Summary of MDG Session, 11-19-08

* Written by Jenn Riley *

Article discussed: Cundiff, Morgan V. (2004). “An Introduction to the Metadata Encoding and Transmission Standard (METS).” Library Hi Tech 22(1): 52-64.

The session began with a question raised: is allowing arbitrary descriptive and administrative metadata formats inside METS documents a good idea? The obvious advantage is that it makes METS very versatile. But this could also limit its scope – does that make METS only for digitized versions of physical things, excluding born digital material? The group as a whole didn’t believe this was an inherent limitation. The ability to add authorized extension schema over time seems to be a good thing, and necessary for the external schema allowance to work.

The flexibility of METS allows it to be used beyond its textual origins – to scores, sound recordings, images, etc. It could potentially be useful beyond libraries, especially to archives and museums. To balance this flexibility, is knowing some sort of structured metadata is being presented enough to ensure a reasonable level of interoperability?

The discussion then turned to the TYPE attribute on

, a topic much discussed in the METS community. How does a METS implementer know what values to use? An organization will presumably develop its own practice but the practices won’t be the same across institutions. A clever name for this was suggested: “plantation” metadata – each place can develop their own.

Are there lessons from library cataloging that could help with this problem? Institutions dealing with the same types of material could join together and harmonize practices. METS Profiles provide the means for documenting this, but they don’t really encourage collaboration. Perhaps the expectation is that the metadata marketplace will converge, and those going their own way will lose out some significant benefits, and see it in their best interest to collaborate.

This line of thought led to the question – How did OCLC/LC/the library community get standardized in the first place? Probably because individuals would write up their own rules, then share them. Eventually these rules became shared practice. Maybe this same shift will happen when sharing really becomes a priority. Diverse practices will converge when people really want them to. 

A question was then raised about when METS should be used instead of MARC. When is MARC not enough? A participant made the analogy that this was like comparing a plantation to a video arcade. The two are for different purposes, and METS can include descriptive metadata in any format, including MARC. If you want to allow a certain type of searching, for example, a user wants to search for a recording by a certain group, saying METS is better than MARC doesn’t make sense. The descriptive metadata schema used within METS is what is going to make the difference in this case, not the use of METS itself. An implementer will still need good descriptive information.

Participants then noted that we had been talking about systems, but we need to talk more about people. Conversations between communities with different practices will help improve interoperability. Can we standardize access points? To do this we would need to develop vocabularies collaboratively between communities, and talk more so that we understand each other’s point of view.

One participant made an extremely astute observation that the structure of METS makes it seem that it wasn’t designed to be used directly by people. While metadata specialists often need to look at METS, and plan for what METS produced by an institution should look like, the commenter is correct that for the most part, METS is intended for machine consumption. A developer present noted that we could write an application that does a lot of what METS does without actually storing it in XML/METS – but the benefit of METS is abstracting out one more layer. Coming full circle to the flexibility issue from earlier in the discussion, it was noted that it is difficult to make standard METS tools (including parsers and generators) due to the almost infinite practices that must be accommodated. This led to the thought that perhaps METS could go much farther in being machine-friendly than it already is. That’s a scary thought to metadata specialists who work with it!

Summary of MDG Session, 10-16-08

* Written by Jenn Riley *

Article discussed: Eklund, Janice. (2007) “Herding Cats: CCO, XML, and the VRA Core.” VRA Bulletin 34, no. 1: 45-68.

The Discussion Group began by picking up a theme from the first meeting of the semester: effective use of terminology in writing about metadata. This article did a good job using new terms consistently and frequently, although terms from VRA Core 3 were occasionally applied to a discussion of Core 4. The discussion of consistency then expanded to consistency in metadata itself. Consistency is very useful when one is combining metadata from multiple sources, and content standards like CCO can go a long way towards promoting this consistency.

The mention of CCO sparked a lively conversation about the way the word “standards” is tossed about in metadata circles. Is CCO a standard or not? CCO and VRA Core are not in total agreement, so what does it mean if both are standards we should follow? One can track why the difference exists—CCO has a broader scope, including museums, than VRA Core. CCO is a standard in the way AACR2 is a standard, but not in the way MARC is a standard. AACR2 is learned by practice, and less by reading the book. CCO is still evolving, taking time to learn and implement. It’s more a guide to best practice than AACR2 is. CCO is principle-based like RDA is supposed to be, because it needs to be applicable to many communities.

The next topic of discussion was whether or not VRA Core is really “core.” Its greater coverage for works of art than Dublin Core certainly speaks to it being a domain-specific “core.” The group was less sure if it represented an “exhaustive core.” Tracking VRA Core’s history could be instructive in this analysis – the evolution from Core 2 to Core 3 to Core 4 shows some stabilization, so this could be evidence that they’ve achieved an agreed-upon core. The only really new thing in Core 4 is the collection root element (in addition to work and image).

The linking capability of VRA Core was singled out as an especially effective part of the format, encouraging the use of identifiers to track relationships within text strings. There is not the infrastructure for collaborative development and sharing of authority records in the visual resources community that there is in the library community, so the process of record linking is more manual now in the VR environment than in the library/MARC community. But there is significant progress being made. The community needs to build good systems, and cooperate between institutions. They also need to expand the notion of authority control, to allow for more variety in name references, for example.

Efforts such as CONA (Cultural Objects Name Authority, forthcoming from the Getty) and the Society of Architectural Historians Architectural Visual Resources Network are helping to build the needed infrastructure. More cooperation overall is needed – the VR community and library community are both starting to realize that each of us having our own copies of records isn’t sustainable. Formats like VRA Core can promote fuller record sharing.

Using separate fields for display and indexing was another feature of VRA Core of interest to the discussion group. It was noted that this practice allowed a great deal of flexibility but also required twice as much work. To decide when this is necessary, one must consider how the information will be used—for search or display? in future systems in addition to future ones? how easy will it be to upgrade systems? It’s more important to include both for data elements that represent key features of the work or medium, for example, cultural context.

The discussion group noted that cultural objects cataloging could be a model for library catalogers looking to re-examine which aspects of their work require the attention of cataloging professionals. Cultural objects cataloging places a greater emphasis on analysis than transcription, which is necessary because cultural objects in general don’t explain themselves. Interestingly enough, some visual resources units are “outsourcing” subject indexing to traditional catalogers. Many catalogers on both sides don’t feel competent to do subjective indexing – is something “about death”? It’s much easier to record form/style, what something is rather than what it is about.

Summary of MDG session, 9-30-08

* Written by Jenn Riley *

Article discussed: Greenberg, Jane. (2005). “Understanding Metadata and Metadata Schemes.” Cataloging & Classification Quarterly 40, no. 3/4: 17-36.

The discussion began with a general question: Does the MODAL framework appear to be a useful way of evaluating metadata schemas? The group in general thought it was, although expressed concern that some of the language in the article was very academic, which sometimes made it difficult for practicing librarians to follow the argument.

Participants appreciated the fact that some metadata schema such as TEI (p. 28 of the article) have as a stated principle the conversion of resources to newer communication formats. This principle is of great benefit, and would be useful for other metadata schemas as well. Data formats will not stay static – our metadata must adapt its format over time to accommodate new ways of communicating.

Some participants noticed a contrast between the design of metadata schemas based on experience and observation and library cataloging rules that are more formalized and change less frequently. This observation led to the question of whether cataloging rules should be more fluid. When the rules do change, the changes are based on experience. From an implementation point of view, it is difficult both for libraries and our users if the rules are constantly changing. Our legacy data is a very real consideration here. So how do we be flexible and adaptable but at the same time consistent and keep up with the legacy data?

The MODAL framework spoke to participants as an analysis tool – helping evaluate the fitness of a given schema for a given purpose. This gets us away from saying a metadata format is “bad” – rather it lets us say that records using the Dublin Core Metadata Element Set are not well-fit to handle FRBRized data, for example.

The article’s methodology of bringing in Cutter’s objectives as an example of underlying objectives and principles sat well with the discussion group. One participant noted that not many current studies do this. These assumptions can help us focus our efforts. Follow up work could to do some comparison of Cutter’s objectives to different metadata formats.

Terminology issues were a hot topic of discussion at the session. Participants thought some kind of collaboratively-developed metadata glossary would be a good idea. They felt it was important for librarians interested in metadata issues to learn new vocabularies. We need to read more, ingest as much as possible, make connections to what we already do. “Cardinality” was an example of a term which was unfamiliar – it brings in the repeatable vs. not repeatable notion that is familiar, but also covers required/not required. Domains do have specialized vocabularies – they serve as “rites of passage” into various professions. Metadata schemes all have context that assumes a specific knowledge base – this article recognizes that. It would be nice if articles had glossaries, though.

Even with discussion, definitions of some terms did not establish a clear consensus. The term “granularity” was defined in the group as “refinement,” “the amount you want to analyze down to,” “extent of the description,” “specificity,” and “granular means you can slice in different ways.”

Participants appreciated the empirical focus of the article, saying that metadata schema design should be observation/experiment based. It’s certainly a good thing to have metadata be practical – actually useful. To help decide what metadata schema to use, try out a couple schemas and see how they work, rather than thinking more abstractly. But also need consider community as a factor. The MODAL framework is “multi-focal” – focusing first on one aspect then go to another. Helps implementers think, for example, about both the community and the data itself.

Participants noted two schools of thought for metadata design: a difference of orientation thinking of a problem looking for a solution, as contrasted with a solution looking for a problem. Is there sill room for cataloger judgment? Absolutely. Perhaps cataloger’s judgment is needed more in the application of a content standard rather than a structure standard.

This distinction led participants to speculate whether the line between the two is blurring (although all recognized it has always been somewhat blurry). RDA especially seems to be trying to do both simultneously. One participant noted that libraries seem to be moving to blur the two, while other communities are moving to separate them more.

Is terminology the only barrier to learning more about metadata? Some individuals learn better with theory and others with practice. All need a little of both. It really just takes time – remember what it was like to learn cataloging? Getting out of one’s comfort zone is difficult. It’s also difficult to be adventurous, when there is less precedent to follow. It’s hard to learn many standards – don’t always know which one to use. When you have to learn lots of things, you learn each of them less well. We also have new objectives, including reaching new people and operating in additional systems. It would be helpful to identify models of other institutions where a technical services unit has made significant progress in these areas.

The group found Table 1, which outlines some typologies of metadata schemas, to be interesting. The lines between them seem arbitrary at worst and murky at best. Over time the thinking in this area has gone from 7 categories to 4 – does this mean our community is looking for simplicity? Does this mean this environment is settling down? Maybe, but initiatives such as the DCMI Abstract Model seem to be going the other direction.

The discussion moved relatively seamlessly from topic to topic, and featured a number of insightful comments, often from new participants. Both nitty-gritty and “big picture” issues were raised. Thanks to all who participated for an enlightening discussion.

Summary of MDG session, 5-27-08

* Written by Jenn Riley *

Article discussed: Hagedorn, Kat, Suzanne Chapman, and David Newman. (July/August 2007) “Enhancing search and browse using automated clustering of subject metadata.” D-Lib Magazine 13, no. 7/8. http://www.dlib.org/dlib/july07/hagedorn/07hagedorn.html

The session began with a brief explanation of the methodology employed by this experiment and the OAI-PMH protocol, as it may not have been clear to those who don’t deal with this sort of technology on a regular basis. After this introduction, discussion moved to wondering why the Michigan “high-level browse list” was chosen for grouping clusters, rather than a more standard list? The group realized the value of a short, extremely general list for this purpose, and noted our own Libraries use a similar locally-developed list. Most standard library controlled vocabularies and classification schemes have far too many top terms to be effective for this sort of use. It was noted that choosing cluster labels, if not the high-level grouping, from a library standard controlled vocabulary would promote interoperability of this enhanced data.

The question of quality control then arose: the article described on person performing a quality check on the cluster labels – this must have been an enormous task! The article mentioned mis-assigned categories that would have been found with a more formal quality review process. Have they thought about how they would fix things on the fly – features like “click here to tell us this is wrong”? Did the experiment designers talk to catalogers or faculty as part of the cluster labeling process? Who were their colleagues they asked to do the labeling?

Is their proposal to not label the clusters at all, but to just connect to the high-level browse categories a good on? The group posited that the high-level browse used the campus structure of majors, rather than not organizational structure of the university. (This is the way the IU Libraries web site is structured). In this case, the subcategories more meaningful than main categories, so at least this level would likely be needed.

The discussion group noted evidence of campus priorities in the high-level browse list, for example that the arts and humanities seemed to be under-represented and lumped together while the sciences received more specific attention. Did this make a difference in the clustering too? As noted in the article, titles in the humanities can be less straightforward than in other discipline, making greater use of metaphors. What do the science records have that humanities records don’t? Abstracts, probably – anything else? Perhaps it’s just that the titles were more specific. Do science subject headings contain more information? Description in humanities collections might be more varied than the language in sciences? Many possibilities were presented but the group wasn’t sure which would really affect the clustering methodology.

The group then wondered if the humanities/sciences differences noted in this article would show up in a single institution, or was it just caused in OAIster because of the fact that different data providers tend to focus on one or the other and the difference is really between data providers rather than between disciplines. The group noted (as a gross generalization) that humanities tend to be more interested in time period, people, and places, whereas the sciences are more interested in topic.

Would the clustering strategy work locally ad not just on aggregations? The suggestion in the article that results might improve if run just on one discipline at a time suggests it might. In this case, clusters would likely be more specific. Perhaps an individual institution could employ this method on full text, and leave running it on metadata records alone to the aggregators. It would be interesting to find out if there’s a difference in effectiveness of this methodology on metadata records for different formats, for example, image vs. text.

The group noted the clustering technique would only be as good as the records from the original site. What if context were missing? (the “on the horse” problem) Garbage in, garbage out, as they say. We understood why the experiment only used English-language records, but it would be interesting to extend this.

The clustering experiment was run using only the data from the title, subject, and description fields. Should they use more? Why not creator? This is useful information. Was it because clusters would then form around creators, which could be collocated using existing creator information? The stopword list was interesting to the group. It made sense why terms such as library and copyright were on it, but there are resources about these things, so we don’t want to artificially exclude them. What if the stopword list were not applied to the title field?

The discussion group wondered how these techniques relate to those operating in the commercial world. Amazon uses “statistically improbable phrases” which seems to be the opposite of this technique – identifying terminology that’s different rather than the same between resources. What about studies comparing these automatic methods to user tagging? No participants knew of such a study in the library literature, but it was noted there might be information on this topic in the information retrieval literature. It would be interesting to compare data from this process to the tags from users generated as part of the LC Flickr project.

The article described the overall approach as attempting to create simple interfaces to complex resources. Is this really our goal? We definitely want to collocate like resources. The interface in the screenshots didn’t seem “Google-style” simple. The group noted that in the library field many believe simple interfaces can only yield simple answers and that people looking with simple techniques are generally just looking for something rather than a comprehensive research goal. This article doesn’t have in its scope a discussion as to whether this is true. One big problem is that the article never defines its user base, ad different user bases employ different search techniques.

The discussion group believed that browseability, as promoted by the clustering technique, is a key idea. With a good browse, the interface can provide more ways to get at resources, and then they are more findable. Hierarchical information can be a good way to get users to resources. With the experiment described in this article, the hierarchy is discipline/genre. Would retrieval improve if we pulled in other data from the record to do faceted browsing? Would this work better for humanities rather than science? Do we need to treat the disciplines differently?

Discussion group participants noted that “this isn’t moonwalking,” meaning that this technique looks promising. It needs some tweaking, but the technique hasn’t promised the moon – it’s not purporting to be a be all, end all solution. Its just something we can do, as one part of the many other techniques we use. Can a simple, Google-style interface eventually work for intensive research needs on this data? Or should it? Should the search just lead them to a seminal article and then they citation chase from there? These are interesting questions.

The group then wondered if the proposal to recluster only every few years was a good one. They would certainly need to do it when getting big new chunks of data that are dissimilar to what’s already in the repository. A possible method would be to randomly test once per month to see if clusters are working out well.

The session ended with some more philosophical questions. Why should services like OAIster exist at all if Google can pick these resources up? Is this type of services beneficial for resources that will never get to the top of a Google search in their native environments? What would happen if one were to apply these techniques to a repository with a more resource-based rather than subject-based collection development policy?

Summary of MDG session, 4-22-08

* Written by Jenn Riley *

The article for discussion this month was:

Borbinha, José. (2004). “Authority control in the world of metadata.” Cataloging & Classification Quarterly 38(3/4): 105-116.

The article provoked a lively discussion that centered largely around the future and functions of authority control. It began by wondering what the tie-in to authority control in the article, as implied by the title, really was. The creator concept is very strong in the article, and authors are something we traditionally control in libraries, although archives treat creators differently. The explicit connection of the article to authority control is at the very end: it’s not so much about all using the same rules, but that we know what rules are being used. Control not as important as interoperability. Is this a good conclusion? It’s a practical one.

The discussion in this article is most useful to practicioners, in that it helps us think about why we do authority control in the first place. Some concern was expressed about the very general statements being made in what was perceived as overly technical language.

At this point, there was a bit of confusion in the room, as two participants realized they’d read the wrong article in preparation for the session. This article:

Vitali, Stefano. “Authority Control of Creators and the Second Edition of ISAAR(CPF), International Standard Archival Authority Record for Corporate Bodies, Persons, and Families.” Cataloging & Classification Quarterly 38(3/4):185-199

…is an interesting one, discussing in depth the motivations and methods for authority control in archives. It’s well worth a read.

That being settled, we returned to the Borbinha article. The effectiveness of crossing institituional borders was questioned– we don’t do this well but Amazon seems to. Perhaps we’re still too focused on our methodology, rather than the goal.

The moderator asked if the conceptual vs. context, etc., perspective was useful. The group was uncertain on this issue, and the gulf between theory and practice emerged as a discussion topic. Practitioners mostly know the records one sees in the cataloging interface, and in this mode, the distinction between, say, structure standards and content standards, can be confusing. AACR/MARC/data entry system are all taught together – the distinction between them is not generally made in training or daily life. Practitioners tend to move through the learning curve with both integrated into one’s mind. So it’s hard to think that we can make an AACR record in Dublin Core – overall it’s very hard to talk about one without the other. Most never see the under-the-hood record coding at all. But in some cases it is useful to keep these distinctions in mind. What do we gain from thinking of them differently? It’s likely not going to be effective to teach the conceptual first and then the practical.

From public service perspective, these functions as shown in Figure 3 are a black box that searches go into and come out of. How useful are these distinctions to that community?

Discussion then turned to the Fellini example in the paper– how would a system bring these together without authority control? Can we live with a system that isn’t perfect? Can we trust a secret and proprietary algorithm? What about the model of a human-generated Wikpedia page with a disambiguation step? Is it better to do the matching up of names ahead of time, or at search time?

Can Google connect Mark Twain and Samuel Clemens, as well as just misspelling Mark? Our authority records handle forms of names found in items, Google handles common misspellings. Are these things different? Authority control serves lots of purposes: disambiguate same name, collocate same person with multiple names, etc. But there’s room for both approaches. Search systems could have an authority list running underneath. Google works differently, pulling data from the Web rather than from an authority file. Ranganathan’s principle: “every book its reader” – can we say every search term its hit? OCLC WorldCat – we can guess they’re employing both the authority-based search and Google-based methodologies? Maybe not, doesn’t seem like they’re stemming. Their searches seem to be very literal.

The dual approach seems promising, as authority files have different purposes than the Google-style work. Maybe we could pull in data from 670 references (or from more structured data proposed by FRAD and showing up in RDA drafts)? Ask the user: “do you want the chemist or social scientist”?

Heterogeneity is part of our life, as the article mentions. We simply have to deal with it. We should find small models that can deal with it, and build on those. What about heterogeneity of thesauri? Some cases it’s clear what thesaurus to use, in others it’s not. When you use different vocabularies, it’s a barrier to interoperability – how do we overcome this? This is the tension between doing one thing well and using the same standards for everything but do each less well that has come up in our discussions before. Yet Google and Amazon aren’t worrying about this. Google connects Twain and Clemens because somebody made the connection on the web page.

This is one of the drivers behind the “OPAC sucks” movement – for the audience that just wants something, not everything. There’s a mistmatch between this goal and the one OPACs are designed around. But maybe users actually want something good (not everything good). Our systems don’t have most useful stuff at the top. WorldCat tries to do this by ranking by holdings. We’re doing something wrong when the Director of Technical Services goes to Amazon to find a book because she can’t find it in or catalog!