Tag Archives: semantic_web

How semantic search helps girls and boys, but in different ways

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Estimated reading time 2–4 minutes

While researching something else, I happened upon this rather cheering paper: The effect of semantic technologies on the exploration of the web of knowledge by female and male users. Gender issues only tangentially affect my core research, as I generally focus on linguistic communities that are defined by organizational or professional context, so gender effects are rather diluted by that point. I also prefer collapsing false dichotomies rather than emphasizing difference and division, and so I was very heartened that this article shows how semantic techniques can be unifying.

The study is based on observing the search strategies of a group of male and a group of female students in Taiwan. Given non-semantic search systems to use, the male students tended to search very broadly and shallowly, skimming large numbers of results and following links and going off on tangents to find other results. This enabled them to cover a lot of ground, often finding something useful, but also often left them with a rather chaotic collection of results and references. The female students tended to search very deeply and narrowly, often stopping to read in depth a paper that they had found, and trying to fully grasp the nature of the results that had been returned. This meant they tended to collect fewer results overall, the results tended to be clustered around a single concept, and they risked falling into the rather wonderfully named “similarity holes”. These “similarity holes” are search traps where a single search term or collection of terms leads to a small set of results and are essentially “dead ends”.

How did semantic search help?

When the students were given semantic search tools, the male students continued to search broadly and shallowly but the semantic associations helped them to conceptualize and organize what they were doing. This meant that they ended up with a far more coherent, relevant, and useful set of search results and references. In contrast, the female students using the semantic associations offered, found it far easier to broaden their searches and to come up with alternative search terms and approaches enabling them to avoid and break out of any “similarity holes” they fell into.

Gender effects dissipate

I was very heartened that improvements in technology can be gender-neutral – they can simply be improvements of benefit in different ways to everyone, they don’t have to deliberately try to account for gender difference. I was also very heartened to note that the researchers found that gender differences in search strategies dissipated once students were taught advanced information seeking and knowledge management strategies. Gender differences were only apparent in novice, inexperienced searchers. So, in information seeking work at least, any biological or socially created gender differences are weak and easily overcome with some well directed instruction and semantic techniques are a help rather than a hindrance.

Libraries, Media, and the Semantic Web meetup at the BBC

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Estimated reading time 3–4 minutes

In a bit of a blog cleanup, I discovered this post languishing unpublished. The event took place earlier this year but the videos of the presentations are still well worth watching. It was an excellent session with short but highly informative talks by some of the smartest people currently working in the semantic web arena. The Videos of the event are available on You Tube.


Jon Voss of Historypin was a true “information altruist”, describing libraries as a “radical idea”. The concept that people should be able to get information for free at the point of access, paid for by general taxation, has huge political implications. (Many of our libraries were funded by Victorian philanthropists who realised that an educated workforce was a more productive workforce, something that appears to have been largely forgotten today.) Historypin is seeking to build a new library, based on personal collections of content and metadata – a “memory-sharing” project. Jon eloquently explained how the Semantic Web reflects the principles of the first librarians in that it seeks ways to encourage people to open up and share knowledge as widely as possible.


Adrian Stevenson of MIMAS described various projects including Archives Hub, an excellent project helping archives, and in particular small archives that don’t have much funding, to share content and catalogues.


Evan Sandhaus of the New York Times explained the IPTC’s rNews – a news markup standard that should help search engines and search analytics tools to index news content more effectively.


Dan Brickley’s “compare and contrast” of Universal Decimal Classification with schema.org was wonderful and he reminded technologists that it very easy to forget that librarians and classification theorists were attempting to solve search problems far in advance of the invention of computers. He showed an example of “search log analysis” from 1912, queries sent to the Belgian international bibliographic service – an early “semantic question answering service”. The “search terms” were fascinating and not so very different to the sort of things you’d expect people to be asking today. He also gave an excellent overview of Lonclass the BBC Archive’s largest classification scheme, which is based on UDC.

BBC Olympics online

Silver Oliver described how BBC Future Media is pioneering semantic technologies and using the Olympic Games to showcase this work on a huge and fast-paced scale. By using semantic techniques, dynamic rich websites can be built and kept up to the minute, even once results start to pour in.

World Service audio archives

Yves Raimond talked about a BBC Research & Development project to automatically index World Service audio archives. The World Service, having been a separate organisation to the core BBC, has not traditionally been part of the main BBC Archive, and most of its content has little or no useful metadata. Nevertheless, the content itself is highly valuable, so anything that can be done to preserve it and make it accessible is a benefit. The audio files were processed through speech-to-text software, and then automated indexing applied to generate suggested tags. The accuracy rate is about 70% so human help is needed to sort out the good tags from the bad (and occasionally offensive!) tags, but thsi is still a lot easier than tagging everything from scratch.

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

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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.

Classification and Ontology – UDCC Seminar 2011

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Estimated reading time 2–4 minutes

I thoroughly enjoyed the third biennial International UDC Consortium seminar at the National Library of the Netherlands, The Hague, last Monday and Tuesday. The UDC conference website includes the full programme and slides and the proceedings have been published by Ergon Verlag.

This is a first of a series of posts covering the conference.

Aida Slavic, UDC editor-in-chief, opened the conference by pointing out that classification is supposed to be an ordered place, but systems and study of it are difficult and complex. We still lack terminology to express and discuss our work clearly. There is now an obvious need to instruct computers to use and interpret classifications and perhaps our work to make our classifications machine readable will also help us explain what we do to other humans.

On being the same as

Professor Patrick Hayes of the Florida Institute for Machine Learning and Cognition delivered the keynote address, pointing out that something so simple as asserting that one thing is the same as another is actually incredibly difficult and one of the problems facing the development of the Semantic Web is that people are asserting that two things are the same when actually they are merely similar.

He explained that the formalisms and logic underpinning the Semantic Web are all slimmed down versions of modern 20th century logic based on a particular world view and set of assumptions. This works very well in theory, but once you start applying such logics to the real messy and complex world with real objects, processes, and ideas, the logics are put under increasing stress.

In logic, when two things are referred to as the same, this means they are two different names for the same thing, not that there are two things that are logically equivalent. So, Paris, the city of my dreams, and Paris the administrative area, Paris throughout history, and Paris – the capital of France are not necessarily all the same. This means that in logic we have to separate out into different versions aspects of an idea that in ordinary language we think of as the same thing.

He described this as the problem of “logic versus Occam” (as in Occam’s razor). Logic drives us to create complexity, in that we have to precisely define every aspect of a concept as a different entity. In order for the Semantic Web to work, we need to be very clear about our definitions so that we don’t muddle up different aspects of a concept.

Online Information Conference – day two

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Estimated reading time 6–10 minutes

Linked Data in Libraries

I stayed in the Linked Data track for Day 2 of the Online Information Conference, very much enjoying Karen Coyle‘s presentation on metadata standards – FRBR, FRSAR, FRAD, RDA – and Sarah Bartlett‘s enthusiasm for using Linked Data to throw open bibliographic data to the world so that fascinating connections can be made. She explained that while the physical sciences have been well mapped and a number of ontologies are available, far less work has been done in the humanities. She encouraged humanities researchers to extend RDF and develop it.

In the world of literature, the potential connections are infinite and very little numerical analysis has been done by academics. For example, “intertextuality” is a key topic in literary criticism, and Linked Data that exposes the references one author makes to another can be analysed to show the patterns of influence a particular author had on others. (Google ngrams is a step in this direction, part index, part concordance.)

She stressed that libraries and librarians have a duty of care to understand, curate, and manage ontologies as part of their professional role.

Karen and Sarah’s eagerness to make the world a better place by making sure that the thoughtfully curated and well-managed bibliographic data held by libraries is made available to all was especially poignant at a time when library services in the UK are being savaged.

The Swedish Union Catalogue is another library project that has benefited from a Linked Data approach. With a concern to give users more access to and pathways into the collections, Martin Malmsten asked if APIs are enough. He stressed the popularity of just chucking the data out there in a quick and dirty form and making it as simple as possible for people to interact with it. However, he pointed out that licences need to be changed and updated, as copyright law designed for a print world is not always applicable for online content.

Martin pointed out that in a commercialised world, giving anything away seems crazy, but that allowing others to link to your data does not destroy your data. If provenance (parametadata) is kept and curated, you can distinguish between the metadata you assert about content and anything that anybody else asserts.

During the panel discussion, provenance and traceability – which the W3C is now focusing on (parametadata) – was discussed and it was noted that allowing other people to link to your data does not destroy your data, and often makes it more valuable. The question of what the “killer app” for the semantic web might be was raised, as was the question of how we might create user interfaces that allow the kinds of multiple pathway browsing that can render multiple relationships and connections comprehensible to people. This could be something a bit like topic maps – but we probably need a 13-year-old who takes all this data for granted to have a clear vision of its potential!

Tackling Linked Data Challenges

The second session of day two was missing Georgi Kobilarov of Uberblic who was caught up in the bad weather. However, the remaining speakers filled the time admirably.

Paul Nelson of Search Technologies pointed out that Google is not “free” to companies, as they pay billions in search engine optimisation (SEO) to help Google. Google is essentially providing a marketing service, and companies are paying huge amounts trying to present their data in the way that suits Google. It is therefore worth bearing in mind that Google’s algorithms are not resulting in a neutral view of available information resources, but are providing a highly commercial view of the web.

John Sheridan described using Linked Data at the National Archives to open up documentation that previously had very little easily searchable metadata. Much of the documentation in the National Archives is structured – forms, lists, directories, etc. – which present particular problems for free text searches, but are prime sources for mashing up and querying.

Taxonomies, Metadata, and Semantics: Frameworks and Approaches

There were some sensible presentations on how to use taxonomies and ontologies to improve search results in the third session.
Tom Reamy of KAPS noted the end of the “religious fervour” about folksonomy that flourished a few years ago, now that people have realised that there is no way for folksonomies to get better and they offer little help to infrequent users of a system. They are still useful as a way of getting insights into the kind of search terms that people use, and can be easier to analyse than search logs. A hybrid approach, using a lightweight faceted taxonomy over the top of folksonomic tags is proving more useful.

Taxonomies remain key in providing the structure on which autocategorisation and text analytics is based, and so having a central taxonomy team that engages in regular and active dialogue with users is vital. Understanding the “basic concepts” (i.e. Lakoff and Rosch’s “basic categories”) that are the most familiar terms to the community of users is vital for constructing a helpful taxonomy and labels should be as short and simple as possible. Labels should be chosen for their distinctiveness and expressiveness.

He also pointed out that adults and children have different learning strategies, which is worth remembering. I was also pleased to hear his clear and emphatic distinction between leisure and workplace search needs. It’s a personal bugbear of mine that people don’t realise that looking for a hairdresser in central London – where any one of a number will do – is not the same as trying to find a specific shot of a particular celebrity shortly after that controversial haircut a couple of years ago from the interview they gave about it on a chat show.

Tom highlighted four key functions for taxonomies:

  • knowledge organisation systems (for asset management)
  • labelling systems (for asset management)
  • navigation systems (for retrieval and discovery)
  • search systems (for retrieval)

He pointed out that text analytics needs taxonomy to underpin it, to base contextualisation rules on. He also stressed the importance of data quality, as data quality problems cause the majority of search project failures. People often focus on cool new features and fail to pay attention to the underlying data structures they need to put in place for effective searching.

He noted that the volumes of data and metadata that need to processed are growing at a furious rate. He highlighted Comcast as a company that is very highly advanced in the search and data management arena, managing multiple streams of data that are constantly being updated, for an audience that expects instant and accurate information.

He stated that structure will remain the key to findability for the foreseeable future. Autonomy is often hailed as doing something different to other search engines because it uses statistical methods, but at heart it still relies on structure in the data.

Richard Padley made it through the snow despite a four-hour train journey from Brighton, and spoke at length about the importance of knowledge organisation to support search. He explained the differences between controlled vocabularies, indexes, taxonomies, and ontologies and how each performs a different function.

Marianne Lykke then talked about information architecture and persuasive design. She also referred to “basic categories” as well as the need to guide people to where you want them to go via simple and clear steps.

Taxonomies, Metadata, and Semantics in Action

I spoke in the final session of the day, on metadata life cycles, asset lifecycles, parametadata, and managing data flows in complex information “ecosystems” with different “pace layers”.

Neil Blue from Biowisdom gave a fascinating and detailed overview of Biowisdom’s use of semantic technologies, in particular ontology-driven concept extraction. Biowisdom handle huge complex databases of information to do with the biological sciences and pharmaceuticals, so face very domain-specific issues, such as how to bridge the gap between “hard” scientific descriptions and “soft” descriptions of symptoms and side-effects typically given by patients.

In the final presentation of the day, Alessandro Pica outlined the use of semantic technologies by Italian News agency AGI.

Linking classification schemes via rdf

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< 1 minute

I was delighted to read an excellent blog post by Dan Brickley about the value of opening up classifications. It is lovely to read something like this from a technical expert who understands the power of innovation and new technology, but who also recognises the value of the information contained in classifications.

Are you a semantic romantic?

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Estimated reading time 8–12 minutes

The “semantic web” is an expression that has been used for long enough now that I for one feel I ought to know what it means, but it is hard to know where to start when so much about it is presented in “techspeak”. I am trying to understand it all in my own non-technical terms, so this post is aimed at “semantic wannabes” rather than “semantic aficionados”. It suggests some ways of starting to think about the semantic web and linked open data without worrying about the technicalities.

At a very basic level, the semantic web is something that information professionals have been doing for years. We know about using common formats so that information can be exchanged electronically, from SGML, HTML, and then XML. In the 90s, publishers used “field codes” to identify subject areas so that articles could be held in databases and re-used in multiple publications. In the library world, metadata standards like MARC and Dublin Core were devised to make it easier to share cataloguing data. The semantic web essentially just extends these principles.

So, why all the hype?

There is money to be made and lost on semantic web projects, and investors always want to try to predict the future so they can back winning horses. The recent Pew Report (thanks to Brendan for the link) shows the huge variety of opinions about what the semantic web will become.

On the one extreme, the semantic evangelists are hoping that we can create a highly sophisticated system that can make sense of our content by itself, with the familiar arguments that this will free humans from mundane tasks so that we can do more interesting things, be better informed and connected, and build a better and more intelligent world. They describe systems that “know” that when you book a holiday you need to get from your house to the airport, that you must remember to reschedule an appointment you made for that week, and that you need to send off your passport tomorrow to renew it in time. This is helpful and can seem spookily clever, but is no more mysterious than making sure my holiday booking system is connected to my diary. There are all sorts of commercial applications of such “convenience data management” and lots of ethical implications about privacy and data security too, but we have had these debates many times in the past.

A more business-focused example might be that a search engine will “realise” that when you search for “orange” you mean the mobile phone company, because it “knows” you are a market analyst working in telecoms. It will then work out that documents that contain the words “orange” and “fruit” are unlikely to be what you are after, and so won’t return them in search results. You will also be able to construct more complex questions, for example to query databases containing information on tantalum deposits and compare them with information about civil conflicts, to advise you on whether the price of mobile phone manufacture is likely to increase over the next five years.

Again, this sort of thing can sound almost magical, but is basically just compiling and comparing data from different data sets. This is familiar ground. The key difference is that for semantically tagged datasets much of the processing can be automated, so data crunching exercises that were simply too time-consuming to be worthwhile in the past become possible. The evangelists can make the semantic web project sound overwhelmingly revolutionary and utopian, especially when people start talking in sci-fi sounding phrases like “extended cognition” and “distributed intelligence”, but essentially this is the familiar territory of structuring content, adding metadata, and connecting databases. We have made the cost-benefit arguments for good quality metadata and efficient metadata management many times.

On the other extreme, the semantic web detractors claim that there is no point bothering with standardised metadata, because it is too difficult politically and practically to get people to co-operate and use common standards. In terms familiar to information professionals, you can’t get enough people to add enough good quality metadata to make the system work. Clay Shirky in “Ontology is overrated” argued that there is no point in trying to get commonalty up front, it is just too expensive (there are no “tag police” to tidy up), you just have to let people tag randomly and then try to work out what they meant afterwards. This is a great way of harvesting cheap metadata, but doesn’t help if you need to be sure that you are getting a sensible answer to a question. It only takes one person to have mistagged something, and your dataset is polluted and your complex query will generate false results. Shirky himself declares that he is talking about the web as a whole, which is fun to think about, but how many of us (apart from Google) are actually engaged in trying to sort out the entire web? Most of us just want to sort out our own little corner.

I expect the semantic web to follow all other standardisation projects. There will always be a huge “non-semantic” web that will contain vast quantities of potentially useful information that can’t be accessed by semantic web systems, but that is no different from the situation today where there are huge amounts of content that can’t be found by search engines (the “invisible web” or “dark web”) – from proprietary databases to personal collections in unusual formats. No system has been able to include everything. No archive contains every jotting scrawled on a serviette, no bookshop stocks every photocopied fanzine, no telephone directory lists every phone number in existence. However, they contain enough to be useful for most people most of the time. No standard provides a perfect universal lingua franca, but common languages increase the number of people you can talk to easily. The adoption of XML is not universal, but for everyone who has “opted in” there are commercial benefits. Not everybody uses pdf files, but for many people they have saved hours of time previously spent converting and re-styling documents.

So, should I join in?

What you really need to ask is not “What is the future of the semantic web?” but “Is it worth my while joining in right now?”. How to answer that question depends on your particular context and circumstances. It is much easier to try to think about a project, product, or set of services that is relevant to you than to worry about what everyone else is doing. If you can build a product quickly and cheaply using what is available now, it doesn’t really matter whether the semantic web succeeds in its current form or gets superseded by something else later.

I have made a start by asking myself very basic questions like:

  • What sort of content/data do we have?
  • How much is there?
  • What format is it in at the moment?
  • What proportion of that would we like to share (is it all public domain, do we have some that is commercially sensitive, but some that isn’t, are there data protection or rights restrictions)?

If you have a lot of data in well-structured and open formats (e.g. XML), there is a good chance it will be fairly straightforward to link your own data sets to each other, and link your data to external data. If there are commercial and legal reasons why the data can’t be made public, it may still be worth using semantic web principles, but you might be limited to working with a small data set of your own that you can keep within a “walled garden” – whether or not this is a good idea is another story for another post.

A more creative approach is to ask questions like:

  • What content/data services are we seeking to provide?
  • Who are our key customers/consumers/clients and what could we offer them that we don’t offer now?
  • What new products or services would they like to see?
  • What other sources of information do they access (users usually have good suggestions for connections that wouldn’t occur to us)?

Some more concrete questions would be ones like:

  • What information could be presented on a map?
  • How can marketing data be connected to web usage statistics?
  • Where could we usefully add legacy content to new webpages?

It is also worth investigating what others are already providing:

  • What content/data out there is accessible? (e.g. recently released UK government data)
  • Could any of it work with our content/data?
  • Whose data would it be really interesting to have access to?
  • Who are we already working with who might be willing to share data (even if we aren’t sure yet what sort of joint products/projects we could devise)?

It’s not as scary as it seems

Don’t be put off by talk about RDF, OWL, and SPARQL, how to construct an ontology, and whether or not you need a triple store. The first questions to ask are familiar ones like who you would like to work with, what could you create if you could get your hands on their content, and what new creations might arise if you let them share yours? Once you can see the semantic web in terms of specific projects that make sense for your organisation, you can call on the technical teams to work out the details. What I have found is that the technical teams are desperate to get their hands on high quality structured content – our content – and are more than happy to sort out the practicalities. As content creators and custodians, we are the ones that understand our content and how it works, so we are the ones who ought to be seizing the initiative and starting to be imaginative about what we can create if we link our data.

A bit of further reading:
Linked Data.org
Linked Data is Blooming: Why You Should Care
What can Data.gov.uk do for me?

Truevert: What is semantic about semantic search?

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Estimated reading time 1–2 minutes

Truevert: What is semantic about semantic search? is an easy introduction to the thinking behind the Truevert semantic search engine. I was heartened by the references to Wittgenstein and the attention Truevert have paid to the work of linguists and philosophers. So much commercial search seems to have been driven by computer scientists with little interest in philosophy, or if they did they kept quiet about it (any counter examples out there?)! Perhaps philosophers have not been so good at promoting themselves either. Perhaps the Chomskyian attempt to divide linguistics itself into “hard scientific” linguistics and “fuzzy” linguistic disciplines like sociolinguistics has not helped.

As a believer in interdisciplinary and collaborative approaches, I have always wondered why we seemed to be so bad at building these bridges and information science has always struck me as a natural crossing point. Of course, there has been a lot of collaboration, but my impression is that academia has been rather better at this than the commercial world, with organisations like ISKO UK working hard to forge links. Herbert Roitblat at Truevert is obviously proud of their philosophical and linguistic awareness, and more interestingly, thinks it is worth broadcasting in a promotional blog post.