Executing Semantics: Towards Networked Science

Anita de WaardDisruptive Technologies Director

Elsevier Labs, Jericho, VT

Three problems, some solutions, and ideas for the future

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Three problems, some solutions, and ideas for the future

• Three examples where scientific discourse falls short: why we need more context

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Three problems, some solutions, and ideas for the future

• Three examples where scientific discourse falls short: why we need more context

• Some projects that are modeling context now

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Three problems, some solutions, and ideas for the future

• Three examples where scientific discourse falls short: why we need more context

• Some projects that are modeling context now• What we really need: networked knowledge

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1. Lexapro for adolescents

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1. Lexapro for adolescents

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1. Lexapro for adolescents

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1. Lexapro for adolescents

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Problem #1: Knowledge is not connected or tracable

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Problem #1: Knowledge is not connected or tracable

• How can we scale up the 1-to-1 interactions on efficacy and side effects happening today?

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Problem #1: Knowledge is not connected or tracable

• How can we scale up the 1-to-1 interactions on efficacy and side effects happening today?

• How do we know who is speaking in a patient forum?

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Problem #1: Knowledge is not connected or tracable

• How can we scale up the 1-to-1 interactions on efficacy and side effects happening today?

• How do we know who is speaking in a patient forum?

• How to we get scientific knowledge in on this?

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Problem #1: Knowledge is not connected or tracable

• How can we scale up the 1-to-1 interactions on efficacy and side effects happening today?

• How do we know who is speaking in a patient forum?

• How to we get scientific knowledge in on this?• How do we know who paid for knowledge?

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Problem #1: Knowledge is not connected or tracable

• How can we scale up the 1-to-1 interactions on efficacy and side effects happening today?

• How do we know who is speaking in a patient forum?

• How to we get scientific knowledge in on this?• How do we know who paid for knowledge?• If a study is sponsored, how do you refer back to

sources that link out to it?

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2. Drug-drug interactions

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2. Drug-drug interactions

• Drug Interaction Knowledge Base

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2. Drug-drug interactions

• Drug Interaction Knowledge Base• Problem: how to integrate knowledge from

various repositories and data stores into a single source

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2. Drug-drug interactions

• Drug Interaction Knowledge Base• Problem: how to integrate knowledge from

various repositories and data stores into a single source

• One of the main stumbling blocks: the way we record experiments in prose:

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E.g. Moltke et al, 1999:

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E.g. Moltke et al, 1999:

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S-CT was transformed to S-DCT by CYP2C19 (Km = 69 µM), CYP2D6 (Km = 29 µM), and CYP3A4 (Km = 588 µM).

After normalization for hepatic abundance, relative contributions to net intrinsic clearance were 37% for CYP2C19, 28% for CYP2D6, and 35% for CYP3A4.

Based on established index reactions, S-CT and S-DCT were negligible inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and weakly inhibited CYP2D6 (IC50 = 70–80 µM)

R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

All samples were of the CYP2D6 and CYP2C19 normal metabolizer phenotype based on prior in vitro phenotyping studies.

The potential inhibitory effect of the stereoisomers of CT, DCT, and DDCT on the activity of six human cytochromes was evaluated using index reactions and methods as follows (Table 1): CYP1A2, phenacetin (100 µM) to acetaminophen (von Moltke et al.,1996a; Venkatakrishnan et al., 1998b); CYP2C9, tolbutamide (100 µM) to hydroxytolbutamide (Venkatakrishnan et al., 1998c); CYP2C19, S-mephenytoin (25 µM) to 4′-OH-mephenytoin (Venkatakrishnan et al., 1998c).

Average relative in vivo abundances, equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000).

Problem #2: Knowledge is not actionable

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Problem #2: Knowledge is not actionable

• Self-reference:

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Problem #2: Knowledge is not actionable

• Self-reference:

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Problem #2: Knowledge is not actionable

• Self-reference:

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R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

Problem #2: Knowledge is not actionable

• Self-reference:

• Reference to external data sources:

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R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

Problem #2: Knowledge is not actionable

• Self-reference:

• Reference to external data sources:

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R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

Problem #2: Knowledge is not actionable

• Self-reference:

• Reference to external data sources:

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R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000).

Problem #2: Knowledge is not actionable

• Self-reference:

• Reference to external data sources:

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R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000).

Problem #2: Knowledge is not actionable

• Self-reference:

• Reference to external data sources:

• Ways of describing meant for human eyes

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R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000).

Problem #2: Knowledge is not actionable

• Self-reference:

• Reference to external data sources:

• Ways of describing meant for human eyes

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R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000).

Based on established index reactions, S-CT and S-DCT were negligible inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and weakly inhibited CYP2D6 (IC50 = 70–80 µM)

Problem #2: Knowledge is not actionable

• Self-reference:

• Reference to external data sources:

• Ways of describing meant for human eyes

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R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000).

Based on established index reactions, S-CT and S-DCT were negligible inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and weakly inhibited CYP2D6 (IC50 = 70–80 µM)

Problem #2: Knowledge is not actionable

• Self-reference:

• Reference to external data sources:

• Ways of describing meant for human eyes

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R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000).

Based on established index reactions, S-CT and S-DCT were negligible inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and weakly inhibited CYP2D6 (IC50 = 70–80 µM)

Problem #2: Knowledge is not actionable

• Self-reference:

• Reference to external data sources:

• Ways of describing meant for human eyes

• Many statements wrapped into one:

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R-CT and its metabolites, studied using the same procedures, had properties very similar to those of the corresponding S-enantiomers.

Average relative in vivo abundances equivalent to the relative activity factors, were estimated using methods described in detail previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000).

Based on established index reactions, S-CT and S-DCT were negligible inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and weakly inhibited CYP2D6 (IC50 = 70–80 µM)

S-CT was transformed to S-DCT by CYP2C19 (Km = 69 µM), CYP2D6 (Km = 29 µM), and CYP3A4 (Km = 588 µM).

3. NIF Antibody Study

Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)

3. NIF Antibody Study• Pilot project to use text mining to identify antibodies used

in studies

Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)

3. NIF Antibody Study• Pilot project to use text mining to identify antibodies used

in studies • Antibodies are a major source of experimental variability:

–Same antibody can give very different results–Different antibodies to the same protein can give very

different results

Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)

3. NIF Antibody Study• Pilot project to use text mining to identify antibodies used

in studies • Antibodies are a major source of experimental variability:

–Same antibody can give very different results–Different antibodies to the same protein can give very

different results• Neuroscientists spend a lot of time tracking down

antibodies and troubleshooting experiments that use antibodies, e.g.:

Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)

3. NIF Antibody Study• Pilot project to use text mining to identify antibodies used

in studies • Antibodies are a major source of experimental variability:

–Same antibody can give very different results–Different antibodies to the same protein can give very

different results• Neuroscientists spend a lot of time tracking down

antibodies and troubleshooting experiments that use antibodies, e.g.:

Tissue sections were blocked with 5% serum and incubated overnight at 4 °C with the following primary antibodies: anti-ChAT (1:100; Millipore, Billerica, MA), anti-Bax (1:50; Santa Cruz), anti-Bcl-xl (1:50; Cell Signaling), anti- neurofilament 200 kDa (1:200; Millipore) ...

Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)

Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)

What studies used my monoclonal mouse antibody against actin in humans?

• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program

• Immunoblot analysis and antibodies• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,

Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)…

Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)

What studies used my monoclonal mouse antibody against actin in humans?

• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program

• Immunoblot analysis and antibodies• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,

Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)…

Subject is Human

Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)

What studies used my monoclonal mouse antibody against actin in humans?

• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program

• Immunoblot analysis and antibodies• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,

Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)…

Subject is Human

mAb=monoclonal antibody

Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)

What studies used my monoclonal mouse antibody against actin in humans?

• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program

• Immunoblot analysis and antibodies• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,

Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)…

Subject is Human

mAb=monoclonal antibody

Maryann Martone, Jan 2012: 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)

What studies used my monoclonal mouse antibody against actin in humans?

• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and from subjects with AD (n11) were obtained from the Rapid Autopsy Program

• Immunoblot analysis and antibodies• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,

Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000, Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8 mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000, Santa Cruz Biotechnology)…

Subject is Human

mAb=monoclonal antibody

•95 antibodies were identified in 8 articles•52 did not contain enough information to determine the antibody used

Problem #3: Knowledge is not connected to the real world

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Problem #3: Knowledge is not connected to the real world

• No way to ensure connections between experiments/real-world manipulations and record

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Problem #3: Knowledge is not connected to the real world

• No way to ensure connections between experiments/real-world manipulations and record

• Specific characteristics of real-world objects matter, a great deal (e.g. patients, genes, etc)

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Problem #3: Knowledge is not connected to the real world

• No way to ensure connections between experiments/real-world manipulations and record

• Specific characteristics of real-world objects matter, a great deal (e.g. patients, genes, etc)

• Sometimes essential details are lost in statistical manipulation, pulling out certain features, etc.

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So what is missing?

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So what is missing?1.Lexapro example: we need to be able to trace claims

throughout the evidence base

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So what is missing?1.Lexapro example: we need to be able to trace claims

throughout the evidence base

2.Drug-drug interaction example: we need to access actionable content from papers

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So what is missing?1.Lexapro example: we need to be able to trace claims

throughout the evidence base

2.Drug-drug interaction example: we need to access actionable content from papers

3.Antibodies example: we need to know which real-world objects the experiment was done on

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So what is missing?1.Lexapro example: we need to be able to trace claims

throughout the evidence base

2.Drug-drug interaction example: we need to access actionable content from papers

3.Antibodies example: we need to know which real-world objects the experiment was done on

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Knowledge context

So what is missing?1.Lexapro example: we need to be able to trace claims

throughout the evidence base

2.Drug-drug interaction example: we need to access actionable content from papers

3.Antibodies example: we need to know which real-world objects the experiment was done on

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Knowledge context

Research context

So what is missing?1.Lexapro example: we need to be able to trace claims

throughout the evidence base

2.Drug-drug interaction example: we need to access actionable content from papers

3.Antibodies example: we need to know which real-world objects the experiment was done on

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Knowledge context

Research context

Real-World context

Some projects addressing this:

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Some projects addressing this:• Knowledge context: manually trace and link

evidence:–Data2Semantics: trace evidence for clinical guidelines–DIKB: trace heritage of Product Insert information

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Some projects addressing this:• Knowledge context: manually trace and link

evidence:–Data2Semantics: trace evidence for clinical guidelines–DIKB: trace heritage of Product Insert information

• Experimental context: share workflow representations:–Workflow4Ever: share workflows–Yolanda Gil’s workflow design: share abstract workflows

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Some projects addressing this:• Knowledge context: manually trace and link

evidence:–Data2Semantics: trace evidence for clinical guidelines–DIKB: trace heritage of Product Insert information

• Experimental context: share workflow representations:–Workflow4Ever: share workflows–Yolanda Gil’s workflow design: share abstract workflows

• Real-world context: manually look up the entities–NIF Antibodies registry–Pharmapendium

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BUT:

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BUT:• This is all manual: doesn’t scale• It is all done after the data is already buried• Papers act as if they are independent entities:

we are not using the social, semantic web!

• Problem: the myth of the standalone article.16

What do we need?

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[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/network-enabled-research/ ‘

What do we need?

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[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/network-enabled-research/ ‘

What do we need?

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[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/network-enabled-research/ ‘

Internet of things: (Bleecker, [1])Interact with ‘objects that blog’ or ‘Blogjects’, that:track where they are and where they’ve been;have histories of their encounters and experienceshave agency - an assertive voice on the social web [2]

What do we need?

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[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/network-enabled-research/ ‘

Internet of things: (Bleecker, [1])Interact with ‘objects that blog’ or ‘Blogjects’, that:track where they are and where they’ve been;have histories of their encounters and experienceshave agency - an assertive voice on the social web [2]

What do we need?

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[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/network-enabled-research/ ‘

Internet of things: (Bleecker, [1])Interact with ‘objects that blog’ or ‘Blogjects’, that:track where they are and where they’ve been;have histories of their encounters and experienceshave agency - an assertive voice on the social web [2]

Research Objects: (Bechofer et al, [2])Create semantically rich aggregations of resources, that can possess some scientific intent or support some research objective

What do we need?

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[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/network-enabled-research/ ‘

Internet of things: (Bleecker, [1])Interact with ‘objects that blog’ or ‘Blogjects’, that:track where they are and where they’ve been;have histories of their encounters and experienceshave agency - an assertive voice on the social web [2]

Research Objects: (Bechofer et al, [2])Create semantically rich aggregations of resources, that can possess some scientific intent or support some research objective

What do we need?

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[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/ 2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA. http://precedings.nature.com/documents/4626/version/1[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/network-enabled-research/ ‘

Internet of things: (Bleecker, [1])Interact with ‘objects that blog’ or ‘Blogjects’, that:track where they are and where they’ve been;have histories of their encounters and experienceshave agency - an assertive voice on the social web [2]

Research Objects: (Bechofer et al, [2])Create semantically rich aggregations of resources, that can possess some scientific intent or support some research objective

Networked Knowledge: (Neylon, [3])If we care about taking advantage of the web and internet for research then we must tackle the building of scholarly communication networks. These networks will have two critical characteristics: scale and a lack of friction. [3]

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Towards networked knowledge:

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Towards networked knowledge:Real-World context:

• Networked Objects in the lab that store our interactions with them and their experiences

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Towards networked knowledge:Real-World context:

• Networked Objects in the lab that store our interactions with them and their experiences

• Easy ways to author our experiences with these tools

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Towards networked knowledge:Real-World context:

• Networked Objects in the lab that store our interactions with them and their experiences

• Easy ways to author our experiences with these tools

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Towards networked knowledge:

Research context:

Real-World context:

• Networked Objects in the lab that store our interactions with them and their experiences

• Easy ways to author our experiences with these tools

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Towards networked knowledge:

Research context:

Real-World context:

• Networked Objects in the lab that store our interactions with them and their experiences

• Easy ways to author our experiences with these tools

• Tools to create Research Objects and allow us to describe our actions and observations

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Towards networked knowledge:

Research context:

Real-World context:

• Networked Objects in the lab that store our interactions with them and their experiences

• Easy ways to author our experiences with these tools

• Tools to create Research Objects and allow us to describe our actions and observations

• Repositories for Research Objects with unique IDs, provenance, persistance

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Towards networked knowledge:

Research context:

Real-World context:

• Networked Objects in the lab that store our interactions with them and their experiences

• Easy ways to author our experiences with these tools

• Tools to create Research Objects and allow us to describe our actions and observations

• Repositories for Research Objects with unique IDs, provenance, persistance

• Infrastructure to connect all of this, traverse it

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Towards networked knowledge:

Research context:

Real-World context:

• Networked Objects in the lab that store our interactions with them and their experiences

• Easy ways to author our experiences with these tools

• Tools to create Research Objects and allow us to describe our actions and observations

• Repositories for Research Objects with unique IDs, provenance, persistance

• Infrastructure to connect all of this, traverse it• Meta-analyses and visualisations to make sense of it.

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Towards networked knowledge:

Research context:

Real-World context:

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Knowledge context?

• We need: – A way to represent ‘Research Thoughts’: Observational and

Interpretational assertions – To link them together in a meaningfully, approaching the richness

of natural language– Tools to comment on other people’s (networked) thoughts, vet

them, judge them, contradict/confirm– Interfaces to link knowledge back and forth through time/

argument and oversee arguments

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Knowledge context?

• We need: – A way to represent ‘Research Thoughts’: Observational and

Interpretational assertions – To link them together in a meaningfully, approaching the richness

of natural language– Tools to comment on other people’s (networked) thoughts, vet

them, judge them, contradict/confirm– Interfaces to link knowledge back and forth through time/

argument and oversee arguments• We have:

– Some interfaces– Some tools to pull out assertions

19

Knowledge context?

• We need: – A way to represent ‘Research Thoughts’: Observational and

Interpretational assertions – To link them together in a meaningfully, approaching the richness

of natural language– Tools to comment on other people’s (networked) thoughts, vet

them, judge them, contradict/confirm– Interfaces to link knowledge back and forth through time/

argument and oversee arguments• We have:

– Some interfaces– Some tools to pull out assertions

•

19

Knowledge context?

DOMEO: Annotating claims

20

DOMEO: Annotating claims

20

DOMEO: Annotating claims

20

DOMEO: Annotating claims

20

DOMEO: Annotating claims

20

Finding ‘Claimed Knowledge Updates’

21

Knowledge context - we need representation of text!

• Some tools exist, but...• We need a) models and b) tools that create ROs and

graft a structured narrative on top of that• Containing the richness of models, thoughts,

serendipity, associations... • Mostly: we need reasons for people to do this and

rewards for them to do so• Some examples of enforced structure: grant proposals,

data plans

22

Summary:

23

Summary:• Current way of publishing does not suffice!

23

Summary:• Current way of publishing does not suffice!• We need networked knowledge to provide:

–Real-world context–Experimental context–Knowledge context

23

Summary:• Current way of publishing does not suffice!• We need networked knowledge to provide:

–Real-world context–Experimental context–Knowledge context

• What do we have: –Real-World/Experimental: technologies, but no practice–Knowledge: some tools to encode this manually and

help pull out semi-automatically

23

Summary:• Current way of publishing does not suffice!• We need networked knowledge to provide:

–Real-world context–Experimental context–Knowledge context

• What do we have: –Real-World/Experimental: technologies, but no practice–Knowledge: some tools to encode this manually and

help pull out semi-automatically• What do we need:

–A framework to tie this all together–Understanding of the authors’ drivers and rewards, to

change their writing habits23

Some ideas for next steps:

24

Some ideas for next steps:• Gathering of minds: Force11.org,

BeyondThePDF, ScienceOnline, ...

24

Some ideas for next steps:• Gathering of minds: Force11.org,

BeyondThePDF, ScienceOnline, ...• People and ideas are converging but there is

no real driver to collaborate

24

Some ideas for next steps:• Gathering of minds: Force11.org,

BeyondThePDF, ScienceOnline, ...• People and ideas are converging but there is

no real driver to collaborate• Need to develop use cases, for instance: drug-

drug interaction experiments?

24

Some ideas for next steps:• Gathering of minds: Force11.org,

BeyondThePDF, ScienceOnline, ...• People and ideas are converging but there is

no real driver to collaborate• Need to develop use cases, for instance: drug-

drug interaction experiments? • Very interested to work on this: happy to

discuss!

24

Some ideas for next steps:• Gathering of minds: Force11.org,

BeyondThePDF, ScienceOnline, ...• People and ideas are converging but there is

no real driver to collaborate• Need to develop use cases, for instance: drug-

drug interaction experiments? • Very interested to work on this: happy to

discuss!

24

a.dewaard@elsevier.com

Acknowledgements:• Collaborations:

–DOMEO: Paolo Ciccarese, Tim Clark, Harvard–Data2Semantics: Rinke Hoekstra, Paul Groth, VU–DIKB: Rich Boycer, Jodi Schneider, Maria Liakata–Provenance and experimental modeling:

Gully Burns, Eduard Hovy, Yolanda Gil, ISI–Linked Data Integration: Joanne Luciano, Deborah

McGuiness, John Erickson, RPI–Claimed Knowledge Updates: Agnes Sandor, Xerox

• Discussions: –Phil Bourne, Cameron Neylon, Dave De Roure,

Carole Goble, Brad Allen, Maryann Martone, Sophia Ananiadou 25