KO | search

Your organization is not the Internet

    1 comment 
Estimated reading time 7–11 minutes

Many people find it very difficult to understand why search within an organization can’t “just be like Google”. This is often because they haven’t thought about the differences between an organization and the Internet.

Your organization is smaller than the Internet

Search engines like Google work because they have access to big data. Google gets billions of searches to process, from billions of users. Even if your organization is a large one, it won’t have that many users either searching or contributing content, so it cannot number crunch on the same scale as Google. Your IT department is probably a lot smaller than Google’s and your enterprise search team’s daily budget is unlikely to cover more than the tiniest fraction of what Google spends. Last, but by no means least, your organization doesn’t have as much content as the Internet, so it probably needs to be far more careful about not losing any that is valuable.

Surfing the net is not many people’s job

There are important differences between how and why people search when they are at work and when they are not, and between how and why they search the Internet and their organization’s Intranet or archives. People rarely surf their organization’s Intranet for fun, to be entertained, or to while away the time. The differences in serious research behaviour and leisure searching are well documented, so I am going to write about another aspect of differences between the Internet and organizations that is often overlooked.

Putting stuff online is not the same as writing a business report

There are vast differences in the ways that people create and curate content on the Internet and within an organization. These differences have a significant effect on the way search functions. The key difference is in how much they link their content to that of others. Of course, there are people whose jobs are to create and curate online content – all the web editors, content strategists, copywriters, social media marketers, etc. – but they will be the first to explain that they have a very specialised set of skills focused on making their content searchable, commercial, or otherwise user friendly. They do a whole lot of things that most people as part of the day job neither know how nor have the time to do.

Links are a form of Knowledge Organization that Google gets for free

One of the key things that web professionals and unpaid web enthusiasts do with their content is to add and manage links. Links are what organize the web. Links are what group sites into clusters by content. Links are the web’s classification scheme. Clay Shirky back in 2005 said “there is no shelf” but it makes just as much sense to think of millions of shelves – infinite shelves going off in all directions, with new ones being created and old ones being discarded. The web is not linear – like a shelf – but it is not without structure. Google effectively picks one of the near infinity of shelves and offers it up as a linear list whenever you do a search. It chooses the shelf that seems to be the most popular, or that fits its commercial model. First on the shelf is often a paid-for advertisement or a Wikipedia entry, followed by other big well-established commercial sites. Out there on the Internet, people do an awful lot of shopping, and not much work, so that’s fine. (If they are doing more shopping than work when they are at work, your organization probably has bigger problems than search to deal with.).

For many other searches, especially more thematic research, people would be disappointed with the results, were it not for the magic of the way the web works – the links. As long as Google slings a site at you that has lots of links to other sites, it doesn’t have to take you straight to what you want, it lets you and the links do the rest of the work. Links gather together similar content, so they function like a classification scheme. The links associate content that is aimed at similar audiences, is on similar topics, is of a similar age. The links represent a huge amount of sorting, cataloguing, and classification work. Google did not have to pay for this work (genius business model). People do this work for Google for free. They do this work as part of creating and curating their content.

Many of Google’s volunteer librarians do this work for fun. They create fan sites, they write Wikipedia articles, they produce lists and generate indexes to their favourite content. They provide cataloguing descriptions and context. They do all this work partly because they enjoy it and partly because they hope to get “repaid” by their site becoming popular. They hope this will either lead to monetary reward (their band will get signed, they’ll get a better job, they’ll sell advertising) or social reward (they’ll make online “friends”, get positive feedback from comments, etc.).

From the commercial angle, people do this work because they expect to gain financial reward. They want to sell more products and make money. This is why there are howls of pain whenever Google tweaks its algorithms. Companies that balk at investing in internal search systems will spend fortunes chasing SEO.

Are your staff content curators?

If you want your organization’s search to be “just like Google” you need to think about how linked your content is. Do people who create content in your organization do so for the same reasons and with the same motivations as people create and link content on the web? It is very unlikely that you have lots of “fans” who will spend their free time creating lists of your companies’ best information resources, or collecting and rating and reviewing reports and documents. Most employees are too busy getting on with their day jobs to spend office hours pursuing their “fan” projects. Even if your staff have plenty of spare time, how many of them are big enough fans of some aspect of work to treat it like a hobby? If you want people to start looking out for similar documents on your Intranet and linking their own documents to them, you will probably have to find ways of motivating them to do this as a special initiative. It is not likely to come “for free”, like it does for the web search engines.

For some organizations, encouraging and incentivising “fan”-type behaviour may work. If the organization already has a strong collaborative culture, with people sharing ideas and using social media, it may be a small step to get them to think of their documents and presentations as blog posts. Including content creation and curation in people’s job roles and rewarding those who do well will foster a link-rich Intranet. By recognising and rewarding people who promote useful links and lists and get them to rank highly in your enterprise searches, you could bring an element of gamification to encourage this sort of behaviour. For other organisations, the culture may support this kind of web-style content creation, but people are generally too busy, have skill sets too far from what is required, or need training and encouragement. In such organizations it may make sense to have the equivalent of web editors, content strategists, user experience specialists, search engine optimizers, etc. working with the organization’s internal content to promote the most valuable resources. In other words, layer of “linkers” who work alongside the content originators.

For other organizations, where it would be inappropriate, too time consuming, or too far from established culture to encourage web-like information behaviour, enterprise search will never work “just like Google”. More formal and standardized metadata management processes are likely to be needed. Organizations that generate a lot of very specific content that is unlikely to be useful in broader contexts, confidential content, or large volumes of very similar structured content are likely to find it hard to move away from directed and standardised searching.

Many organizations will have a “mixed economy” with different types of content and different departments operating with different styles (e.g. what works in a marketing department is unlikely to work in the same way in a finance department).

Without links, search is a lot of dead ends

Without links, each search result is isolated. This stops the searcher in their tracks and means they cannot surf in the way they do on the Internet. They will have to check search results one after another in a linear fashion. If your search engine is not getting the most relevant results to the top of that list, your staff will be spending a huge amount of time working their way through that list. They cannot plump for one likely looking result then follow the trail of links, as they do on the web. The links as a form of classification do not exist, so you need another mechanism (taxonomy, ontology, index, directory) to help people find groups of related content and browse through from one document to another.

So, even though you may have the technology and the budget to match Google’s, unless your content creators are linking freely, you will never completely succeed in turning your Intranet into a mini-Internet.

Top
information management

Big Data

    2 comments 
Estimated reading time 6–9 minutes

I recently attended a gem of an event at the British Computer Society. There were three top class speakers and great networking with interesting people afterwards. I am always a little concerned that I’m not “hard” IT enough for these events, but metadata and taxonomies are such a cross-domain topic I always end up in fascinating conversations.

Why Information Professionals should care about Big Data

There are huge skills shortages in Big Data, mainly on the understanding and interpretation side, rather than the IT angle. The feeling seemed to be that knowing how to build the machines to do the work is the easy bit, but understanding the meanings of data, avoiding the semantic traps and pitfalls, the interpretation of statistics, and above all the appreciation of risk in order to make sound judgement calls are far harder. I believe this is where information scientists and professionals, taxonomists and ontologists, librarians, archivists, and historians have a vital role to play. Our skills of understanding and interpretation are vital, so we need to step up and embrace Big Data collections as a new form of libraries and archives.

Big Data is scary because there is so much of it, it changes so fast, and it has the power to create feedback loops. An especial danger, and one that is familiar to classificationists (as Bowker and Star discuss in their book Sorting Things Out), is that interpretations, biases, and assumptions can be “hard coded” into raw data right from the point of collection. We know that the way you ask a survey question skews the answers you are likely to get, and which data sets you decide to collect and compare will lead you to certain interpretations above others. Make the wrong choices and the algorithms will send you down false alleyways while appearing to be coldly neutral and scientific (a theme that Jonah Bossewitch inspired me to think about). Miss a key factor, and you will make an apparently unarguable leap to a wrong conclusion.

For example, if you see some statistics that indicate a recent increase in clickthroughs to advertisements, you might conclude that your new mobile marketing strategy is a success. However, further investigation might reveal that most of those clickthroughs don’t result in a sale. If you look again you might see that most of the clickthroughs are followed by people immediately reverting back to the site they were on before, making it far more likely that they are just accidentally clicking on adverts. If you refine the results to identify those who were using a specific new device, it seems likely most of the hits were because people were getting used to an unfamiliar touchscreen. Your apparently successful marketing strategy might in fact just be annoying your users.

How Big Data is changing the world

The first speaker, John Morton, Chief Technology Officer, SAS UK, described how increases in processing power have meant that data capture, collection, and analysis are now taking place on a mind-boggling scale at incredible speeds. One example of how this has changed over the last decade is a processing job that used to take 167 hours can now be carried out in 84 seconds. At the same time, huge amounts of unstructured and unmanaged data that used to be uncollectable ephemera are now being stored and can be analysed. He spoke of an urgent need for “data curators” and suggested they would be the librarians of the future, as without management and curation the value of the data is lost. Organisations are typically only manging 5% of their data. Issues familiar to librarians and archivists, such as the importance of metadata, quality tracking, and provenance, are vital in a Big Data world, where the quality of your analysis depends on the quality of the data you mine. Much of this quality lies within the structure of the data and its metadata.

Big Data and Artificial Intelligence

Peter Waggett, Emerging Technology Programme Leader, IBM, talked about how Big Data analysis was used to power the “intelligence” of Watson, the computer that won the US quiz game Jeopardy, beating human contestants. The mistakes Watson made were in many ways more interesting than the questions it answered correctly, as was the speed of answering. Watson always needed some processing time, whereas humans were much faster when they just “knew” the answer.

Although Watson just seems like fun, there are many examples of how Big Data techniques can be used in practice. The retail industry is one of the leaders in Big Data analysis, using data on shopping behaviour gathered from sources like loyalty cards and online shopping orders. Some companies are now using RFID chips (e.g. clothing company Zara) to gather data about the physical movement of products.

(I wondered if retailers were leaping ahead because they can use Big Data to reap rewards with comparatively little risk. In retail, there are huge profits to be made by better tailoring stock to consumer demands, but mistakes are generally not disastrous – a product line that doesn’t sell well and has to be dropped is not a new problem and is one that business models and buyers’ expertise already make allowances for.)

Another example of Big Data analysis is the football team AC Milan, where analysing data about players’ physiology and movements has helped predict injury rates and manage players in order to minimise risks.

The Internet of Things is going to generate even more Big Data and understanding its applications in new arenas – sporting or otherwise – is going to be a huge challenge for managers of the future.

Big Data and Bias

Brijesh Malkan, Founding Partner, EGO Ventures, highlighted some issues to be resolved as we move into a Big Data age. The nature of privacy is changing as oceans of personal data sweep around the world (Facebook already knows you), and so organisations are going to need transparent ethical policies about handling such data. We have reached our “cognitive limits” with so much data to read and so information visualisation is going to be of increasing importance. Data quality also needs to be managed if data mining techniques are to be effective and if algorithmic processing is to produce sensible, useful results. Brijesh talked about “Bayesian filters” and “cognitive scrubbers” to help compensate for biases in data, whether these biases are embedded in the data capture process, in the choices of data used, in the algorithms processing the data, or ultimately in the decisions made by the humans who are interpreting the data.

He spoke of the need for more understanding of psychology, especially of groupthink, echo chambers, and risk perception. Financial markets in particular are prone to “stampede” behaviour, creating bubbles and panics in markets. Data mining of social networking can be prone to creating feedback loops and encouraging risky behaviour. He also spoke of a desperate shortage of people who understand statistics and probability, even within the scientific community.

(This reminded me of the question Patrick Lambe raised at the ISKO UK conference in the summer, asking how information professionals can do something useful for science and for society. Understanding how to interpret and capture data and account for biases, explaining how easy it is to manipulate people’s perceptions through the way information is presented, and teaching how knowledge requires judgement as well as number crunching would seem to be skills that we can offer to the Big Data world already.)

Top
culture | search | semantic web

Data Ghosts in the Facebook Machine by Fantasticlife

    1 comment 
< 1 minute

Understanding how data mining works is going to become increasingly important. There is a huge gap in popular and even professional knowledge about what organisations can now do “under the surface” with our data. For a very clear and straightforward explanation of how social graphs work and why we should be paying attention read Data Ghosts in the Facebook Machine.

Top
culture | Digital Asset Management | information architecture | KO | search | semantic web

Holodecks, marketing, and crime scenes – the DAM link between different worlds

    Start a conversation 
Estimated reading time 4–6 minutes

In the last two weeks I have attended three very different conferences, with DAM as the common thread. The first was Media Pro Expo, where I spoke on a panel with the DAM Foundation, alongside Mark Davey, Madi Solomon, and David Lipsey. The second was Createasphere‘s first European DAM conference, and the third (co-located with the Createasphere event) was the SPAR Europe Conference on 3D Imaging and Data Management for Engineering, Construction, Manufacturing, and Security.

The contrast between Media Pro and SPAR, and their respective audiences, was striking, but so were the similarities of the problems they faced, such as the common need to manage rich media assets and huge volumes of data. Media Pro was aimed at marketing companies, and had lots of amusing exhibits showcasing ways of using technology to create engaging and entertaining campaigns. (I enjoyed playing with an interactive magazine cover linked to a camera that allowed you to put your picture “on the cover” and select your favourite headlines.) Marketing companies are concerned with keeping, curating and mining data not just about customers’ contact details, but also their likes, social connections, and shopping habits in order to create personalised campaigns, so they have become great consumers of metadata.

3D Imaging and Data Management

SPAR was all about scanning and mapping, not in the sense that I am familiar with, but literally surveying the Earth and making maps. There were companies that use lasers to create roadmaps, others that carry out aerial surveys, and some that create 3-D representations of buildings. There are systems for surveying and modelling building sites to make sure that construction avoids sewers, pipes, and underground cables, and even a system for creating 3-D photosets of crime scenes to help the police in investigation and evidence gathering.

Createasphere

At Createasphere I talked about managing metadata in complex information environments and how we need to treat metadata as content in its own right. There were a range of excellent and diverse presentations, covering topics from the potential of immersive virtual worlds and the huge volumes of data they produce, to descriptions of technical metadata exchange projects.

I began to think about the crossover point between the creativity and imagination of the media and marketing companies and the power and accuracy of the surveying companies and how this is going to bring about hugely powerful fantasy “Holodeck” worlds that will make Second Life and the Sims look quainter than the Mickey Mouse cartoons of the 1930s.

Better than the real world

One challenge for information professionals is to think about how we can create navigation and search systems that do more than just replicate the real-world paradigms we are used to at the moment – I am thinking of things like road signs and timetables – but how to harness the best of semantic techniques and data mining processes to create reactive intuitive worlds that work better than the real one. Ed Lantz of Vortex Immersion Media spoke of “intelligent spaces” that automatically access our data, our assets, information about us, and arrange themselves to suit us. How do we prepare for a world when the likes of Apple’s speech recognition system Siri aren’t genies in bottles, but are the environment around us? We used to worry about ghosts in the machine, but will we end up as the ghosts inside the machine? We worry about putting our assets out there into the cloud, but perhaps we should be thinking more about what it will be like when we step inside the cloud or bring the cloud into our homes?

There was a post circulating on Twitter recently describing the library of the future as a hellish place where characters from books come alive and stalk the readers in the rooms. It was somewhat derided as a childish joke, but if we create Holodecks and then try to live in them, it could well come true. The implicit warning it contains that we could inadvertently trap ourselves in such a hellish place where privacy, rights, control, and manipulation are so hidden from view that we lose our sense of self seems to be very mature and insightful. Another post I read was about how interface designers are currently working on “pictures under glass” and need to start to use the full tactile, haptic, and 360 degree expressivity of our physical bodies, such as we are beginning to with technologies like the Wii and Kinect.

Making work fun

Theresa Regli of the Real Story Group pointed out that the world we are in now is one in which people still don’t grasp the importance of labelling their images, so immersive virtual worlds seem a long way off, but she also talked of the need for corporate interfaces to embrace “gamification”, as employees are far more productive when their jobs are fun. It may take some time, but I like the idea of a Holodeck meeting room where people make presentations and collaborate on plans by dancing around, rather than sitting staidly at a table. Rather than the hellish library where AI brings fictional monsters to life, it might turn out to be a lot of fun and all that movement may even be good for our health!

Top
archives | KO | semantic web

More than a schedule, give me an index

    Start a conversation 
Estimated reading time 4–6 minutes

People have started to talk about the death of the schedule, often in the context of complaining that broadcasters are ill-prepared for this inevitability and schedulers complaining that no-one appreciates their skills in placing programmes appropriately and in context. One example is “hammocking” – making sure that viewers receive a “varied diet” across an evening, perhaps placing the news between two lighthearted pop culture programmes.

Meanwhile, the anti-schedulists point out that given the choice, some people will download and watch an entire series in one marathon session (people have “Torchwood weekends”), so that they don’t have to commit to being in front of the TV at 9pm every Thursday, or will watch a film broken down into 20 minute sections on their mobile phone while commuting. Schedulism and anti-schedulism can seem like major culture clash, but is easily resolved when you think purely in terms of knowledge organisation.

A schedule is just metadata

A schedule is merely a set of metadata about programmes. It used to be the most important set of metadata for most people (along with the programme title!) as it was the key to not missing the programme. Now that we have catchup services and archives, knowing exactly when a programme will be broadcast or was broadcast may be less significant for finding that programme again, leading some people to claim that schedules are no longer needed. However, there are plenty of people who don’t want to look for specific programmes but want to sit down and be entertained for the evening. For them, schedules remain vital as they outline what is available. Scheduling in this sense is editorial selection, with all the craftsmanship and judgement that implies.

People are fascinated to know what was broadcast on the day they were born, and which programmes went out together, and schedules offer all sorts of socio-political and cultural information, giving snapshots of what were popular topics or contentious issues over time.

Schedule data is less significant in a vast online digital archive, but it is still useful. For example, you might want to find an episode you missed in a long-running series. You probably won’t know that it was episode 12 of 26, but you might remember that the reason you missed it was because you were out celebrating a friend’s birthday, which is a date you know. This may be a lot quicker than reading through the episode descriptions, which are usually too vague to be helpful, as the writers don’t want to give away “spoilers”, such as the final cliffhanger, which is often the part of the episode you remember the best. The programme descriptions are intended to entice you to watch the programme, not help you work out whether or not you have already seen it.

Don’t ditch the schedule, add to it

What is important to bear in mind is that digital archives can offer schedule data almost effortlessly, but can offer many more metadata streams as well. These metadata streams are in many ways innovative and can lead to fascinating new ways of grouping programmes and promoting content. Rich subject metadata (such as a subject index) becomes an engine by which you can drive all sorts of automatically created content channels. You can group programmes by theme or topic as well as series and genre. So you don’t have to rely on when something was shown, you can use an index to gather together all programmes about fishing, or harpsichords, or the miners’ strike – bringing together documentaries (Heart of the Matter, Panorama), news and current affairs (also Question time, Newsnight, even The Money Programme), as well as plays (The Price of Coal), or even comedies (The Comic Strip Presents.. The Strike).

Such subjective metadata also gives you extra contextual information, for example in the case of the Miners’ Strike, it shows you that there were miners’ strikes in 1921, 1926, 1955, 1972, 1974, 1981, as well as in 1984, and that miners around the world have gone on strike at various times. This historic perspective is hard to pick out from schedule data. (Even if you could see programmes about miners’ strikes had been broadcast in these years, you would have to do further research to find out if they were covering contemporary events.) If the programmes have such metadata attached, anyone – any user of the archive – can effectively build rich personalised channels on their favourite topics or themes, and share those with others who have similar interests.

Metadata advertises content

If the metadata is in a Linked and Open format, the associative trails can wander beyond your collection to others, reaching new audiences, perhaps via social networks. This releases the “long tail” of content that is otherwise hard to find and re-use, as well as putting popular content into context. Making your metadata available more widely means more people will have more and more routes in to exploring your archive, even if you choose to restrict this to in-house teams or paying subscribers.

Either way, if you want to sell individual programmes or parts of programmes, knowing not just when you transmitted them but knowing exactly what they are about – via the rich semantic metadata you have added – offers a very useful sales and marketing tool.

Top
KO | semantic web

Transforming and extending classification systems – UDCC Seminar

    Start a conversation 
Estimated reading time 2–3 minutes

This post is the last in a series about the UDC consortium international seminar in The Hague, 19-20 September, 2011

Joan S. Mitchell, OCLC (USA), and Marcia Lei Zeng, Kent State University (USA), supported by Maja Žumer, University of Ljubljana (Slovenia), talked about extending models for controlled vocabularies to classification systems: modelling DDC with FRSAD, which led to interesting discussions about their concepts of “nomen” and “thema”.

Along with my former colleague Andy Heather, now CTO at DODS Parliamentary Communications Ltd, I talked about our work on the data migration of classifications from a legacy database into new taxonomy management software, presenting our paper: Transformation of a legacy UDC-based classification system: exploiting and remodelling semantic relationships.

Conclusions

The key ideas I took away from the conference were:
1) Classifications and ontologies are not an either/or choice. They have different properties and different strengths and weaknesses and so should be chosen according to the task in hand.
2) It is difficult to turn a classification into an ontology, but easy to turn an ontology into a taxonomy, so if you don’t have either to start with and can’t decide, an ontology is a safer bet. If you already have a classification, you need to think carefully about whether it is worth turning it into a fully modelled ontology, as converting it to RDF or SKOS is likely to be much easier. However, at the moment, RDF and SKOS have limitations, especially in handling faceted taxonomies, so beware of losing semantic richness in the conversion process. Polyhierarchies offer a way of expressing facets in SKOS.
4) Vocabulary control and alignment continue to be significant issues for the Semantic Web.
5) Ontology curation, management, and semantic alignment will be increasingly important issues for the Semantic Web.

Slides and audio recordings of all 21 talks can be now downloaded from the conference website.

Conference proceedings are published by Ergon Verlag and can now be
purchased/ordered online from http://seminar.udcc.org/2011/php/proceedings.php.

Top
KO | semantic web

Classification and ontology in specific subjects – UDCC Seminar

    Start a conversation 
Estimated reading time 3–5 minutes

Day two of the UDC consortium international seminar opened with two subject-specific talks – Wolfram Sperber described a classification of mathematics and Andrew Buxton showed how similar chemistry classification and ontologies are, using the ChEBI ontology. He also described the different ways classifications and ontologies could be used to support each other and about the lack of good graphical tools and visualisations to represent ontologies.

Categories and relations: key elements of ontologies – Categorial Distinctions

Roberto Poli, University of Trento (Italy) talked about the compliexisties of part-whole relationships. There are simple wholes, composed of a sum of their parts, but some parts of wholes cannot simply be added together – for example, the social, psychological, and physical aspects of a person. He also discussed the difference between science as epistemological – dealing with what can be known – and ontological – deraling with what exists.

Towards a relation ontology for the Semantic Web

Dagobert Soergel made a bold claim that the only way for the Semantic Web to deliver its promise is if we adopt a relation ontology and map each dataset to the standard, to allow interoperability. He pointed out that you “do not getting semantics from syntax alone”.

Relations in the notational hierarchy of the Dewey Decimal Classification

Rebecca Green from OCLC described the difficulties encountered when trying to automatically create ontologies from the Dewey Decimal Classification. These included semantic differences in the way subclasses had been defined, meaning that no single rule would handle them all appropriately.

Modelling concepts and structures in analytico-synthetic classifications

The eminent Ingetraut Dahlberg compared Aristotle and Ranganathan’s key facets and UDC and Colon Classification systems. She also presented a survey of academic subject areas analysed into facets.

Representing the structural elements of a freely faceted classification

Claudio Gnoli of the University of Pavia, talked about freely faceted classifications, in comparison with systems such as UDC. He emphasised the urgency of publishing classifications on line, but highlighted the limitations of SKOS and OWL to fully expressed faceted systems despite the fact that faceted systems are extremely good tools for obtaining precise search results. Faceted systems are also excellent for combining information across disciplines, allowing you to combine aspects of one subject areas with aspects of a different one, and interdisciplinarity is becoming increasingly important as an approach, as innovation often happens at the boundaries between disciplines.

He pointed out that a polyhierarchical approach can be modelled in SKOS as a way of representing facets, but that this approach is often overlooked. He also called for more work to be done on SKOS so that it can represent facets directly.

Facet analysis as a tool for modelling subject domains and terminologies

Vanda Broughton, University College London, offered the Bliss Classification as a useful tool for online subject classification, but called for help in how best to publish it for general use. Should it be released as a text document, database, or should work be done to convert it to an ontology – and if so, in what form?

She stressed how the logical approach of facet analysis and regular syntax makes it predictable and hence ideal for machine manipulation.

Analytico-synthetic approach for handling knowledge diversity in media content analysis

Devika P. Madalli, Indian Statistical Institute, DRTC (India), described the Living Knowledge project that used an analytico-synthetic approach in order to bring together around useful themes diverse content from different sources using varied means of expression. This supported a rich faceted search system.

Slides and audio recordings of all 21 talks can be now downloaded from the conference website.

Conference proceedings are published by Ergon Verlag and can now be
purchased/ordered online from http://seminar.udcc.org/2011/php/proceedings.php.

Top
KO | search | semantic web

Classification meets the Web – UDCC Seminar 2011

    Start a conversation 
Estimated reading time 2–2 minutes

This post is 4th in a series about the UDC consortium international seminar in The Hague, 19-20 September, 2011.

Interoperability of knowledge organization systems with and through ontologies

Daniel Kless from the University of Melbourne pointed out that problems with ontologies arise when combining them, as errors in combination can have disastrous effects on subsequent reasoning. A well-defined modelling method is needed to minimise this. Standards such as OWL and RDF do not address the problems of methodology or terminology control.

Towards the integration of knowledge organization systems with the linked data cloud

Vincenzo Maltese of the University of Trento, Italy, explained how it is vital to make clear the semantics and purpose of any ontology when attempting to share Linked Data. Ontologies may differ in their scope, purpose, structure, terminology, language, coverage, formality, and conceptualization. He drew a distinction between descriptive ontologies and classification ontologies. It is very easy to convert a descriptive ontology to a classification ontology and the process can be automated, but extremely difficult to convert a classification ontology to a descriptive one and the process requires human intellectual and editorial effort.

Classification and reference vocabulary in linked environment data

Joachim Fock of the Federal Environment Agency (Germany) talked about how they transformed their keyword thesaurus to a Linked Data format.

Top
KO | semantic web

Classifications and ontologies on their own terms – UDCC Seminar 2011

    Start a conversation 
Estimated reading time 2–2 minutes

This post is the third in a series about the UDC consortium international seminar in The Hague, 19-20 September, 2011.

Approaches to providing context in knowledge representation structures

Barbara Kwasnik, Syracuse University (USA), talked about ways that context can be used as a disambiguation tool, and described different kinds of contexts: warrant, scientific, educational, cultural, etc. However, interdisciplinary approaches can be difficult. It is easy to have different ontological commitments, but you need a mapping to know when and which bits need to work across domains. Ontologies will need updating as the world and world views shift and change, so we need ways of defining their scope, as well as provenance and mappings. There are also difficulties in establishing the neutrality of ontologies.

Interaction between elementary structures in universes of knowledge

Richard P. Smiraglia, University of Wisconsin (USA),
talked about how people want to turn the multidimensional world into a unidimensional top-down model. He pointed out that people tend to assume UDC is like Dewey, but it actually works far more like Ranganathan’s Colon Classification. He called for new theories of organizing knowledge in shifting contexts and theories about how to mediate between concepts and structures like UDC.

Demystifying ontology

Emad Khazraee, Drexel University (USA), talked about how ontological approaches are as old as literature itself, showing a picture of what I think was the ancient Sumerian king list. He talked about boundary objects and the overlap between different academic areas that are interested in knowledge organisation and learning. He also discussed the differences between ontology-as-categorial-analysis and ontology-as-technology.

Top
KO | semantic web

The role of classification and ontology on the Web – UDCC Seminar 2011

    Start a conversation 
Estimated reading time 2–3 minutes

This post is the second in a series about the UDC consortium international seminar in The Hague, 19-20 September, 2011.

Knowledge Organization Systems (KOSs) as hubs in the Web of Data

In a minor change of schedule, Thomas Baker from the DCMI talked about some of the practical issues with using Linked Data. Provenance data can be recorded as additional information but it is not standardised or an integral part of RDF and this is something that is a growing concern, receving attention from W3C. URI persistence and alignment remain concerns for data managment and governance.

Aligning web vocabularies

Guus Schreiber also dealt with the problem of making sure we are all talking about the same thing when we try to align our vocabularies. He called for ontologists to be modest about what they can achieve and not to try to hide the problems that occur when you try to transfer an ontology form one domain to another. Errors typically occur due to failures to notice subtle differences between domains.

Vocabulary alignment is a complex business that requires a lot of intellectual effort and multiple techniques should be used to reinforce and support each other. It is much better to map small vocabularies to large ones that can then act as “pivots”.

There is still no adequate methodology for evaluating alignments nor for mediating consensus between observers. Perhaps there should be a way of recording the strength of consensus and the presence of disagreements and alternative views.

Classification, Collaboration and the Web of Data

Dan Brickley described three types of graph – the hypertext graph of the Internet’s links between documents, the social graph of links between people, and the factual graph of links between data. Currently Linked Data is bringing together the hypertext and factual graphs, and another step would be to add in the social dimension.

He called for a focus on what the various tools can actually do, to be wary of over-evangelical ontologists, and to remember that subject classifications are strong and robust tools that are more appropriate for many types of work than ontologies.

He said that you could expect Linked Data to solve about a third of your information linking problems.

Top
Blogroll