S&I ontology to point to relevant BI/DWH-Reports • Support AM-based information exchange and evaluation

process (use explicit and implicit collaborative filtering based on recommendation and usage ontologies)

• Define S&I ontology refinement process/mechanisms (EM-AM information exchange processes and rules)

• Define/automate information needs refinement for data collection (loop-based, taking into account explicit and implicit information evaluation, and EM-ontology changes/refinement)