A document taxonomy alignment method, relying on document glosses and utilizing a soft ontology expansion, enables us to devise some allnew hierarchical leaf nodes for the purpose of better aligning a plurality of document taxonomies.
In our past work of mapping different document taxonomies, we frequently were left with some “isolated nodes”, i.e. categories of documents seeming to have no correlate in the other taxonomies. An example was in the Archery category on Yahoo, the subcategory of “Kyudo” (traditional Japanese archery). There was no equivalent to this category on DMOZ or About.com, the two taxonomies we were hoping to correlate. However, a soft ontology expansion we had devised to assist in the mapping meanwhile produced numerous candidate ontology nodes, such as “coaching/training” or “competitions/tournaments,” and in this particular case, “traditional archery.” While not a node in any of three reference taxonomies, “traditional archery” nonetheless applied to a great number of documents in all three, and especially in Yahoo’s “Kyudo” category. Having used DMOZ as our “master taxonomy”, we not only added “traditional archery” to it, but devised a method of automatically adding every other similar example, with the result of adding these new nodes: Traditional Archery, Coaching & Training, Equipment & Gear, Stories & Discussion.
The first of these, “traditional archery”, included (as a child node) all the Kyudo documents, plus numerous documents from the other two indices, all of which pertain to traditional forms of archery. Since there are other traditional forms of archery (such as medieval European forms) besides Kyudo, it made sense that Kyudo be subsumed in the new node. We found that it was rather straightforward to devise a heuristic for automating this addition of nodes according to the following heuristic: 1. Find an expanded concept that is instantiated disproportionately in the document glosses of an unmapped node. 2. Test if that node is instantiated also in numerous documents not classified at a leaf node in a plurality of taxonomies. 3. If such a node is found, then create a new node with that concept and place the relevant documents under it.
In order to explain how this was accomplished, we will outline (1) our general approach to taxonomy node alignment by semantic resemblance; (2) our conception of a soft ontology expansion (3) the way in which results of the soft ontology expansion can be leveraged to create new nodes as described above.
One approach to taxonomy alignment is the intensional method, which examines the semantics of the names of the nodes, and the titles of documents, as well as the glosses applied to those documents by the taxonomy editors. We applied such a method to human-crafted document taxonomies bearing short glosses. These glosses are meant to summarize what the documents are about and what differentiates each one from others in the same topic, hence they are obviously valuable to our task.
We take the content words of the document titles and glosses, as well as bi-grams containing a topic word in any derived form (e.g., in the archery category we would take “field archery” and “archer’s union”, in addition to single words such as “arrows” and “bows”). We then check to see which of these may be closely related by semantic resemblance. For measuring semantic resemblance, we test for “semantic proximity” in WordNet, which we define as having a maximum distance of 2 in the WordNet hierarchy, with the additional limitations: 1. Only synonyms, hyponyms, hypernyms, and sister-terms are to be considered. 2. Sister-terms are considered proximate only if they share multiple content words in their glosses and/or example sentences in WordNet.
3. Hypernyms are included only if they are at least 4 levels down in the WordNet hierarchy from the root.
Note that this is similar to
The result of our method is, for example, that “clubs” and “organizations” are treated as equivalent terms. This happens by means of a simple percentage match Recognizing Emergent Nodes in
Aligning Multiple Document Taxonomies 3 scoring of the content words in node names. For example, the pair of “Equipment Manufacturers” and “Archery and Bowhunting Gear Manufacturers” receives a score of 0.80, owing to the following facts: First, “Archery” is omitted because it is the same as the overarching topic of “Archery” and hence implicit in all node names. Second, the stop word “and” is discarded. Third, “gear” is matched to “equipment” as a hypernym. That leaves five words total, with only one of them (“bowhunting”) lacking a match: hence the score of 4/5 = 0.80. By trial and error we decided 0.66 was sufficient for alignment.
The virtue of this simple node name resemblance test is that it lets us align, for example, “Clubs and Organizations” with “Clubs and Associations” in two different taxonomies. However it leaves us with the different problem of the numerous documents not assigned a leaf node. In other words, in all three indices, many documents were simply classified in “Archery” without being assigned to a subcategory. In some cases, this seems correct, in that the documents in question were very general archery documents (or websites) not belonging to any particular subclass. But in many other cases, it seemed that a node in a different taxonomy was a natural place for such documents. For example, a website of personal anecdotes and combined with feedback from others, was classified in one taxonomy simply as an “archery” document, but it would have found a perfect home in “Chats and Forums”. This defeats taxonomy alignment, in that it is implied that none of the documents in the one taxonomy would belong in “Chats & Forums” of the other – and yet many of them did.
This type of predicament was later resolved, in some cases, by the results of a soft ontology expansion of all three taxonomies. In other words, after having enriched the ontological characterization of each specific leaf node, we could often align it with an appropriate subset of the documents lumped together in a more general topic of a different taxonomy.
For this exercise, we went back to our extracted words and bi-grams (e.g. “calendar” and “field archery”, etc.), examined their WordNet glosses and example sentences and compared them with collocations and phrases in the document glosses, and found the following to hold true: if two words were frequently paired (collocated after skipping non-content words) in the taxonomy document glosses and also were found in each other’s WordNet glosses, they were, without exception (in our case studies), genuinely related and of ontological import in the category. Our operational definition of “frequent” was: having at least one occurrence in all three taxonomies and having multiple occurrences (2 or more) in at least two of three taxonomies.
This technique has similarities to
Next, when checked the non-leaf-node documents’ glosses for the presence of these concepts. If they matched, then we moved them to the newly created node. For example, several documents with glosses containing “discussion” and “stories” found their way into “Stories & Discussion.” In the end, 37 of 189 documents were thus “migrated downward” to a leaf node, with the result that, on inspection, it seemed the alignment between taxonomies was more complete and intensionally unequivocal. This illustrates that taxonomy alignment cannot be divorced from issues of taxonomical scope and adequacy. If one taxonomy lacks the scope or granularity of another, then the only way to achieve proper alignment is to sort through some of the items in the less granular taxonomy so as to “multiply align” it to other nodes.
Finally, we reached the result that certain of our soft ontology concepts embrace otherwise isolated nodes of one taxonomy, together with non-leaf-node documents of another. A clear example was the topic mentioned earlier, “Kyudo.” Our soft ontology expansion had derived a “traditional archery” as a bi-gram. This was very dense in the Kyudo category (occurring in all but one of its items), while being found also in 16 non-leaf-node documents in DMOZ, including: Donadoni Archery: Supplier of traditional archery equipment in Italy… The Archery Centre: Specialists in field, traditional, and re-enactment archery… Perris Archery: Recurve, compound and traditional archery equipment…
Our procedure was to use the concept string as a new node name (inserting “and” between words that had been found separately rather than directly collocated), and including as a child node the originally isolated node. So our master taxonomy now included “Archery/Traditional Archery/Kyudo” with several DMOZ documents placed in the new node “traditional archery.” This satisfied us as being a far better alignment than we would have without the new node. Kyudo documents now had a closer parent than just being a direct child of “Archery.” And the new interstitial node of “traditional archery” functions to explain where “Kyudo” belongs. We think the same of “Stories and Discussion” introduced as a parent of “Chat and Forums”, and of “Coaching and Training” as a parent for “Instruction” documents that Yahoo had mixed with “Gear”. Table 2 shows the overall alignment results. New Nodes
-Child node Stories & Discussion - Chats & Forums
DMOZ
Chats & Forums Equipment and Gear
Regarding accuracy, the introduction of new nodes carried just one misclassified document, which had been misclassified already on of the third party indices. In general, the accuracy of this method should be as good as the accuracy of the classification of the participant taxonomies. However, we are concerned about the naming of the newly created nodes. In the Archery case above, all the names read nicely, but when we did Soccer, one node received the name “Instructing” when we would prefer to see “Instruction.” Further work could be done on node naming
Editorially created document glosses are a boon to taxonomy alignment, in that they constitute a rich resource to guide semantic resemblance analysis, and have the added bonus, when soft ontology expansion is applied via WordNet, of enabling us to create new interstitial nodes for a more complete and unequivocal alignment of taxonomies.