UKON 2016: The EPSRC ICT Theme Update

These are my notes for Miriam Dowie’s talk at the UK Ontology Network Meeting on 14 April, 2016.

 

Source https://www.epsrc.ac.uk/files/aboutus/logos-and-indentity/colour-sponsorship-logo-high-resolution/ 14 April 2016

In real terms, the funding situation for the EPSRC is flat, which is good. There are a number of factors in how the budget is built, including research council baselines, prior committments, and other factors. There will be an RCUK communication soon regarding the budget, but the bottom line is that the funding will not be stopping.

EPSRC ICT covers research into computer science, user-interface tech, communications, electronics and photonics around the common thread of new ways to transmit, present, manage, analyse, and process data. The main cross ICT priorities are TI3 (Towards an intelligent information infrastructure), MACDES (Many-core architectures and concurrentcy in  distributed and embedded systems, and others.

The EPSRC ICT Theme is in the middle of their work on refreshing their positions on individual research areas and cross-research area priorities. Why the “refresh”? There are finite resources, and the need to allow new areas to emerge and to achieve balance between priorities, flavors of resources, themes, mechanisms etc.

There aren’t any final conclusions yet, so any and all useful input is welcome. The announcement of the conclusions in December 2016. There will be sessions and workshops to assist with communication.

There is a call for evidence for universities, businesses and recognized professional bodies right now against the following headings: quality, national importance, capacity and further information.

There are Strategic Advisory Team nominations coming up soon. There are about 3000 college members for the EPSRC Peer Review College. Expression of interest are now being invited from candidates who wish to join the Associate College (there is also a Full College). Deadline is 10 May 2016, more details https://www.epsrc.ac.uk/funding/calls/associatepeerreviewcollege/ .

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

UKON 2016 Short Talks V

These are my notes for the fifth session of talks at the UK Ontology Network Meeting on 14 April, 2016.

Source https://lh3.googleusercontent.com/-Ql7RFDSgvxQ/AAAAAAAAAAI/AAAAAAAAAFA/pnoDTCze85Q/s120-c/photo.jpg 14 April 2016

The OntoEnrich Platform: using workflows for quality assurance and axiomatic enrichment of  ontologies
Manuel Quesada-Martinez, Jesualdo Tomas Fernandez-Breis, Robert Stevens, Mathalie Aussenac-Gilles, Daniel Karlsson

A lexical regularity (LR) is a group of consecutive ordered words that appear in more than one class of an ontology. An OntoEnrich workflow combines different types of filters, metrics and steps to support the user in the inspection of LRs, and in deciding how interesting they are.

A workflow might start with the calculation of the lexical analysis. Then you filter on select LRs that contain adjectives. Then you manually inspect the LRs and calculate two metrics and sort the set of LRs by that. Then you explore the LRs guided by the metrics. Try it at http://sele.inf.um.es/ontoenrich .

Probabilistic Annotation Framework: Knowledge Assembly at Scale with Semantic and Probabilistic Techniques
Szymon Kiarman, Laris Soldatova, Robert Stevens, Ross King

The methodology for knowledge assembly in this research is Reading-Assembly-Explanation. When inferences are extracted from papers, it’s not clear if it has been extracted correctly. They applied probabilistic reasoning over this and recorded evidence to improve the inferences. The PAF (Probabilistic Annotation Framework) has an ontology covering event-related concepts, metadata concepts and probability types.

The growing scope of the environmental ontology
Pier Luigi Buttigieg

EnvO is a community ontology for the environment and works with OBO and some classes mined from EOL. Environmental systems (biomes, ecoregions, ecozones, habitates), features (coral reefs, hospitals, guts, kimchi), materials and processes (carbon fixation, volcanic eruptions) are all modelled in EnvO. They remain orthologous by sharing with PCO, and using BFO, ChEBI, etc.

Because humans think they’re different from everything else, they are building an human ecosystem section and are working with SDGI in association with UNEP. SDGI is a semantic interface for the sustainable development goals.

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

UKON 2016: The Workflows of Ontology Authoring: Controlled vs. Naturalistic Settings

These are my notes for a talk at the UK Ontology Network Meeting on 14 April, 2016 by Markel Vigo, Nicolas Matentzoglu, Caroline Jay, Robert Stevens.

Source https://lh3.googleusercontent.com/-Ql7RFDSgvxQ/AAAAAAAAAAI/AAAAAAAAAFA/pnoDTCze85Q/s120-c/photo.jpg 14 April 2016

4 years ago we knew very little about how ontologist actually perform their authoring. They wanted to know about typical authoring workflows and the effectiveness of the current tool support. There were interviews with ontologists at UKON 2014 and a user study in the lab at UKON 2015 which helped identify the workflows for exploration, editing and reasoning. This work does have implications for tool design.

In a lab study, the external validity is at risk. Tasks were predefined, with a provided ontology, and they could be in an unfamiliar environment. Here, there were 16 users and times ranged between 30-75 minutes and used a modified version of Protege4US and eye tracking. Therefore they also did a remote study where people worked in their own environment and on their own ontologies. They got 7 users for this part.

They collect the raw event data from the users as log files. Then the data is cleaned and put into a CSV file. Then the same consecutive events are merged. Then they performed workflow mining through N-gram analysis. There were 9K events in the lab and 30K events remotely (doesn’t include mouse hovering events). Lab study had a dominance of entity selection, while in remote study the vast majority are the hierarchy extending events (people’s remote ontologies are larger). There was more variety in the remote setting (more heavy editing, more uncertainty in how we want to model things, more searching, more individuals and annotations). They also looked at how workflows linked together, and if one commonly preceded or followed another.

The remote study does corroborate lab study, but also extends it. The next step is to evaluate the inference inspector, and to explore other avenues, e.g. task difficulty estimation using pupillometry. Also, they’d like to cross-compare data from more than 6 independent studies.

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

UKON 2016 Short Talks IV

These are my notes for the fourth session of talks at the UK Ontology Network Meeting on 14 April, 2016.

Source https://lh3.googleusercontent.com/-Ql7RFDSgvxQ/AAAAAAAAAAI/AAAAAAAAAFA/pnoDTCze85Q/s120-c/photo.jpg 14 April 2016

Mining informative OWL axioms with DL miner
Viachaslau Sazonau, Uli Sattler

A reminder: TBox = OWL Class and property axioms, and ABox = individuals with labels and relations. A common question is “What is missing in my TBox?” The ABox might provide hints… A domain expert could scan the ontology and find the additional axioms “manually”.  DL-Miner automatically scans the ABox and generates hypotheses for the TBox.

If the hypothesis is correct, add to TBox. If incorrect, you then check the ABox as there might be something wrong there. An alternative would be go outside the ontology (to the laboratory, for example) to see if there’s another reason why the hypothesis has been suggested.

Justification and Reasoner Verification
Michael Lee, Bijan Parsia, Uli Sattler

Reasoners are vital and it is essential they are correct as they do tasks impossible to do ourselves. When there are disagreements that have occurred before (ORE) an error must have occurred. They want to find disagreements and resolve them. They will evaluate each justification for a disagreement either with a human or a reasoner.

They look at FaCT++ and HermiT. One reasoner would make a statement, and the other reasoner states whether it disagrees or not. If they can’t make a decision, it goes to a human. They did this with 4 reasoners (also Pellet and JFact) and looked at 190 ontologies. 181 had agreements on classifications, and 9 disagreements. This resulted in 1622 justifications for the disagreements. They found errors with data types and missing asserted axioms.

When you do ontology engineering, make sure you use more than one reasoner. However, reasoners are generally stable with a 95% level of agreement. In future, would be worth making a service where you can submit your ontologies to be compared across reasoners.

Antipattern Comprehension: An Empirical Evaluation
Tie Hou, Peter Chapman, Andrew Blake

Comprehension of justifications is known to be difficult for even experienced ontologists. Even with reasoners, understanding is difficult. They are trying to make things easier with visualization. Most visualization tools show only the hierarchical structure of an ontology, however incoherence in an ontology can arise from the interaction between concepts and properties. Therefore they use concept diagrams which can be viewed individually or merged.

Does visualization make it easier to examine incoherence? A set of antipattern categorizations were extracted from online TONES ontology repo. They focused only on the identification of logical contradictions. Participants using Protege statements did not perform any worse than those using diagrams. They want to extend the study to help debug ontologies. The study was performed with students with no knowledge of ontologies, and they’d like to do it again with experts.

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

UKON 2016 Short Talks III

These are my notes for the third session of talks at the UK Ontology Network Meeting on 14 April, 2016.

Source https://lh3.googleusercontent.com/-Ql7RFDSgvxQ/AAAAAAAAAAI/AAAAAAAAAFA/pnoDTCze85Q/s120-c/photo.jpg 14 April 2016

Ontology-driven Applications with MEKON and HOBO
Colin Puleston

Currently used in a sepsis prediction system and a clinical trials design / design-retrieval system (early prototypes). MEKON  is based around a generic frames model with a plugin framework which allows the incorporation of ontology-like knowledge sources and associated reasoning mechanisms. HOBO extends MEKON to enable the creation of domain specific object models, bound to appropriately populated frames models, Instantiations of object models operate in tandem with instantiations of bound frames models.

They come with instance store mechanisms, delivered via plugins. Current plugins are based on OWL DL, RDF + SPARQL and the XML database BaseX. They also come with a Model Explorer GUI to allow the model developer to browse, explore dynamic behaviour of specific instantiations, and exercise the instance store (store instances and execute queries).

MEKON and HOBO provide layered architecture (structured means of combining generic knowledge sources and reasoners, and domain specific processing), handle much of the dirty work, and provide client access via appropriate APIs (generic frames model, domain-specific object models).

MEKON enables the creation of a skeleton application without having to write a single line of code.

The Synthetic Biology Open Language
Goksel Misrili, James Alastair McLaughlin, Matthew Pocock, Anil Wipat

Synthetic biology aims to engineer novel and predictable biological systems through existing engineering paradigms. Lots of information is required, including DNA sequence info, regulatory elements, molecular interactions and more. They are currently using SBOL 2.0. Modules allow hierarchical representation of computational and biological systems. A biological example would be a subtilin sensor.

SBOL utilises existing SW resources, e.g. BioPAX, SO, SBO, EDAM, Dublin Core, Provenance Ontology. SBOL is serialized as XML. SBOL allows the unambiguous exchange of synthetic biology designs, and is developed by a large consortium.

ConSBOL: A terminology for biopolymer design
Matthew Pocock, Chris Taylor, James Alastair McLaughlin, Anil Wipat

The presenter was unable to make the workshop today.

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

UKON 2016: Modelling Dispositions

These are my notes for Alexander Carruth’s talk at the UK Ontology Network Meeting on 14 April, 2016.

Some ULOs, such as BFO, do try to model dispositions. What is a disposition? Fragility, solubility are canonical examples. Dispositions are capacities, tendencies, or causal powers (for example). They are the features in virtue of which things engage in particular causal interactions. Other examples are mass and charge.

Traditionally, the dominant account of dispositions is called the Conditional Analysis (CA). This basically says if S occurs, then O will M. S = stimulus, M = some manifestation. Example: if the vase is struck, it will break. This relationally captures a disposition’s nature as D(s,m). There have been some challenges to the CA method in recent years.

Source http://www.danielborup.com/wp-content/uploads/2013/01/Cracks1.jpg 14 April 2016

There are two ongoing debates about the nature of dispositions. The first is the Tracking Debate (single trackers versus multi trackers). This debate concerns the number and variety of manifestations that can be associated with a single disposition. Within multi tracking, there is quantitative and qualitative multi tracking. Multi trackers: Being ball-shaped has a variety of manifestations (e.g. rolling, making a dent in some clay). Therefore dispositions have multiple manifestations produced by multiple stimuli.

The second debate concerns how dispositions operate: CA assumes a stimulus-based account of how dispositions operate. The Mutual Manifestation view states that dispositions ‘work together’, with no distinction possible between the active disposition and the mere stimulus.

Therefore there are four accounts of disposition:

  • single-track stimulus manifestation (CA) D(s,m)
  • Multi-track stimulus manifestation
  • single-track, mutual manifestation D1(D2, m1)
  • Multi-track, mutual manifestation

How should we react? Monism (choose which of the four accounts to go with); pluralism (greater complexity but could pick and choose); or pragmatism (different responses for different purposes)?

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

UKON 2016 Short Talks II

These are my notes for the second morning session of talks at the UK Ontology Network Meeting on 14 April, 2016.

Source https://lh3.googleusercontent.com/-Ql7RFDSgvxQ/AAAAAAAAAAI/AAAAAAAAAFA/pnoDTCze85Q/s120-c/photo.jpg 14 April 2016

Dialogues for finding correspondences between partially disclosed Ontologies
Terry Payne and Valentina Tamma

and-

Dialogue based meaning negotiation
Gabrielle Santos, Valentina Tamma, Terry Payne, Floriana Grasso

Different systems have different ontologies, so you need to align them. Many approaches exist. There are 3 problems: different alignment systems produce different solutions; you may not want to align all of your ontology; part of that ontology might be commercially sensitive and therefore not want to expose or disclose it.

You can use 2 agents to negotiate possible alignments. Agents selectively identify what mappings should be disclosed if the agents are knowledge rich. If not knowledge rich, then the agents need to start exchanging segments of the ontology.

They have started to develop a formal inquiry dialogue that allows two agents to exchange knowledge about known mappings. If you’re looking at many different alignments, you many have many one-to-many mappings. Which mapping should be selected? Could it be resolved through objections within the dialogue? Through the dialogue each agent extends their ontology by including correspondences.

They’ve used a cognitive approach to reaching consensus over possible correspondences. Agents identify possible concepts that may be ontologically equivalent in their respective ontologies. Each then seeks further evidence. One agent asks the other agent if it can align an entity. Start with a lexical match. The second phase will then ask for evidence to support the correspondence, e.g. what are the structural similarities? The CID dialogue has been empirically evaluated using OAEI datasets.

The Cardiovascular Disease Ontolgoy (CVDO)
Mercedes Arguello Casteleiro, Julie Klein, Robert Stevens

CVDKB content: 34 publications, including human and mouse. How can we connect the biological information, e.g. HGNC, UniProt, MGI, ChEBI, miRBase? From this they have 86792 mouse proteins, 172121 proteins from human, and many metabolites and miRNAs. The CVDO reuses some ontologies, such as OBI, and parts of other ontologies including SO, PRO, GO, CL, UBERON, PATO…

There is a CVDO application which allows SPARQL queries over the OWL. They’re looking at including SOLR and elasticsearch to make searching fast, via the conversion of OWL to JSON-LD. Go to http://cvdkb.cs.man.ac.uk to try it out. The idea is to hide the ontological complexity from the end user.

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!