The survey results also show considerable variation by sector, both of policy and technology implementation. With respect to technologies, ICT companies are also the most willing to consider semantic approaches. The ICT sector has an unusually high interest in all ST components, with 20% or more being willing to consider all of them, and over half of IT respondents looking at Web 2.0 (social computing). [...]  The use of tagging technologies – which overall is the least mature approach in the survey – is most advanced in Life Sciences. The Life Sciences, Media & Entertainment, and ICT sectors all have a reasonably strong interest in Natural Language Processing (roughly 25% on average). Ontologies and RDF/OWL are the technologies least often considered, though the interest in these Semantic Technologies is not insignificant. Taxonomies are slightly more popular, perhaps indicating that companies are taking the first step to prepare for a more semantic approach to IT solutions. The ICT, Energy & Utilities, and Media & Entertainment sectors all have a reasonably strong interest in using taxonomies.

The interview covers questions like:

• How can Linked Data help to make companies more productive?
• Do you think that the Linked Data Initiative can build upon a stable architecture or will it face more and more problems the bigger the “cloud” will grow?
• What´s the ultimate argument for an Enterprise Architect to use languages like SPARQL at least in addition to SQL?
• How will a “Real Time Semantic Web” change the whole game?
• How will the “Semantic Web” be called in 10 years? Will there still be a “Semantic Web”?

Read the full version of the interview here. ]]> http://blog.semantic-web.at/2010/06/16/kingsley-idehen-i-only-think-in-terms-of-a-web-of-linked-data/feed 0 http://blog.aksw.org/2010/ontowiki-095-available/ http://blog.aksw.org/2010/ontowiki-095-available/#comments Mon, 14 Jun 2010 14:55:47 +0000 Sebastian Tramp http://blog.aksw.org/2010/ontowiki-095-available/ AKSW research group is pleased to announce that OntoWiki 0.9.5 is now available for download.

OntoWiki is a web-application enabling the collaborative creation and (linked data) publication of RDF knowledge bases.

• Support for Semantic Pingback, a protocol which enables OntoWiki to communicate named links from linked data resources or blog systems like WordPress.
• Support for the publication of provenance information via Linked Data.
• A new navigation module which support the configuration and usage of arbitrary navigation hierarchies (e.g. based on classes, SKOS elements, geospatial entities or FOAF groups).
• A bookmarklet for collecting RDFa-based information into a specific OntoWiki knowledge base.
• More editing widgets, e.g. for phone number and mailto: resources.
• A new mapping module for the resource visualisation and filtering based on maps.
• Attribute / Tag clouds based on selected RDF properties.
• A GUI for complex SPARQL filter (contains, larger, smaller, between and bound)
• A JSON/RPC server as an additional interface (e.g. for the command line client)
• A plugin to create nice URIs based on the content of a new resource.

• Linked Data editing: users generate linked data to describe their resources (concepts) on top of SKOS
• Linked data lookup: mapping between own thesauri and additional facts from the semantic web The following resources can be used at the moment: DBpedia, Umbel, Yago, DMOZ, LCSH, Geonames & Wordnet; this service is highly configurable – also internal linked data sources can be mapped and used to enrich local thesauri; the lookup service makes use of the very fast TuQS server
• Linked data publishing: based on linked data patterns any resource can be published as linked data, ready to re-use for any linked data application; example: http://open.poolparty.punkt.at/Wine/13 which can also be viewed by linked data browsers like Zitgist’s DataViewer
• SPARQL endpoint: another way how PoolParty’s RDF data can be accessed by semantic web developers

• Translation support: works for nearly any language and domain with high accuracy – thanks to Google Translate
• Online Documentation: PoolParty’s end-user manual is open for the public, easy to access and searchable; since PoolParty 2.7 it is available not only as PDF document but also as browsable Wiki
• Flexible Reporting Tool: As we have already blogged before, PoolParty’s new reporting tool is flexible enough to manage to export formats like, for example, Google Synonyms; also “traditional” thesaurus reports like hierarchical reports are available
• iPhone front-end: If you have to do research using your thesauri while you are somewhere outside of the office, this could be a possible solution for you – see this screenshot!

At this year’s EuroITV conference you will hold a workshop on the EU project NoTube. Can you give us a brief insight what this project is about?

What promises does the Social Semantic Web hold with respect to innovate the television experience? What is the vision?

What economic effects on the value chain do you expect from semantically empowered television? Will there be new revenue opportunities with respect to advertising or Pay TV models?

Despite many attempts experience has shown that television is a rather conservative and innovation-averse medium. What can be done to stimulate the uptake of semantic technologies in the television sector?

Workshop Information

About Lyndon Nixon

1. Use even more metadata to describe your resources and improve navigation and semantic search functionalities significantly
2. Publish (at least) parts of your metadata / knowledge models as linked (open) data to stimulate innovative services around your contents on top of network effects
3. Use linked data for data integration and semantic mashups; combine your own contents with contents from the web to improve your business intelligence

We have learned: Linked Data can help us to build expressive knowledge models by using SKOS as an “interface” to the Linked Data Cloud.

Don´t forget: SKOS stands for Simple Knowledge Organization System, thus PoolParty was designed as an easy-to-use Linked Data and Thesaurus Editor and Publishing System.

Well, in the previous section we mentioned in passing that our technology was based on a synergistic approach, combining syntax, semantics and pragmatics. In this final part of the survey, we’ll explain just how we do this, and why our system yields unparalleled results. In doing so we’ll do our best to abstract away from the underlying mathematics and details of the machine-learning algorithms, and instead present the linguistic principles by which our algorithms work using examples. To wrap things up, we’ll end this review with a snapshot of what Digital Trowel’s Sentiment Analysis looks like in action.

Our technological approach begins with the observation that sentiment is conveyed on three interacting levels of increasing structural complexity. Namely the lexical, phrasal and semantic-event level of structure. We’ll explain.

Lexical sentiment, sometimes referred to as dictionary-based sentiment, is the sentiment attributed to single isolated words. For example:

great, wonderful, terribly, worrisome, helpful, etc…

Though single words clearly carry sentiment, this is the most rudimentary and least reliable sentiment available. To see this consider the following phrases using the above examples:

Great failure
Wonderful fiasco
Terribly surprising comeback
Worrisome transformation for previous skeptics
Helpful in expediting the demise

It should be evident from the above phrases that the initial or “natural” sentiment associated with the isolated words, have all been transformed if not negated. To avoid such “wonderful fiascoes” in deciphering the sentiment, we employ the lexical analysis of sentiment only after the text has undergone syntactic parsing. In simple words syntactic parsing means that sentences are analyzed to determine their grammatical structure and that each word is assigned its corresponding Part Of Speech (POS) tag.

Consider the following example taken from Cisco’s website (where red and green indicate negative and positive sentiment, respectively):

If Cisco does not achieve the desired level of acceptances, the company will withdraw the offer and evaluate alternative ways to expand our activities in the video communications market.

To glean the lexical sentiment, the sentence is first parsed, i.e. grammatically analyzed. For starters, this allows us to determine the subject of the sentence (“Cisco”) as well as any pronominal phrase referring to the subject (“the company”) – both of which have been marked in bold above. Naturally, this is of critical import to us is in determining what company the sentiment is to be associated with. Secondly, once we obtain a phrasal structure of the sentence we are able to determine how a candidate lexical entry interacts with clause-mate entries. In the example above, “desired” is typically associated with positive sentiment, but this sentiment is reversed due to the negation “does not” appearing earlier in the clause. On the other hand in the subsequent clause the verb entries “evaluate” and “expand” maintain and even substantiate their positive sentiment, as there is nothing in the clause to alter their natural interpretation.

Obviously, not all lexical entries are born equal. Entries may vary both in the extent to which they convey a sentiment and their relative intra-clausal effect. For example “excellent” conveys a stronger sentiment than “good”, whereas “great” and “superb” generally indicate the same level of positivity, but “great” is more susceptible to lexical negation (cf. “great mistake” vs. “superb mistake”). Different entries therefore receive different weights, depending on their relative sentimental strength and susceptibility to polarity-transformations. In order to correctly assign weight to these words, DT uses advanced statistical models which are generated using large manually-analyzed text corpora. In addition further factors such as conditional, speculative and contra-factual clause structures are taken into account before the final contribution of specific entries are calculated.

But this is only the first and most rudimentary element of our synergistic approach.  The second more complex element is that associated with the phrasal level of structure. The phrasal level of analysis assigns a sentiment value to full phrases rather than to single words. Consider the following examples:

Cisco Chief Executive John Chambers has said the firm aims to gain market share in a tech recovery.

Boosted by those moves and  …  following last year’s 40 percent decline

Company Struggles in Attempt to Buy Time

In the examples above the lexical level may signal certain entries are positive or negative, but  only a real phrase-level analysis can ascertain the sentiment. It is here that we first allow semantic and pragmatic factors to interact. It is not enough to understand the meaning of each word in isolation, the meaning of the entire phrase must be deciphered, and to so  correctly, context is needed.

Take a look for instance at the third example above. Usually when companies buy something, it’s either a product or another company. Here, however, it is clear that an idiomatic meaning is intended (buying time… stalling).

DT’s SA takes pragmatics to a whole new level. Not only do we use carefully developed word-classes to allow our engine to utilize outside knowledge in interpreting text, but, working with a team of linguists and economists we have developed specialized sets of phrase level interpretive rules, which allow the engine to identify the context of a sentence or phrase.  All of this combined with the simple pragmatic module which is used to identify key companies by resolving anaphora and common nicknames and descriptors and you end up with a context identifier that allows our engine to assign sentiment to even highly complicated, idiomatic or obscure phrases. Believe it or not, allowing our semantic and pragmatic modules to collaborate, our engine is able to pick up on sarcastic, wishy-washy, and even ironic notes in the text.

This brings us to the third level of our Synergistic Sentiment Analysis, which is based on the interpretation of actual events within the text. Transcending both lexical and phrasal levels of interpretation, we have trained our engine to identify key economic events, and together with a team of experienced financial experts, we’ve created a scale of positive and negative weights for these events. Take a look at the following examples:

shares of Cisco Systems (Nasdaq: CSCO) were recently up 47 percent

Cisco expects revenue to grow 1 to 4 percent

Cisco(R) (NASDAQ: CSCO) today announced a revised recommended voluntary cash offer to acquire TANDBERG (OSLO: TAA)

All the above are real examples of events captured by our SA engine and marked as positive. We currently have our engine trained to extract and evaluate dozens of types of events including purchases, stock offerings, workforce changes, legal events, product launches or recalling, hiring and firing of key figures, new facilities, bankruptcy, etc… etc…

The event-level of our SA assigns the highest weights since it combines and epitomizes all of our techniques. Using syntactic, semantic and pragmatic analyses to determine the contribution of the event to the sentiment. In fact, we believe that by identifying and analyzing the key events in the text we are emulating just what an expert would do when attempting to estimate the sentiment associated with a given text excerpt.

Starting from the lexical level, which allows us to pick up on subtle tones in the text , building up to phrases which indicate attitude, and embedding these all within a semantic-pragmatic event extractor and economic-analyzer, we believe we are truly able to capture the sentiment of text very much like a human would, with incredible reliability and consistency. We may not have yet passed the Turintg Test, but we’re surely on the way to improve the ability of machines to “understand” the natural language that humans use!

Well, for now that’s all we can show, without divulging too much

Read more on Orri Erling’s Weblog…